pymatgen.io.vasp package

This package implements modules for input and output to and from VASP. It imports the key classes form both vasp_input and vasp_output to allow most classes to be simply called as pymatgen.io.vasp.Incar for example, to retain backwards compatibility.

Submodules

pymatgen.io.vasp.help module

Get help with VASP parameters from VASP wiki.

class VaspDoc[source]

Bases: object

A VASP documentation helper.

Init for VaspDoc.

classmethod get_help(tag, fmt='text')[source]

Get help on a VASP tag.

Parameters:

tag (str) – VASP tag, e.g., ISYM.

Returns:

Help text.

classmethod get_incar_tags()[source]

Returns: All incar tags.

print_help(tag)[source]

Print the help for a TAG.

Parameters:

tag (str) – Tag used in VASP.

print_jupyter_help(tag)[source]

Display HTML help in ipython notebook.

Parameters:

tag (str) – Tag used in VASP.

pymatgen.io.vasp.inputs module

Classes for reading/manipulating/writing VASP input files. All major VASP input files.

exception BadIncarWarning[source]

Bases: UserWarning

Warning class for bad INCAR parameters.

exception BadPoscarWarning[source]

Bases: UserWarning

Warning class for bad POSCAR entries.

class Incar(params: dict[str, Any] | None = None)[source]

Bases: dict, MSONable

INCAR object for reading and writing INCAR files. Essentially consists of a dictionary with some helper functions.

Creates an Incar object.

Parameters:

params (dict) – A set of input parameters as a dictionary.

as_dict() dict[source]

MSONable dict.

check_params() None[source]

Check INCAR for invalid tags or values. If a tag doesn’t exist, calculation will still run, however VASP will ignore the tag and set it as default without letting you know.

copy() a shallow copy of D[source]
diff(other: Incar) dict[str, dict[str, Any]][source]

Diff function for Incar. Compares two Incars and indicates which parameters are the same and which are not. Useful for checking whether two runs were done using the same parameters.

Parameters:

other (Incar) – The other Incar object to compare to.

Returns:

of the following format:

{“Same” : parameters_that_are_the_same, “Different”: parameters_that_are_different} Note that the parameters are return as full dictionaries of values. E.g. {“ISIF”:3}

Return type:

dict[str, dict]

classmethod from_dict(dct: dict[str, Any]) Incar[source]
Parameters:

dct (dict) – Serialized Incar

Returns:

Incar

classmethod from_file(filename: PathLike) Incar[source]

Reads an Incar object from a file.

Parameters:

filename (str) – Filename for file

Returns:

Incar object

classmethod from_str(string: str) Incar[source]

Reads an Incar object from a string.

Parameters:

string (str) – Incar string

Returns:

Incar object

get_str(sort_keys: bool = False, pretty: bool = False) str[source]

Returns a string representation of the INCAR. The reason why this method is different from the __str__ method is to provide options for pretty printing.

Parameters:
  • sort_keys (bool) – Set to True to sort the INCAR parameters alphabetically. Defaults to False.

  • pretty (bool) – Set to True for pretty aligned output. Defaults to False.

static proc_val(key: str, val: Any)[source]

Helper method to convert INCAR parameters to proper types like ints, floats, lists, etc.

Parameters:
  • key – INCAR parameter key

  • val – Actual value of INCAR parameter.

write_file(filename: PathLike)[source]

Write Incar to a file.

Parameters:

filename (str) – filename to write to.

class Kpoints(comment: str = 'Default gamma', num_kpts: int = 0, style: KpointsSupportedModes = KpointsSupportedModes.Gamma, kpts: Sequence[float | Sequence] = ((1, 1, 1),), kpts_shift: Vector3D = (0, 0, 0), kpts_weights=None, coord_type=None, labels=None, tet_number: int = 0, tet_weight: float = 0, tet_connections=None)[source]

Bases: MSONable

KPOINT reader/writer.

Highly flexible constructor for Kpoints object. The flexibility comes at the cost of usability and in general, it is recommended that you use the default constructor only if you know exactly what you are doing and requires the flexibility. For most usage cases, the three automatic schemes can be constructed far more easily using the convenience static constructors (automatic, gamma_automatic, monkhorst_automatic) and it is recommended that you use those.

Parameters:
  • comment (str) – String comment for Kpoints. Defaults to “Default gamma”.

  • num_kpts – Following VASP method of defining the KPOINTS file, this parameter is the number of kpoints specified. If set to 0 (or negative), VASP automatically generates the KPOINTS.

  • style – Style for generating KPOINTS. Use one of the Kpoints.supported_modes enum types.

  • kpts (2D array) – 2D array of kpoints. Even when only a single specification is required, e.g. in the automatic scheme, the kpts should still be specified as a 2D array. e.g., [[20]] or [[2,2,2]].

  • kpts_shift (3x1 array) – Shift for Kpoints.

  • kpts_weights – Optional weights for kpoints. Weights should be integers. For explicit kpoints.

  • coord_type – In line-mode, this variable specifies whether the Kpoints were given in Cartesian or Reciprocal coordinates.

  • labels – In line-mode, this should provide a list of labels for each kpt. It is optional in explicit kpoint mode as comments for k-points.

  • tet_number – For explicit kpoints, specifies the number of tetrahedrons for the tetrahedron method.

  • tet_weight – For explicit kpoints, specifies the weight for each tetrahedron for the tetrahedron method.

  • tet_connections – For explicit kpoints, specifies the connections of the tetrahedrons for the tetrahedron method. Format is a list of tuples, [ (sym_weight, [tet_vertices]), …]

The default behavior of the constructor is for a Gamma centered, 1x1x1 KPOINTS with no shift.

as_dict() dict[source]

MSONable dict.

static automatic(subdivisions)[source]

Convenient static constructor for a fully automatic Kpoint grid, with gamma centered Monkhorst-Pack grids and the number of subdivisions along each reciprocal lattice vector determined by the scheme in the VASP manual.

Parameters:

subdivisions – Parameter determining number of subdivisions along each reciprocal lattice vector.

Returns:

Kpoints object

static automatic_density(structure: Structure, kppa: float, force_gamma: bool = False)[source]

Returns an automatic Kpoint object based on a structure and a kpoint density. Uses Gamma centered meshes for hexagonal cells and face-centered cells, Monkhorst-Pack grids otherwise.

Algorithm:

Uses a simple approach scaling the number of divisions along each reciprocal lattice vector proportional to its length.

Parameters:
  • structure (Structure) – Input structure

  • kppa (float) – Grid density

  • force_gamma (bool) – Force a gamma centered mesh (default is to use gamma only for hexagonal cells or odd meshes)

Returns:

Kpoints

static automatic_density_by_lengths(structure: Structure, length_densities: Sequence[float], force_gamma: bool = False)[source]

Returns an automatic Kpoint object based on a structure and a k-point density normalized by lattice constants.

Algorithm:

For a given dimension, the # of k-points is chosen as length_density = # of kpoints * lattice constant, e.g. [50.0, 50.0, 1.0] would have k-points of 50/a x 50/b x 1/c.

Parameters:
  • structure (Structure) – Input structure

  • length_densities (list[floats]) – Defines the density of k-points in each

  • dimension

  • [50.0 (e.g.) –

  • 50.0

  • 1.0].

  • force_gamma (bool) – Force a gamma centered mesh

Returns:

Kpoints

static automatic_density_by_vol(structure: Structure, kppvol: int, force_gamma: bool = False) Kpoints[source]

Returns an automatic Kpoint object based on a structure and a kpoint density per inverse Angstrom^3 of reciprocal cell.

Algorithm:

Same as automatic_density()

Parameters:
  • structure (Structure) – Input structure

  • kppvol (int) – Grid density per Angstrom^(-3) of reciprocal cell

  • force_gamma (bool) – Force a gamma centered mesh

Returns:

Kpoints

static automatic_gamma_density(structure: Structure, kppa: float)[source]

Returns an automatic Kpoint object based on a structure and a kpoint density. Uses Gamma centered meshes always. For GW.

Algorithm:

Uses a simple approach scaling the number of divisions along each reciprocal lattice vector proportional to its length.

Parameters:
  • structure – Input structure

  • kppa – Grid density

static automatic_linemode(divisions, ibz)[source]

Convenient static constructor for a KPOINTS in mode line_mode. gamma centered Monkhorst-Pack grids and the number of subdivisions along each reciprocal lattice vector determined by the scheme in the VASP manual.

Parameters:
  • divisions – Parameter determining the number of k-points along each high symmetry line.

  • ibz – HighSymmKpath object (pymatgen.symmetry.bandstructure)

Returns:

Kpoints object

copy()[source]
classmethod from_dict(dct: dict) Self[source]
Parameters:

dct (dict) – Dict representation.

Returns:

Kpoints

classmethod from_file(filename)[source]

Reads a Kpoints object from a KPOINTS file.

Parameters:

filename (str) – filename to read from.

Returns:

Kpoints object

classmethod from_str(string)[source]

Reads a Kpoints object from a KPOINTS string.

Parameters:

string (str) – KPOINTS string.

Returns:

Kpoints object

static gamma_automatic(kpts: tuple[int, int, int] = (1, 1, 1), shift: Vector3D = (0, 0, 0))[source]

Convenient static constructor for an automatic Gamma centered Kpoint grid.

Parameters:
  • kpts – Subdivisions N_1, N_2 and N_3 along reciprocal lattice vectors. Defaults to (1,1,1)

  • shift – Shift to be applied to the kpoints. Defaults to (0,0,0).

Returns:

Kpoints object

static monkhorst_automatic(kpts: tuple[int, int, int] = (2, 2, 2), shift: Vector3D = (0, 0, 0))[source]

Convenient static constructor for an automatic Monkhorst pack Kpoint grid.

Parameters:
  • kpts – Subdivisions N_1, N_2, N_3 along reciprocal lattice vectors. Defaults to (2,2,2)

  • shift – Shift to be applied to the kpoints. Defaults to (0,0,0).

Returns:

Kpoints object

property style: KpointsSupportedModes[source]

Style for kpoint generation. One of Kpoints_supported_modes enum.

supported_modes[source]

alias of KpointsSupportedModes

write_file(filename)[source]

Write Kpoints to a file.

Parameters:

filename (str) – Filename to write to.

class KpointsSupportedModes(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)[source]

Bases: Enum

Enum type of all supported modes for Kpoint generation.

Automatic = 0[source]
Cartesian = 4[source]
Gamma = 1[source]
Line_mode = 3[source]
Monkhorst = 2[source]
Reciprocal = 5[source]
classmethod from_str(mode: str) KpointsSupportedModes[source]
Parameters:

mode – String

Returns:

Kpoints_supported_modes

class Orbital(n, l, j, E, occ)[source]

Bases: tuple

Create new instance of Orbital(n, l, j, E, occ)

E[source]

Alias for field number 3

j[source]

Alias for field number 2

l[source]

Alias for field number 1

n[source]

Alias for field number 0

occ[source]

Alias for field number 4

class OrbitalDescription(l, E, Type, Rcut, Type2, Rcut2)[source]

Bases: tuple

Create new instance of OrbitalDescription(l, E, Type, Rcut, Type2, Rcut2)

E[source]

Alias for field number 1

Rcut[source]

Alias for field number 3

Rcut2[source]

Alias for field number 5

Type[source]

Alias for field number 2

Type2[source]

Alias for field number 4

l[source]

Alias for field number 0

class Poscar(structure: Structure, comment: str | None = None, selective_dynamics: ArrayLike | None = None, true_names: bool = True, velocities: ArrayLike | None = None, predictor_corrector: ArrayLike | None = None, predictor_corrector_preamble: str | None = None, lattice_velocities: ArrayLike | None = None, sort_structure: bool = False)[source]

Bases: MSONable

Object for representing the data in a POSCAR or CONTCAR file.

structure[source]

Associated Structure.

comment[source]

Optional comment string.

true_names[source]

Boolean indication whether Poscar contains actual real names parsed from either a POTCAR or the POSCAR itself.

selective_dynamics[source]

Selective dynamics attribute for each site if available. A Nx3 array of booleans.

velocities[source]

Velocities for each site (typically read in from a CONTCAR). A Nx3 array of floats.

predictor_corrector[source]

Predictor corrector coordinates and derivatives for each site; i.e. a list of three 1x3 arrays for each site (typically read in from an MD CONTCAR).

predictor_corrector_preamble[source]

Predictor corrector preamble contains the predictor-corrector key, POTIM, and thermostat parameters that precede the site-specific predictor corrector data in MD CONTCAR.

lattice_velocities[source]

Lattice velocities and current lattice (typically read in from an MD CONTCAR). A 6x3 array of floats.

temperature[source]

Temperature of velocity Maxwell-Boltzmann initialization. Initialized to -1 (MB hasn’t been performed).

Parameters:
  • structure (Structure) – Structure object.

  • comment (str | None, optional) – Optional comment line for POSCAR. Defaults to unit cell formula of structure. Defaults to None.

  • selective_dynamics (ArrayLike | None, optional) – Bool values for selective dynamics, where N is the number of sites. Defaults to None.

  • true_names (bool, optional) – Set to False if the names in the POSCAR are not well-defined and ambiguous. This situation arises commonly in VASP < 5 where the POSCAR sometimes does not contain element symbols. Defaults to True.

  • velocities (ArrayLike | None, optional) – Velocities for the POSCAR. Typically parsed in MD runs or can be used to initialize velocities. Defaults to None.

  • predictor_corrector (ArrayLike | None, optional) – Predictor corrector for the POSCAR. Typically parsed in MD runs. Defaults to None.

  • predictor_corrector_preamble (str | None, optional) – Preamble to the predictor corrector. Defaults to None.

  • lattice_velocities (ArrayLike | None, optional) – Lattice velocities and current lattice for the POSCAR. Available in MD runs with variable cell. Defaults to None.

  • sort_structure (bool, optional) – Whether to sort the structure. Useful if species are not grouped properly together. Defaults to False.

as_dict() dict[source]

MSONable dict.

classmethod from_dict(dct: dict) Poscar[source]
Parameters:

dct (dict) – Dict representation.

Returns:

Poscar

classmethod from_file(filename, check_for_potcar=True, read_velocities=True, **kwargs) Poscar[source]

Reads a Poscar from a file.

The code will try its best to determine the elements in the POSCAR in the following order:

1. If check_for_potcar is True, the code will try to check if a POTCAR is in the same directory as the POSCAR and use elements from that by default. (This is the VASP default sequence of priority). 2. If the input file is VASP5-like and contains element symbols in the 6th line, the code will use that if check_for_potcar is False or there is no POTCAR found. 3. Failing (2), the code will check if a symbol is provided at the end of each coordinate.

If all else fails, the code will just assign the first n elements in increasing atomic number, where n is the number of species, to the Poscar, where a warning would be issued. For example, H, He, Li, …. This will ensure at least a unique element is assigned to each site and any analysis that does not require specific elemental properties should work.

Parameters:
  • filename (str) – File name containing Poscar data.

  • check_for_potcar (bool) – Whether to check if a POTCAR is present in the same directory as the POSCAR. Defaults to True.

  • read_velocities (bool) – Whether to read or not velocities if they are present in the POSCAR. Default is True.

Returns:

Poscar object.

classmethod from_str(data, default_names=None, read_velocities=True) Poscar[source]

Reads a Poscar from a string.

The code will try its best to determine the elements in the POSCAR in the following order:

1. If default_names are supplied and valid, it will use those. Usually, default names comes from an external source, such as a POTCAR in the same directory.

2. If there are no valid default names but the input file is VASP5-like and contains element symbols in the 6th line, the code will use that.

3. Failing (2), the code will check if a symbol is provided at the end of each coordinate.

If all else fails, the code will just assign the first n elements in increasing atomic number, where n is the number of species, to the Poscar. For example, H, He, Li, …. This will ensure at least a unique element is assigned to each site and any analysis that does not require specific elemental properties should work fine.

Parameters:
  • data (str) – String containing Poscar data.

  • default_names ([str]) – Default symbols for the POSCAR file, usually coming from a POTCAR in the same directory.

  • read_velocities (bool) – Whether to read or not velocities if they are present in the POSCAR. Default is True.

Returns:

Poscar object.

get_str(direct: bool = True, vasp4_compatible: bool = False, significant_figures: int = 16) str[source]

Returns a string to be written as a POSCAR file. By default, site symbols are written, which means compatibility is for vasp >= 5.

Parameters:
  • direct (bool) – Whether coordinates are output in direct or Cartesian. Defaults to True.

  • vasp4_compatible (bool) – Set to True to omit site symbols on 6th line to maintain backward vasp 4.x compatibility. Defaults to False.

  • significant_figures (int) – No. of significant figures to output all quantities. Defaults to 16. Note that positions are output in fixed point, while velocities are output in scientific format.

Returns:

String representation of POSCAR.

property lattice_velocities[source]

Lattice velocities in Poscar (including the current lattice vectors).

property natoms: list[int][source]

Sequence of number of sites of each type associated with the Poscar. Similar to 7th line in vasp 5+ POSCAR or the 6th line in vasp 4 POSCAR.

property predictor_corrector[source]

Predictor corrector in Poscar.

property predictor_corrector_preamble[source]

Predictor corrector preamble in Poscar.

property selective_dynamics[source]

Selective dynamics in Poscar.

set_temperature(temperature: float)[source]

Initializes the velocities based on Maxwell-Boltzmann distribution. Removes linear, but not angular drift (same as VASP).

Scales the energies to the exact temperature (microcanonical ensemble) Velocities are given in A/fs. This is the vasp default when direct/cartesian is not specified (even when positions are given in direct coordinates)

Overwrites imported velocities, if any.

Parameters:

temperature (float) – Temperature in Kelvin.

property site_symbols: list[str][source]

Sequence of symbols associated with the Poscar. Similar to 6th line in VASP 5+ POSCAR.

property velocities[source]

Velocities in Poscar.

write_file(filename: PathLike, **kwargs)[source]

Writes POSCAR to a file. The supported kwargs are the same as those for the Poscar.get_str method and are passed through directly.

class Potcar(symbols: Sequence[str] | None = None, functional: str | None = None, sym_potcar_map: dict[str, str] | None = None)[source]

Bases: list, MSONable

Object for reading and writing POTCAR files for calculations. Consists of a list of PotcarSingle.

Parameters:
  • symbols (list[str]) – Element symbols for POTCAR. This should correspond to the symbols used by VASP. E.g., “Mg”, “Fe_pv”, etc.

  • functional (str) – Functional used. To know what functional options there are, use Potcar.FUNCTIONAL_CHOICES. Note that VASP has different versions of the same functional. By default, the old PBE functional is used. If you want the newer ones, use PBE_52 or PBE_54. Note that if you intend to compare your results with the Materials Project, you should use the default setting. You can also override the default by setting PMG_DEFAULT_FUNCTIONAL in your .pmgrc.yaml.

  • sym_potcar_map (dict) – Allows a user to specify a specific element symbol to raw POTCAR mapping.

FUNCTIONAL_CHOICES = ('PBE', 'PBE_52', 'PBE_52_W_HASH', 'PBE_54', 'PBE_54_W_HASH', 'PBE_64', 'LDA', 'LDA_52', 'LDA_52_W_HASH', 'LDA_54', 'LDA_54_W_HASH', 'LDA_64', 'PW91', 'LDA_US', 'PW91_US', 'Perdew_Zunger81')[source]
as_dict()[source]

MSONable dict representation

classmethod from_dict(dct) Self[source]
Parameters:

dct (dict) – Dict representation.

Returns:

Potcar

classmethod from_file(filename: str) Self[source]

Reads Potcar from file.

Parameters:

filename – Filename

Returns:

Potcar

set_symbols(symbols: Sequence[str], functional: str | None = None, sym_potcar_map: dict[str, str] | None = None)[source]

Initialize the POTCAR from a set of symbols. Currently, the POTCARs can be fetched from a location specified in .pmgrc.yaml. Use pmg config to add this setting.

Parameters:
  • symbols (list[str]) – A list of element symbols

  • functional (str) – The functional to use. If None, the setting PMG_DEFAULT_FUNCTIONAL in .pmgrc.yaml is used, or if this is not set, it will default to PBE.

  • sym_potcar_map (dict) – A map of symbol:raw POTCAR string. If sym_potcar_map is specified, POTCARs will be generated from the given map data rather than the config file location.

property spec[source]

Get the atomic symbols and hash of all the atoms in the POTCAR file.

property symbols[source]

Get the atomic symbols of all the atoms in the POTCAR file.

write_file(filename: str) None[source]

Write Potcar to a file.

Parameters:

filename (str) – filename to write to.

class PotcarSingle(data: str, symbol: str | None = None)[source]

Bases: object

Object for a single POTCAR. The builder assumes the POTCAR contains the complete untouched data in “data” as a string and a dict of keywords.

data[source]

POTCAR data as a string.

Type:

str

keywords[source]

Keywords parsed from the POTCAR as a dict. All keywords are also accessible as attributes in themselves. E.g., potcar.enmax, potcar.encut, etc.

Type:

dict

md5 hashes of the entire POTCAR file and the actual data are validated against a database of known good hashes. Appropriate warnings or errors are raised if a POTCAR hash fails validation.

Parameters:
  • data (str) – Complete and single POTCAR file as a string.

  • symbol (str) – POTCAR symbol corresponding to the filename suffix e.g. “Tm_3” for POTCAR.TM_3”. If not given, pymatgen will attempt to extract the symbol from the file itself. This is not always reliable!

property atomic_no: int[source]

Attempt to return the atomic number based on the VRHFIN keyword.

copy() PotcarSingle[source]

Returns a copy of the PotcarSingle.

Returns:

PotcarSingle

property electron_configuration: list[tuple[int, str, int]] | None[source]

Electronic configuration of the PotcarSingle.

property element: str[source]

Attempt to return the atomic symbol based on the VRHFIN keyword.

classmethod from_file(filename: str) Self[source]

Reads PotcarSingle from file.

Parameters:

filename – Filename.

Returns:

PotcarSingle

classmethod from_symbol_and_functional(symbol: str, functional: str | None = None)[source]

Makes a PotcarSingle from a symbol and functional.

Parameters:
  • symbol (str) – Symbol, e.g., Li_sv

  • functional (str) – Functional, e.g., PBE

Returns:

PotcarSingle

property functional: str | None[source]

Functional associated with PotcarSingle.

property functional_class[source]

Functional class associated with PotcarSingle.

functional_dir = {'LDA': 'POT_LDA_PAW', 'LDA_52': 'POT_LDA_PAW_52', 'LDA_52_W_HASH': 'POTPAW_LDA_52', 'LDA_54': 'POT_LDA_PAW_54', 'LDA_54_W_HASH': 'POTPAW_LDA_54', 'LDA_64': 'POT_LDA_PAW_64', 'LDA_US': 'POT_LDA_US', 'PBE': 'POT_GGA_PAW_PBE', 'PBE_52': 'POT_GGA_PAW_PBE_52', 'PBE_52_W_HASH': 'POTPAW_PBE_52', 'PBE_54': 'POT_GGA_PAW_PBE_54', 'PBE_54_W_HASH': 'POTPAW_PBE_54', 'PBE_64': 'POT_PAW_PBE_64', 'PW91': 'POT_GGA_PAW_PW91', 'PW91_US': 'POT_GGA_US_PW91', 'Perdew_Zunger81': 'POT_LDA_PAW'}[source]
functional_tags = {'91': {'class': 'GGA', 'name': 'PW91'}, 'am': {'class': 'GGA', 'name': 'AM05'}, 'ca': {'class': 'LDA', 'name': 'Perdew-Zunger81'}, 'hl': {'class': 'LDA', 'name': 'Hedin-Lundquist'}, 'lm': {'class': 'GGA', 'name': 'Langreth-Mehl-Hu'}, 'pb': {'class': 'GGA', 'name': 'Perdew-Becke'}, 'pe': {'class': 'GGA', 'name': 'PBE'}, 'ps': {'class': 'GGA', 'name': 'PBEsol'}, 'pw': {'class': 'GGA', 'name': 'PW86'}, 'rp': {'class': 'GGA', 'name': 'revPBE'}, 'wi': {'class': 'LDA', 'name': 'Wigner Interpolation'}}[source]
property hash_sha256_from_file: str | None[source]

SHA256 hash of the POTCAR file as read from the file. None if no SHA256 hash is found.

identify_potcar(mode: Literal['data', 'file'] = 'data', data_tol: float = 1e-06) tuple[list[str], list[str]][source]

Identify the symbol and compatible functionals associated with this PotcarSingle.

This method checks the summary statistics of either the POTCAR metadadata (PotcarSingle._summary_stats[key][“header”] for key in (“keywords”, “stats”) ) or the entire POTCAR file (PotcarSingle._summary_stats) against a database of hashes for POTCARs distributed with VASP 5.4.4.

Parameters:
  • mode ('data' | 'file') – ‘data’ mode checks the POTCAR header keywords and stats only while ‘file’ mode checks the entire summary stats.

  • data_tol (float) – Tolerance for comparing the summary statistics of the POTCAR with the reference statistics.

Returns:

List of symbols associated with the PotcarSingle potcar_functionals (list): List of potcar functionals associated with

the PotcarSingle

Return type:

symbol (list)

identify_potcar_hash_based(mode: Literal['data', 'file'] = 'data')[source]

Identify the symbol and compatible functionals associated with this PotcarSingle.

This method checks the md5 hash of either the POTCAR metadadata (PotcarSingle.md5_header_hash) or the entire POTCAR file (PotcarSingle.md5_computed_file_hash) against a database of hashes for POTCARs distributed with VASP 5.4.4.

Parameters:

mode ('data' | 'file') – ‘data’ mode checks the hash of the POTCAR metadata in self.keywords, while ‘file’ mode checks the hash of the entire POTCAR file.

Returns:

List of symbols associated with the PotcarSingle potcar_functionals (list): List of potcar functionals associated with

the PotcarSingle

Return type:

symbol (list)

property is_valid: bool[source]

Check that POTCAR matches reference metadata. Parsed metadata is stored in self._summary_stats as a human-readable dict,

self._summary_stats = {
“keywords”: {

“header”: list[str], “data”: list[str],

}, “stats”: {

“header”: dict[float], “data”: dict[float],

},

}

Rationale: Each POTCAR is structured as

Header (self.keywords) Data (actual pseudopotential values in data blocks)

For the Data block of POTCAR, there are unformatted data blocks of unknown length and contents/data type, e.g., you might see

<float> <bool> <Data Keyword> <int> <int> <float> <float> … <float> <Data Keyword> <float> … <float>

but this is impossible to process algorithmically without a full POTCAR schema. Note also that POTCARs can contain different data keywords

All keywords found in the header, essentially self.keywords, and the data block (<Data Keyword> above) are stored in self._summary_stats[“keywords”]

To avoid issues of copyright, statistics (mean, mean of abs vals, variance, max, min) for the numeric values in the header and data sections of POTCAR are stored in self._summary_stats[“stats”]

tol is then used to match statistical values within a tolerance

property md5_computed_file_hash: str[source]

md5 hash of the entire PotcarSingle.

property md5_header_hash: str[source]

Computes a md5 hash of the metadata defining the PotcarSingle.

property nelectrons: float[source]

Number of electrons

parse_functions = {'COPYR': <method 'strip' of 'str' objects>, 'DEXC': <function _parse_float>, 'EATOM': <function _parse_float>, 'EAUG': <function _parse_float>, 'EMMIN': <function _parse_float>, 'ENMAX': <function _parse_float>, 'ENMIN': <function _parse_float>, 'GGA': <function _parse_list>, 'ICORE': <function _parse_int>, 'IUNSCR': <function _parse_int>, 'LCOR': <function _parse_bool>, 'LEXCH': <method 'strip' of 'str' objects>, 'LPAW': <function _parse_bool>, 'LULTRA': <function _parse_bool>, 'LUNSCR': <function _parse_bool>, 'NDATA': <function _parse_int>, 'POMASS': <function _parse_float>, 'QCUT': <function _parse_float>, 'QGAM': <function _parse_float>, 'RAUG': <function _parse_float>, 'RCLOC': <function _parse_float>, 'RCORE': <function _parse_float>, 'RDEP': <function _parse_float>, 'RDEPT': <function _parse_float>, 'RMAX': <function _parse_float>, 'RPACOR': <function _parse_float>, 'RRKJ': <function _parse_list>, 'RWIGS': <function _parse_float>, 'SHA256': <method 'strip' of 'str' objects>, 'STEP': <function _parse_list>, 'TITEL': <method 'strip' of 'str' objects>, 'VRHFIN': <method 'strip' of 'str' objects>, 'ZVAL': <function _parse_float>}[source]
property potential_type: Literal['NC', 'PAW', 'US'][source]

Type of PSP. E.g., US, PAW, etc.

property sha256_computed_file_hash: str[source]

Computes a SHA256 hash of the PotcarSingle EXCLUDING lines starting with ‘SHA256’ and ‘COPYR’.

property symbol: str[source]

The POTCAR symbol, e.g. W_pv

verify_potcar() tuple[bool, bool][source]

Attempts to verify the integrity of the POTCAR data.

This method checks the whole file (removing only the SHA256 metadata) against the SHA256 hash in the header if this is found. If no SHA256 hash is found in the file, the file hash (md5 hash of the whole file) is checked against all POTCAR file hashes known to pymatgen.

Returns:

has_sha256 and passed_hash_check are returned.

Return type:

tuple[bool, bool]

write_file(filename: str) None[source]

Write PotcarSingle to a file.

Parameters:

filename (str) – Filename to write to.

exception UnknownPotcarWarning[source]

Bases: UserWarning

Warning raised when POTCAR hashes do not pass validation.

class VaspInput(incar: dict | Incar, kpoints: Kpoints | None, poscar: Poscar, potcar: Potcar | None, optional_files: dict[PathLike, object] | None = None, **kwargs)[source]

Bases: dict, MSONable

Class to contain a set of vasp input objects corresponding to a run.

Initializes a VaspInput object with the given input files.

Parameters:
  • incar (Incar) – The Incar object.

  • kpoints (Kpoints) – The Kpoints object.

  • poscar (Poscar) – The Poscar object.

  • potcar (Potcar) – The Potcar object.

  • optional_files (dict) – Other input files supplied as a dict of {filename: object}. The object should follow standard pymatgen conventions in implementing a as_dict() and from_dict method.

  • **kwargs – Additional keyword arguments to be stored in the VaspInput object.

as_dict()[source]

MSONable dict.

copy(deep: bool = True)[source]

Deep copy of VaspInput.

classmethod from_dict(dct: dict) Self[source]
Parameters:

dct (dict) – Dict representation.

Returns:

VaspInput

classmethod from_directory(input_dir, optional_files=None)[source]

Read in a set of VASP input from a directory. Note that only the standard INCAR, POSCAR, POTCAR and KPOINTS files are read unless optional_filenames is specified.

Parameters:
  • input_dir (str) – Directory to read VASP input from.

  • optional_files (dict) – Optional files to read in as well as a dict of {filename: Object type}. Object type must have a static method from_file.

run_vasp(run_dir: PathLike = '.', vasp_cmd: list | None = None, output_file: PathLike = 'vasp.out', err_file: PathLike = 'vasp.err') None[source]

Write input files and run VASP.

Parameters:
  • run_dir – Where to write input files and do the run.

  • vasp_cmd – Args to be supplied to run VASP. Otherwise, the PMG_VASP_EXE in .pmgrc.yaml is used.

  • output_file – File to write output.

  • err_file – File to write err.

write_input(output_dir='.', make_dir_if_not_present=True)[source]

Write VASP input to a directory.

Parameters:
  • output_dir (str) – Directory to write to. Defaults to current directory (“.”).

  • make_dir_if_not_present (bool) – Create the directory if not present. Defaults to True.

pymatgen.io.vasp.optics module

Classes for parsing and manipulating VASP optical properties calculations.

class DielectricFunctionCalculator(cder_real: NDArray, cder_imag: NDArray, eigs: NDArray, kweights: NDArray, nedos: int, deltae: float, ismear: int, sigma: float, efermi: float, cshift: float, ispin: int, volume: float)[source]

Bases: MSONable

Class for postprocessing VASP optical properties calculations.

This objects helps load the different parameters from the vasprun.xml file but allows users to override them as needed.

The standard vasprun.xml from an LOPTICS=.True. calculation already contains the complex frequency dependent dielectric functions. However you have no way to decompose the different contributions. Since the WAVEDER file is also written during an optical calculation, you can reconstruct the dielectric functions purely in Python and have full control over contribution from different bands and k-points.

VASP’s linear optics follow these steps:
  • Calculate the imaginary part

  • Perform symmetry operations (this is not implemented here)

  • Calculate the real part

Currently, this Calculator only works for ISYM=0 calculations since we cannot guarantee that our externally defined symmetry operations are the same as VASP’s. This can be fixed by printing the symmetry operators into the vasprun.xml file. If this happens in future versions of VASP, we can dramatically speed up the calculations here by considering only the irreducible kpoints.

property cder[source]

Complex CDER from WAVEDER.

cder_imag: NDArray[source]
cder_real: NDArray[source]
cshift: float[source]
deltae: float[source]
efermi: float[source]
eigs: NDArray[source]
classmethod from_directory(directory: Path | str)[source]

Construct a DielectricFunction from a directory containing vasprun.xml and WAVEDER files.

classmethod from_vasp_objects(vrun: Vasprun, waveder: Waveder)[source]

Construct a DielectricFunction from Vasprun, Kpoint, and Waveder objects.

Parameters:
  • vrun – Vasprun object

  • kpoint – Kpoint object

  • waveder – Waveder object

get_epsilon(idir: int, jdir: int, efermi: float | None = None, nedos: int | None = None, deltae: float | None = None, ismear: int | None = None, sigma: float | None = None, cshift: float | None = None, mask: NDArray | None = None) tuple[NDArray, NDArray][source]

Compute the frequency dependent dielectric function.

Parameters:
  • idir – First direction of the dielectric tensor

  • jdir – Second direction of the dielectric tensor

  • efermi – Fermi energy

  • nedos – Number of points in the DOS

  • deltae – Energy step in the DOS

  • ismear – Smearing method (only has 0:gaussian, >0:Methfessel-Paxton)

  • sigma – Smearing width

  • cshift – Complex shift used for Kramer-Kronig transformation

  • mask – Mask for the bands/kpoint/spin index to include in the calculation

ismear: int[source]
ispin: int[source]
kweights: NDArray[source]
nedos: int[source]
plot_weighted_transition_data(idir: int, jdir: int, mask: NDArray | None = None, min_val: float = 0.0)[source]

Data for plotting the weight matrix elements as a scatter plot.

Since the computation of the final spectrum (especially the smearing part) is still fairly expensive. This function can be used to check the values of some portion of the spectrum (defined by the mask). In a sense, we are lookin at the imaginary part of the dielectric function before the smearing is applied.

Parameters:
  • idir – First direction of the dielectric tensor.

  • jdir – Second direction of the dielectric tensor.

  • mask – Mask to apply to the CDER for the bands/kpoint/spin index to include in the calculation

  • min_val – Minimum value below this value the matrix element will not be shown.

sigma: float[source]
volume: float[source]
delta_func(x, ismear)[source]

Replication of VASP’s delta function.

delta_methfessel_paxton(x, n)[source]

D_n (x) = exp -x^2 * sum_i=0^n A_i H_2i(x) where H is a Hermite polynomial and A_i = (-1)^i / ( i! 4^i sqrt(pi) ).

epsilon_imag(cder: NDArray, eigs: NDArray, kweights: ArrayLike, efermi: float, nedos: int, deltae: float, ismear: int, sigma: float, idir: int, jdir: int, mask: NDArray | None = None)[source]

Replicate the EPSILON_IMAG function of VASP.

Parameters:
  • cder – The data written to the WAVEDER (nbands, nbands, nkpoints, nspin, diri, dirj)

  • eigs – The eigenvalues (nbands, nkpoints, nspin)

  • kweights – The kpoint weights (nkpoints)

  • efermi – The fermi energy

  • nedos – The sampling of the energy values

  • deltae – The energy grid spacing

  • ismear – The smearing parameter used by the step_func.

  • sigma – The width of the smearing

  • idir – The first direction of the dielectric tensor

  • jdir – The second direction of the dielectric tensor

  • mask – Mask for the bands/kpoint/spin index to include in the calculation

Returns:

Array of size nedos with the imaginary part of the dielectric function.

Return type:

np.array

get_delta(x0: float, sigma: float, nx: int, dx: float, ismear: int = 3)[source]

Get the smeared delta function to be added to form the spectrum.

This replaces the SLOT function from VASP. Uses finite differences instead of evaluating the delta function since the step function is more likely to have analytic form.

Parameters:
  • x0 – The center of the dielectric function.

  • sigma – The width of the smearing

  • nx – The number of grid points in the output grid.

  • dx – The gridspacing of the output grid.

  • ismear – The smearing parameter used by the step_func.

Returns:

Array of size nx with delta function on the desired outputgrid.

Return type:

np.array

get_step(x0, sigma, nx, dx, ismear)[source]

Get the smeared step function to be added to form the spectrum.

This replaces the SLOT function from VASP.

Parameters:
  • x0 – The center of the dielectric function.

  • sigma – The width of the smearing

  • nx – The number of grid points in the output grid.

  • dx – The gridspacing of the output grid.

  • ismear – The smearing parameter used by the step_func.

Returns:

Array of size nx with step function on the desired outputgrid.

Return type:

np.array

kramers_kronig(eps: np.ndarray, nedos: int, deltae: float, cshift: float = 0.1) NDArray[source]

Perform the Kramers-Kronig transformation.

Perform the Kramers-Kronig transformation exactly as VASP does it. The input eps should be complex and the imaginary part of the dielectric function should be stored as the real part of the complex input array. The output should be the complex dielectric function.

Parameters:
  • eps – The dielectric function with the imaginary part stored as the real part and nothing in the imaginary part.

  • nedos – The sampling of the energy values

  • deltae – The energy grid spacing

  • cshift – The shift of the imaginary part of the dielectric function.

Returns:

Array of size nedos with the complex dielectric function.

Return type:

np.array

step_func(x, ismear)[source]

Replication of VASP’s step function.

step_methfessel_paxton(x, n)[source]

S_n (x) = (1 + erf x)/2 - exp -x^2 * sum_i=1^n A_i H_{2i-1}(x) where H is a Hermite polynomial and A_i = (-1)^i / ( i! 4^i sqrt(pi) ).

pymatgen.io.vasp.outputs module

Classes for reading/manipulating/writing VASP output files.

class BSVasprun(filename: str, parse_projected_eigen: bool | str = False, parse_potcar_file: bool | str = False, occu_tol: float = 1e-08, separate_spins: bool = False)[source]

Bases: Vasprun

A highly optimized version of Vasprun that parses only eigenvalues for bandstructures. All other properties like structures, parameters, etc. are ignored.

Parameters:
  • filename – Filename to parse

  • parse_projected_eigen – Whether to parse the projected eigenvalues. Defaults to False. Set to True to obtain projected eigenvalues. Note that this can take an extreme amount of time and memory. So use this wisely.

  • parse_potcar_file – Whether to parse the potcar file to read the potcar hashes for the potcar_spec attribute. Defaults to True, where no hashes will be determined and the potcar_spec dictionaries will read {“symbol”: ElSymbol, “hash”: None}. By Default, looks in the same directory as the vasprun.xml, with same extensions as Vasprun.xml. If a string is provided, looks at that filepath.

  • occu_tol – Sets the minimum tol for the determination of the vbm and cbm. Usually the default of 1e-8 works well enough, but there may be pathological cases.

  • separate_spins (bool) – Whether the band gap, CBM, and VBM should be reported for each individual spin channel. Defaults to False, which computes the eigenvalue band properties independent of the spin orientation. If True, the calculation must be spin-polarized.

as_dict()[source]

JSON-serializable dict representation.

class Chgcar(poscar, data, data_aug=None)[source]

Bases: VolumetricData

Simple object for reading a CHGCAR file.

Parameters:
  • poscar (Poscar | Structure) – Object containing structure.

  • data – Actual data.

  • data_aug – Augmentation charge data.

classmethod from_file(filename: str) Self[source]

Read a CHGCAR file.

Parameters:

filename (str) – Path to CHGCAR file.

Returns:

Chgcar

property net_magnetization[source]

Net magnetization from Chgcar

class Dynmat(filename)[source]

Bases: object

Object for reading a DYNMAT file.

data[source]

A nested dict containing the DYNMAT data of the form: [atom <int>][disp <int>][‘dispvec’] =

displacement vector (part of first line in dynmat block, e.g. “0.01 0 0”)

[atom <int>][disp <int>][‘dynmat’] =

<list> list of dynmat lines for this atom and this displacement

Type:

dict

Authors: Patrick Huck

Parameters:

filename – Name of file containing DYNMAT.

get_phonon_frequencies()[source]

Calculate phonon frequencies.

property masses[source]

Returns the list of atomic masses.

property natoms[source]

Returns the number of atoms.

property ndisps[source]

Returns the number of displacements.

property nspecs[source]

Returns the number of species.

class Eigenval(filename, occu_tol=1e-08, separate_spins=False)[source]

Bases: object

Object for reading EIGENVAL file.

filename[source]

String containing input filename.

Type:

str

occu_tol[source]

Tolerance for determining occupation in band properties.

Type:

float

ispin[source]

Spin polarization tag.

Type:

int

nelect[source]

Number of electrons.

Type:

int

nkpt[source]

Number of kpoints.

Type:

int

nbands[source]

Number of bands.

Type:

int

kpoints[source]

List of kpoints.

Type:

list

kpoints_weights[source]

Weights of each kpoint in the BZ, should sum to 1.

Type:

list

eigenvalues[source]

Eigenvalues as a dict of {(spin): np.ndarray(shape=(nkpt, nbands, 2))}. This representation is based on actual ordering in VASP and is meant as an intermediate representation to be converted into proper objects. The kpoint index is 0-based (unlike the 1-based indexing in VASP).

Type:

dict

Reads input from filename to construct Eigenval object.

Parameters:
  • filename (str) – filename of EIGENVAL to read in

  • occu_tol (float) – tolerance for determining band gap

  • separate_spins (bool) – whether the band gap, CBM, and VBM should be reported for each individual spin channel. Defaults to False, which computes the eigenvalue band properties independent of the spin orientation. If True, the calculation must be spin-polarized.

Returns:

a pymatgen.io.vasp.outputs.Eigenval object

property eigenvalue_band_properties[source]

Band properties from the eigenvalues as a tuple, (band gap, cbm, vbm, is_band_gap_direct). In the case of separate_spins=True, the band gap, cbm, vbm, and is_band_gap_direct are each lists of length 2, with index 0 representing the spin-up channel and index 1 representing the spin-down channel.

class Elfcar(poscar, data)[source]

Bases: VolumetricData

Read an ELFCAR file which contains the Electron Localization Function (ELF) as calculated by VASP.

For ELF, “total” key refers to Spin.up, and “diff” refers to Spin.down.

This also contains information on the kinetic energy density.

Parameters:
  • poscar (Poscar or Structure) – Object containing structure.

  • data – Actual data.

classmethod from_file(filename: str) Self[source]

Reads a ELFCAR file.

Parameters:

filename – Filename

Returns:

Elfcar

get_alpha()[source]

Get the parameter alpha where ELF = 1/(1+alpha^2).

class KpointOptProps(tdos: Dos | None = None, idos: Dos | None = None, pdos: list | None = None, efermi: float | None = None, eigenvalues: dict | None = None, projected_eigenvalues: dict | None = None, projected_magnetisation: ndarray | None = None, kpoints: Kpoints | None = None, actual_kpoints: list | None = None, actual_kpoints_weights: list | None = None, dos_has_errors: bool | None = None)[source]

Bases: object

Simple container class to store KPOINTS_OPT data in a separate namespace. Used by Vasprun.

actual_kpoints: list | None = None[source]
actual_kpoints_weights: list | None = None[source]
dos_has_errors: bool | None = None[source]
efermi: float | None = None[source]
eigenvalues: dict | None = None[source]
idos: Dos | None = None[source]
kpoints: Kpoints | None = None[source]
pdos: list | None = None[source]
projected_eigenvalues: dict | None = None[source]
projected_magnetisation: ndarray | None = None[source]
tdos: Dos | None = None[source]
class Locpot(poscar: Poscar, data: ndarray, **kwargs)[source]

Bases: VolumetricData

Simple object for reading a LOCPOT file.

Parameters:
  • poscar (Poscar) – Poscar object containing structure.

  • data (np.ndarray) – Actual data.

classmethod from_file(filename, **kwargs)[source]

Read a LOCPOT file.

Parameters:

filename (str) – Path to LOCPOT file.

Returns:

Locpot

class Oszicar(filename)[source]

Bases: object

A basic parser for an OSZICAR output from VASP. In general, while the OSZICAR is useful for a quick look at the output from a VASP run, we recommend that you use the Vasprun parser instead, which gives far richer information about a run.

electronic_steps[source]

All electronic steps as a list of list of dict. e.g., [[{“rms”: 160.0, “E”: 4507.24605593, “dE”: 4507.2, “N”: 1, “deps”: -17777.0, “ncg”: 16576}, …], [….] where electronic_steps[index] refers the list of electronic steps in one ionic_step, electronic_steps[index][subindex] refers to a particular electronic step at subindex in ionic step at index. The dict of properties depends on the type of VASP run, but in general, “E”, “dE” and “rms” should be present in almost all runs.

Type:

list

ionic_steps[source]

All ionic_steps as a list of dict, e.g., [{“dE”: -526.36, “E0”: -526.36024, “mag”: 0.0, “F”: -526.36024}, …] This is the typical output from VASP at the end of each ionic step. The stored dict might be different depending on the type of VASP run.

Type:

list

Parameters:

filename (str) – Filename of file to parse.

property all_energies[source]

Compilation of all energies from all electronic steps and ionic steps as a tuple of list of energies, e.g., ((4507.24605593, 143.824705755, -512.073149912, …), …).

as_dict()[source]

MSONable dict

property final_energy[source]
class Outcar(filename)[source]

Bases: object

Parser for data in OUTCAR that is not available in Vasprun.xml.

Note, this class works a bit differently than most of the other VaspObjects, since the OUTCAR can be very different depending on which “type of run” performed.

Creating the OUTCAR class with a filename reads “regular parameters” that are always present.

magnetization[source]

Magnetization on each ion as a tuple of dict, e.g., ({“d”: 0.0, “p”: 0.003, “s”: 0.002, “tot”: 0.005}, … )

Type:

tuple

chemical_shielding[source]

Chemical shielding on each ion as a dictionary with core and valence contributions.

Type:

dict

unsym_cs_tensor[source]

Unsymmetrized chemical shielding tensor matrixes on each ion as a list. e.g., [[[sigma11, sigma12, sigma13], [sigma21, sigma22, sigma23], [sigma31, sigma32, sigma33]], …]

Type:

list

cs_g0_contribution[source]

G=0 contribution to chemical shielding. 2D rank 3 matrix.

Type:

np.array

cs_core_contribution[source]

Core contribution to chemical shielding. dict. e.g., {‘Mg’: -412.8, ‘C’: -200.5, ‘O’: -271.1}

Type:

dict

efg[source]

Electric Field Gradient (EFG) tensor on each ion as a tuple of dict, e.g., ({“cq”: 0.1, “eta”, 0.2, “nuclear_quadrupole_moment”: 0.3}, {“cq”: 0.7, “eta”, 0.8, “nuclear_quadrupole_moment”: 0.9}, …)

Type:

tuple

charge[source]

Charge on each ion as a tuple of dict, e.g., ({“p”: 0.154, “s”: 0.078, “d”: 0.0, “tot”: 0.232}, …)

Type:

tuple

is_stopped[source]

True if OUTCAR is from a stopped run (using STOPCAR, see VASP Manual).

Type:

bool

run_stats[source]

Various useful run stats as a dict including “System time (sec)”, “Total CPU time used (sec)”, “Elapsed time (sec)”, “Maximum memory used (kb)”, “Average memory used (kb)”, “User time (sec)”, “cores”.

Type:

dict

elastic_tensor[source]

Total elastic moduli (Kbar) is given in a 6x6 array matrix.

Type:

np.array

drift[source]

Total drift for each step in eV/Atom.

Type:

np.array

ngf[source]

Dimensions for the Augmentation grid.

Type:

tuple

sampling_radii[source]

Size of the sampling radii in VASP for the test charges for the electrostatic potential at each atom. Total array size is the number of elements present in the calculation.

Type:

np.array

electrostatic_potential[source]

Average electrostatic potential at each atomic position in order of the atoms in POSCAR.

Type:

np.array

final_energy_contribs[source]

Individual contributions to the total final energy as a dictionary. Include contributions from keys, e.g.: {‘DENC’: -505778.5184347, ‘EATOM’: 15561.06492564, ‘EBANDS’: -804.53201231, ‘EENTRO’: -0.08932659, ‘EXHF’: 0.0, ‘Ediel_sol’: 0.0, ‘PAW double counting’: 664.6726974100002, ‘PSCENC’: 742.48691646, ‘TEWEN’: 489742.86847338, ‘XCENC’: -169.64189814}

Type:

dict

efermi[source]

Fermi energy.

Type:

float

filename[source]

Filename.

Type:

str

final_energy[source]

Final energy after extrapolation of sigma back to 0, i.e. energy(sigma->0).

Type:

float

final_energy_wo_entrp[source]

Final energy before extrapolation of sigma, i.e. energy without entropy.

Type:

float

final_fr_energy[source]

Final “free energy”, i.e. free energy TOTEN.

Type:

float

has_onsite_density_matrices[source]

Boolean for if onsite density matrices have been set.

Type:

bool

lcalcpol[source]

If LCALCPOL has been set.

Type:

bool

lepsilon[source]

If LEPSILON has been set.

Type:

bool

nelect[source]

Returns the number of electrons in the calculation.

Type:

float

spin[source]

If spin-polarization was enabled via ISPIN.

Type:

bool

total_mag[source]

Total magnetization (in terms of the number of unpaired electrons).

Type:

float

One can then call a specific reader depending on the type of run being performed. These are currently: read_igpar(), read_lepsilon() and read_lcalcpol(), read_core_state_eign(), read_avg_core_pot().

See the documentation of those methods for more documentation.

Authors: Rickard Armiento, Shyue Ping Ong

Parameters:

filename (str) – OUTCAR filename to parse.

as_dict()[source]

MSONable dict.

read_avg_core_poten()[source]

Read the core potential at each ionic step.

Returns:

A list for each ionic step containing a list of the average core potentials for each atom: [[avg core pot]].

Example

The average core potential of the 2nd atom of the structure at the last ionic step is: [-1][1]

read_chemical_shielding()[source]

Parse the NMR chemical shieldings data. Only the second part “absolute, valence and core” will be parsed. And only the three right most field (ISO_SHIELDING, SPAN, SKEW) will be retrieved.

Returns:

List of chemical shieldings in the order of atoms from the OUTCAR. Maryland notation is adopted.

read_core_state_eigen()[source]

Read the core state eigenenergies at each ionic step.

Returns:

[core state eig]}]. The core state eigenenergie list for each AO is over all ionic step.

Return type:

A list of dict over the atom such as [{“AO”

Example

The core state eigenenergie of the 2s AO of the 6th atom of the structure at the last ionic step is [5][“2s”][-1]

read_corrections(reverse=True, terminate_on_match=True)[source]

Reads the dipol qudropol corrections into the Outcar.data[“dipol_quadrupol_correction”].

Parameters:
  • reverse (bool) – Whether to start from end of OUTCAR. Defaults to True.

  • terminate_on_match (bool) – Whether to terminate once match is found. Defaults to True.

read_cs_core_contribution()[source]

Parse the core contribution of NMR chemical shielding.

Returns:

G0 contribution matrix.

Return type:

list[list]

read_cs_g0_contribution()[source]

Parse the G0 contribution of NMR chemical shielding.

Returns:

G0 contribution matrix as list of list.

read_cs_raw_symmetrized_tensors()[source]

Parse the matrix form of NMR tensor before corrected to table.

Returns:

nsymmetrized tensors list in the order of atoms.

read_elastic_tensor()[source]

Parse the elastic tensor data.

Returns:

6x6 array corresponding to the elastic tensor from the OUTCAR.

read_electrostatic_potential()[source]

Parses the eletrostatic potential for the last ionic step.

read_fermi_contact_shift()[source]

Output example: Fermi contact (isotropic) hyperfine coupling parameter (MHz) ————————————————————- ion A_pw A_1PS A_1AE A_1c A_tot ————————————————————-

1 -0.002 -0.002 -0.051 0.000 -0.052 2 -0.002 -0.002 -0.051 0.000 -0.052 3 0.056 0.056 0.321 -0.048 0.321

[-0.002, -0.002, -0.051, 0.0, -0.052], [0.056, 0.056, 0.321, -0.048, 0.321]] from ‘fch’ data.

read_freq_dielectric()[source]

Parses the frequency dependent dielectric function (obtained with LOPTICS). Frequencies (in eV) are in self.frequencies, and dielectric tensor function is given as self.dielectric_tensor_function.

read_igpar()[source]
Renders accessible:

er_ev = e<r>_ev (dictionary with Spin.up/Spin.down as keys) er_bp = e<r>_bp (dictionary with Spin.up/Spin.down as keys) er_ev_tot = spin up + spin down summed er_bp_tot = spin up + spin down summed p_elc = spin up + spin down summed p_ion = spin up + spin down summed.

(See VASP section “LBERRY, IGPAR, NPPSTR, DIPOL tags” for info on what these are).

read_internal_strain_tensor()[source]
Reads the internal strain tensor and populates self.internal_strain_tensor with an array of voigt notation

tensors for each site.

read_lcalcpol()[source]

Reads the lcalpol.

# TODO: Document the actual variables.

read_lepsilon()[source]

Reads an LEPSILON run.

# TODO: Document the actual variables.

read_lepsilon_ionic()[source]

Reads an LEPSILON run, the ionic component.

# TODO: Document the actual variables.

read_neb(reverse=True, terminate_on_match=True)[source]

Reads NEB data. This only works with OUTCARs from both normal VASP NEB calculations or from the CI NEB method implemented by Henkelman et al.

Parameters:
  • reverse (bool) – Read files in reverse. Defaults to false. Useful for large files, esp OUTCARs, especially when used with terminate_on_match. Defaults to True here since we usually want only the final value.

  • terminate_on_match (bool) – Whether to terminate when there is at least one match in each key in pattern. Defaults to True here since we usually want only the final value.

Renders accessible:

tangent_force - Final tangent force. energy - Final energy. These can be accessed under Outcar.data[key]

read_nmr_efg()[source]

Parse the NMR Electric Field Gradient interpreted values.

Returns:

Electric Field Gradient tensors as a list of dict in the order of atoms from OUTCAR. Each dict key/value pair corresponds to a component of the tensors.

read_nmr_efg_tensor()[source]

Parses the NMR Electric Field Gradient Raw Tensors.

Returns:

A list of Electric Field Gradient Tensors in the order of Atoms from OUTCAR

read_onsite_density_matrices()[source]

Parse the onsite density matrices, returns list with index corresponding to atom index in Structure.

read_pattern(patterns, reverse=False, terminate_on_match=False, postprocess=<class 'str'>)[source]

General pattern reading. Uses monty’s regrep method. Takes the same arguments.

Parameters:
  • patterns (dict) – A dict of patterns, e.g., {“energy”: r”energy\(sigma->0\)\s+=\s+([\d\-.]+)”}.

  • reverse (bool) – Read files in reverse. Defaults to false. Useful for large files, esp OUTCARs, especially when used with terminate_on_match.

  • terminate_on_match (bool) – Whether to terminate when there is at least one match in each key in pattern.

  • postprocess (callable) – A post processing function to convert all matches. Defaults to str, i.e., no change.

Renders accessible:

Any attribute in patterns. For example, {“energy”: r”energy\(sigma->0\)\s+=\s+([\d\-.]+)”} will set the value of self.data[“energy”] = [[-1234], [-3453], …], to the results from regex and postprocess. Note that the returned values are lists of lists, because you can grep multiple items on one line.

read_piezo_tensor()[source]

Parse the piezo tensor data.

read_pseudo_zval()[source]

Create pseudopotential ZVAL dictionary.

read_table_pattern(header_pattern, row_pattern, footer_pattern, postprocess=<class 'str'>, attribute_name=None, last_one_only=True, first_one_only=False)[source]

Parse table-like data. A table composes of three parts: header, main body, footer. All the data matches “row pattern” in the main body will be returned.

Parameters:
  • header_pattern (str) – The regular expression pattern matches the table header. This pattern should match all the text immediately before the main body of the table. For multiple sections table match the text until the section of interest. MULTILINE and DOTALL options are enforced, as a result, the “.” meta-character will also match “n” in this section.

  • row_pattern (str) – The regular expression matches a single line in the table. Capture interested field using regular expression groups.

  • footer_pattern (str) – The regular expression matches the end of the table. E.g. a long dash line.

  • postprocess (callable) – A post processing function to convert all matches. Defaults to str, i.e., no change.

  • attribute_name (str) – Name of this table. If present the parsed data will be attached to “data. e.g. self.data[“efg”] = […]

  • last_one_only (bool) – All the tables will be parsed, if this option is set to True, only the last table will be returned. The enclosing list will be removed. i.e. Only a single table will be returned. Default to be True. Incompatible with first_one_only.

  • first_one_only (bool) – Only the first occurrence of the table will be parsed and the parsing procedure will stop. The enclosing list will be removed. i.e. Only a single table will be returned. Incompatible with last_one_only.

Returns:

List of tables. 1) A table is a list of rows. 2) A row if either a list of attribute values in case the capturing group is defined without name in row_pattern, or a dict in case that named capturing groups are defined by row_pattern.

class Procar(filename)[source]

Bases: object

Object for reading a PROCAR file.

data[source]

The PROCAR data of the form below. It should VASP uses 1-based indexing, but all indices are converted to 0-based here. { spin: nd.array accessed with (k-point index, band index, ion index, orbital index) }

Type:

dict

weights[source]

The weights associated with each k-point as an nd.array of length nkpoints.

Type:

np.array

phase_factors[source]

Phase factors, where present (e.g. LORBIT = 12). A dict of the form: { spin: complex nd.array accessed with (k-point index, band index, ion index, orbital index) }

Type:

dict

nbands[source]

Number of bands.

Type:

int

nkpoints[source]

Number of k-points.

Type:

int

nions[source]

Number of ions.

Type:

int

Parameters:

filename – Name of file containing PROCAR.

get_occupation(atom_index, orbital)[source]

Returns the occupation for a particular orbital of a particular atom.

Parameters:
  • atom_num (int) – Index of atom in the PROCAR. It should be noted that VASP uses 1-based indexing for atoms, but this is converted to 0-based indexing in this parser to be consistent with representation of structures in pymatgen.

  • orbital (str) – An orbital. If it is a single character, e.g., s, p, d or f, the sum of all s-type, p-type, d-type or f-type orbitals occupations are returned respectively. If it is a specific orbital, e.g., px, dxy, etc., only the occupation of that orbital is returned.

Returns:

Sum occupation of orbital of atom.

get_projection_on_elements(structure: Structure)[source]

Method returning a dictionary of projections on elements.

Parameters:

structure (Structure) – Input structure.

Returns:

[k index][b index][{Element:values}]]

Return type:

a dictionary in the {Spin.up

exception UnconvergedVASPWarning[source]

Bases: Warning

Warning for unconverged vasp run.

exception VaspParseError[source]

Bases: ParseError

Exception class for VASP parsing.

class Vasprun(filename: str | Path, ionic_step_skip: int | None = None, ionic_step_offset: int = 0, parse_dos: bool = True, parse_eigen: bool = True, parse_projected_eigen: bool = False, parse_potcar_file: bool = True, occu_tol: float = 1e-08, separate_spins: bool = False, exception_on_bad_xml: bool = True)[source]

Bases: MSONable

Vastly improved cElementTree-based parser for vasprun.xml files. Uses iterparse to support incremental parsing of large files. Speedup over Dom is at least 2x for smallish files (~1Mb) to orders of magnitude for larger files (~10Mb).

VASP results

ionic_steps[source]

All ionic steps in the run as a list of {“structure”: structure at end of run, “electronic_steps”: {All electronic step data in vasprun file}, “stresses”: stress matrix}.

Type:

list

tdos[source]

Total dos calculated at the end of run.

Type:

Dos

idos[source]

Integrated dos calculated at the end of run.

Type:

Dos

pdos[source]

List of list of PDos objects. Access as pdos[atomindex][orbitalindex].

Type:

list

efermi[source]

Fermi energy.

Type:

float

eigenvalues[source]

Final eigenvalues as a dict of {(spin, kpoint index):[[eigenvalue, occu]]}. The kpoint index is 0-based (unlike the 1-based indexing in VASP).

Type:

dict

projected_eigenvalues[source]

Final projected eigenvalues as a dict of {spin: nd-array}. To access a particular value, you need to do Vasprun.projected_eigenvalues[spin][kpoint index][band index][atom index][orbital_index]. The kpoint, band and atom indices are 0-based (unlike the 1-based indexing in VASP).

Type:

dict

projected_magnetisation[source]

Final projected magnetization as a numpy array with the shape (nkpoints, nbands, natoms, norbitals, 3). Where the last axis is the contribution in the 3 Cartesian directions. This attribute is only set if spin-orbit coupling (LSORBIT = True) or non-collinear magnetism (LNONCOLLINEAR = True) is turned on in the INCAR.

Type:

np.array

other_dielectric[source]

Dictionary, with the tag comment as key, containing other variants of the real and imaginary part of the dielectric constant (e.g., computed by RPA) in function of the energy (frequency). Optical properties (e.g. absorption coefficient) can be obtained through this. The data is given as a tuple of 3 values containing each of them the energy, the real part tensor, and the imaginary part tensor ([energies],[[real_partxx,real_partyy,real_partzz,real_partxy, real_partyz,real_partxz]],[[imag_partxx,imag_partyy,imag_partzz,imag_partxy, imag_partyz, imag_partxz]]).

Type:

dict

nionic_steps[source]

The total number of ionic steps. This number is always equal to the total number of steps in the actual run even if ionic_step_skip is used.

Type:

int

force_constants[source]

Force constants computed in phonon DFPT run(IBRION = 8). The data is a 4D numpy array of shape (natoms, natoms, 3, 3).

Type:

np.array

normalmode_eigenvals[source]

Normal mode frequencies. 1D numpy array of size 3*natoms.

Type:

np.array

normalmode_eigenvecs[source]

Normal mode eigen vectors. 3D numpy array of shape (3*natoms, natoms, 3).

Type:

np.array

md_data[source]

Available only for ML MD runs, i.e., INCAR with ML_LMLFF = .TRUE. md_data is a list of dict with the following format: [{‘energy’: {‘e_0_energy’: -525.07195568, ‘e_fr_energy’: -525.07195568, ‘e_wo_entrp’: -525.07195568, ‘kinetic’: 3.17809233, ‘lattice kinetic’: 0.0, ‘nosekinetic’: 1.323e-5, ‘nosepot’: 0.0, ‘total’: -521.89385012}, ‘forces’: [[0.17677989, 0.48309874, 1.85806696], …], ‘structure’: Structure object}].

Type:

list

incar[source]

Incar object for parameters specified in INCAR file.

Type:

Incar

parameters[source]

Incar object with parameters that vasp actually used, including all defaults.

Type:

Incar

kpoints[source]

Kpoints object for KPOINTS specified in run.

Type:

Kpoints

actual_kpoints[source]

List of actual kpoints, e.g., [[0.25, 0.125, 0.08333333], [-0.25, 0.125, 0.08333333], [0.25, 0.375, 0.08333333], ….].

Type:

list

actual_kpoints_weights[source]

List of kpoint weights, E.g., [0.04166667, 0.04166667, 0.04166667, 0.04166667, 0.04166667, ….].

Type:

list

atomic_symbols[source]

List of atomic symbols, e.g., [“Li”, “Fe”, “Fe”, “P”, “P”, “P”].

Type:

list

potcar_symbols[source]

List of POTCAR symbols. e.g., [“PAW_PBE Li 17Jan2003”, “PAW_PBE Fe 06Sep2000”, ..].

Type:

list

kpoints_opt_props[source]

Object whose attributes are the data from KPOINTS_OPT (if present, else None). Attributes of the same name have the same format and meaning as Vasprun (or they are None if absent). Attributes are: tdos, idos, pdos, efermi, eigenvalues, projected_eigenvalues, projected magnetisation, kpoints, actual_kpoints, actual_kpoints_weights, dos_has_errors.

Type:

object

Author: Shyue Ping Ong

Parameters:
  • filename (str) – Filename to parse

  • ionic_step_skip (int) – If ionic_step_skip is a number > 1, only every ionic_step_skip ionic steps will be read for structure and energies. This is very useful if you are parsing very large vasprun.xml files and you are not interested in every single ionic step. Note that the final energies may not be the actual final energy in the vasprun.

  • ionic_step_offset (int) – Used together with ionic_step_skip. If set, the first ionic step read will be offset by the amount of ionic_step_offset. For example, if you want to start reading every 10th structure but only from the 3rd structure onwards, set ionic_step_skip to 10 and ionic_step_offset to 3. Main use case is when doing statistical structure analysis with extremely long time scale multiple VASP calculations of varying numbers of steps.

  • parse_dos (bool) – Whether to parse the dos. Defaults to True. Set to False to shave off significant time from the parsing if you are not interested in getting those data.

  • parse_eigen (bool) – Whether to parse the eigenvalues. Defaults to True. Set to False to shave off significant time from the parsing if you are not interested in getting those data.

  • parse_projected_eigen (bool) – Whether to parse the projected eigenvalues and magnetization. Defaults to False. Set to True to obtain projected eigenvalues and magnetization. Note that this can take an extreme amount of time and memory. So use this wisely.

  • parse_potcar_file (bool/str) – Whether to parse the potcar file to read the potcar hashes for the potcar_spec attribute. Defaults to True, where no hashes will be determined and the potcar_spec dictionaries will read {“symbol”: ElSymbol, “hash”: None}. By Default, looks in the same directory as the vasprun.xml, with same extensions as Vasprun.xml. If a string is provided, looks at that filepath.

  • occu_tol (float) – Sets the minimum tol for the determination of the vbm and cbm. Usually the default of 1e-8 works well enough, but there may be pathological cases.

  • separate_spins (bool) – Whether the band gap, CBM, and VBM should be reported for each individual spin channel. Defaults to False, which computes the eigenvalue band properties independent of the spin orientation. If True, the calculation must be spin-polarized.

  • exception_on_bad_xml (bool) – Whether to throw a ParseException if a malformed XML is detected. Default to True, which ensures only proper vasprun.xml are parsed. You can set to False if you want partial results (e.g., if you are monitoring a calculation during a run), but use the results with care. A warning is issued.

as_dict()[source]

JSON-serializable dict representation.

calculate_efermi(tol: float = 0.001)[source]

Calculate the Fermi level using a robust algorithm.

Sometimes VASP can put the Fermi level just inside of a band due to issues in the way band occupancies are handled. This algorithm tries to detect and correct for this bug.

Slightly more details are provided here: https://www.vasp.at/forum/viewtopic.php?f=4&t=17981

property complete_dos[source]

A complete dos object which incorporates the total dos and all projected dos.

property complete_dos_normalized: CompleteDos[source]

A CompleteDos object which incorporates the total DOS and all projected DOS. Normalized by the volume of the unit cell with units of states/eV/unit cell volume.

property converged: bool[source]

Returns: bool: True if a relaxation run is both ionically and electronically converged.

property converged_electronic: bool[source]

Returns: bool: True if electronic step convergence has been reached in the final ionic step.

property converged_ionic: bool[source]

Returns: bool: True if ionic step convergence has been reached, i.e. that vasp

exited before reaching the max ionic steps for a relaxation run. In case IBRION=0 (MD) True if the max ionic steps are reached.

property dielectric[source]

Returns: The real and imaginary part of the dielectric constant (e.g., computed by RPA) in function of the energy (frequency). Optical properties (e.g. absorption coefficient) can be obtained through this. The data is given as a tuple of 3 values containing each of them the energy, the real part tensor, and the imaginary part tensor ([energies],[[real_partxx,real_partyy,real_partzz,real_partxy, real_partyz,real_partxz]],[[imag_partxx,imag_partyy,imag_partzz, imag_partxy, imag_partyz, imag_partxz]]).

property eigenvalue_band_properties[source]

Band properties from the eigenvalues as a tuple, (band gap, cbm, vbm, is_band_gap_direct). In the case of separate_spins=True, the band gap, cbm, vbm, and is_band_gap_direct are each lists of length 2, with index 0 representing the spin-up channel and index 1 representing the spin-down channel.

property epsilon_ionic: list[float][source]

Property only available for DFPT calculations and when IBRION=5, 6, 7 or 8.

Returns:

The ionic part of the static dielectric constant. Present when it’s a DFPT run (LEPSILON=TRUE) and IBRION=5, 6, 7 or 8

property epsilonassets: list[float][source]

Property only available for DFPT calculations.

Returns:

The static part of the dielectric constant. Present when it’s a DFPT run (LEPSILON=TRUE)

property epsilonassets_wolfe: list[float][source]

Property only available for DFPT calculations.

Returns:

The static part of the dielectric constant without any local field effects. Present when it’s a DFPT run (LEPSILON=TRUE)

property final_energy[source]
get_band_structure(kpoints_filename: str | None = None, efermi: float | Literal['smart'] | None = None, line_mode: bool = False, force_hybrid_mode: bool = False, ignore_kpoints_opt: bool = False) BandStructureSymmLine | BandStructure[source]

Get the band structure as a BandStructure object.

Parameters:
  • kpoints_filename – Full path of the KPOINTS file from which the band structure is generated. If none is provided, the code will try to intelligently determine the appropriate KPOINTS file by substituting the filename of the vasprun.xml with KPOINTS (or KPOINTS_OPT). The latter is the default behavior.

  • efermi – The Fermi energy associated with the bandstructure, in eV. By default (None), uses the value reported by VASP in vasprun.xml. To manually set the Fermi energy, pass a float. Pass ‘smart’ to use the calculate_efermi() method, which calculates the Fermi level by first checking whether it lies within a small tolerance (by default 0.001 eV) of a band edge) If it does, the Fermi level is placed in the center of the bandgap. Otherwise, the value is identical to the value reported by VASP.

  • line_mode – Force the band structure to be considered as a run along symmetry lines. (Default: False)

  • force_hybrid_mode – Makes it possible to read in self-consistent band structure calculations for every type of functional. (Default: False)

  • ignore_kpoints_opt – Normally, if KPOINTS_OPT data exists, it has the band structure data. Set this flag to ignore it. (Default: False)

Returns:

a BandStructure object (or more specifically a BandStructureSymmLine object if the run is detected to be a run along symmetry lines)

Two types of runs along symmetry lines are accepted: non-sc with Line-Mode in the KPOINT file or hybrid, self-consistent with a uniform grid+a few kpoints along symmetry lines (explicit KPOINTS file) (it’s not possible to run a non-sc band structure with hybrid functionals). The explicit KPOINTS file needs to have data on the kpoint label as commentary.

If VASP was run with KPOINTS_OPT, it reads the data from that file unless told otherwise. This overrides hybrid mode.

get_computed_entry(inc_structure=True, parameters=None, data=None, entry_id: str | None = None)[source]

Returns a ComputedEntry or ComputedStructureEntry from the Vasprun.

Parameters:
  • inc_structure (bool) – Set to True if you want ComputedStructureEntries to be returned instead of ComputedEntries.

  • parameters (list) – Input parameters to include. It has to be one of the properties supported by the Vasprun object. If parameters is None, a default set of parameters that are necessary for typical post-processing will be set.

  • data (list) – Output data to include. Has to be one of the properties supported by the Vasprun object.

  • entry_id (str) – Specify an entry id for the ComputedEntry. Defaults to “vasprun-{current datetime}”

Returns:

ComputedStructureEntry/ComputedEntry

get_potcars(path: str | Path | bool) Potcar | None[source]

Returns the POTCAR from the specified path.

Parameters:

path (str | Path | bool) – If a str or Path, the path to search for POTCARs. If a bool, whether to take the search path from the specified vasprun.xml

Returns:

The POTCAR from the specified path or None if not found/no path specified.

Return type:

Potcar | None

get_trajectory()[source]

This method returns a Trajectory object, which is an alternative representation of self.structures into a single object. Forces are added to the Trajectory as site properties.

Returns:

from pymatgen.core.trajectory

Return type:

Trajectory

property hubbards: dict[str, float][source]

Hubbard U values used if a vasprun is a GGA+U run. Otherwise an empty dict.

property is_hubbard: bool[source]

True if run is a DFT+U run.

property is_spin: bool[source]

True if run is spin-polarized.

property md_n_steps: int[source]

Number of steps for md runs.

property optical_absorption_coeff: list[float][source]

Calculate the optical absorption coefficient from the dielectric constants. Note that this method is only implemented for optical properties calculated with GGA and BSE.

Returns:

optical absorption coefficient

Return type:

list[float]

property run_type[source]

Returns the run type. Currently detects GGA, metaGGA, HF, HSE, B3LYP, and hybrid functionals based on relevant INCAR tags. LDA is assigned if PAW POTCARs are used and no other functional is detected.

Hubbard U terms and vdW corrections are detected automatically as well.

property structures: list[Structure][source]

Returns: List of Structure objects for the structure at each ionic step.

update_charge_from_potcar(path)[source]

Sets the charge of a structure based on the POTCARs found.

Parameters:

path – Path to search for POTCARs

update_potcar_spec(path)[source]
Parameters:

path – Path to search for POTCARs

Returns:

Potcar spec from path.

class VolumetricData(structure: Structure, data: dict[str, ndarray], distance_matrix: ndarray | None = None, data_aug: ndarray | None = None)[source]

Bases: VolumetricData

Container for volumetric data that allows for reading/writing with Poscar-type data.

Typically, this constructor is not used directly and the static from_file constructor is used. This constructor is designed to allow summation and other operations between VolumetricData objects.

Parameters:
  • structure (Structure) – associated with the volumetric data

  • data (dict[str, np.array]) – Actual volumetric data.

  • distance_matrix (np.array) – A pre-computed distance matrix if available. Useful so pass distance_matrices between sums, short-circuiting an otherwise expensive operation.

  • data_aug (np.array) – Any extra information associated with volumetric data (typically augmentation charges)

static parse_file(filename: str) tuple[Poscar, dict, dict][source]

Convenience method to parse a generic volumetric data file in the vasp like format. Used by subclasses for parsing file.

Parameters:

filename (str) – Path of file to parse

Returns:

Poscar object, data dict, data_aug dict

Return type:

tuple[Poscar, dict, dict]

write_file(file_name: str | Path, vasp4_compatible: bool = False) None[source]

Write the VolumetricData object to a vasp compatible file.

Parameters:
  • file_name (str) – Path to a file

  • vasp4_compatible (bool) – True if the format is vasp4 compatible

class WSWQ(nspin: int, nkpoints: int, nbands: int, me_real: ndarray, me_imag: ndarray)[source]

Bases: MSONable

Class for reading a WSWQ file. The WSWQ file is used to calculation the wave function overlaps between

  • W: Wavefunctions in the current directory’s WAVECAR file

  • WQ: Wavefunctions stored in a filed named the WAVECAR.qqq.

The overlap is computed using the overlap operator S which make the PAW wavefunctions orthogonormal:

<W_k,m| S | W_k,n> = delta_{mn}

The WSWQ file contains matrix elements of the overlap operator S evaluated between the planewave wavefunctions W and WQ:

COVL_k,mn = < W_s,k,m | S | WQ_s,k,n >

The indices of WSWQ.data are:

[spin][kpoint][band_i][band_j]

nspin[source]

Number of spin channels

Type:

int

nkpoints[source]

Number of k-points

Type:

int

nbands[source]

Number of bands

Type:

int

me_real[source]

Real part of the overlap matrix elements

Type:

numpy.ndarray

me_imag[source]

Imaginary part of the overlap matrix elements

Type:

numpy.ndarray

property data[source]

Complex overlap matrix.

classmethod from_file(filename: str) Self[source]

Constructs a WSWQ object from a file.

Parameters:

filename (str) – Name of WSWQ file.

Returns:

WSWQ object.

me_imag: ndarray[source]
me_real: ndarray[source]
nbands: int[source]
nkpoints: int[source]
nspin: int[source]
class Wavecar(filename='WAVECAR', verbose=False, precision='normal', vasp_type=None)[source]

Bases: object

This is a class that contains the (pseudo-) wavefunctions from VASP.

Coefficients are read from the given WAVECAR file and the corresponding G-vectors are generated using the algorithm developed in WaveTrans (see acknowledgments below). To understand how the wavefunctions are evaluated, please see the evaluate_wavefunc docstring.

It should be noted that the pseudopotential augmentation is not included in the WAVECAR file. As a result, some caution should be exercised when deriving value from this information.

The usefulness of this class is to allow the user to do projections or band unfolding style manipulations of the wavefunction. An example of this can be seen in the work of Shen et al. 2017 (https://doi.org/10.1103/PhysRevMaterials.1.065001).

filename[source]

String of the input file (usually WAVECAR).

Type:

str

vasp_type[source]

String that determines VASP type the WAVECAR was generated with. One of ‘std’, ‘gam’, ‘ncl’.

Type:

str

nk[source]

Number of k-points from the WAVECAR.

Type:

int

nb[source]

Number of bands per k-point.

Type:

int

encut[source]

Energy cutoff (used to define G_{cut}).

Type:

float

efermi[source]

Fermi energy.

Type:

float

a[source]

Primitive lattice vectors of the cell (e.g. a_1 = self.a[0, :]).

Type:

np.array

b[source]

Reciprocal lattice vectors of the cell (e.g. b_1 = self.b[0, :]).

Type:

np.array

vol[source]

The volume of the unit cell in real space.

Type:

float

kpoints[source]

The list of k-points read from the WAVECAR file.

Type:

np.array

band_energy[source]

The list of band eigenenergies (and corresponding occupancies) for each kpoint, where the first index corresponds to the index of the k-point (e.g. self.band_energy[kp]).

Type:

list

Gpoints[source]

The list of generated G-points for each k-point (a double list), which are used with the coefficients for each k-point and band to recreate the wavefunction (e.g. self.Gpoints[kp] is the list of G-points for k-point kp). The G-points depend on the k-point and reciprocal lattice and therefore are identical for each band at the same k-point. Each G-point is represented by integer multipliers (e.g. assuming Gpoints[kp][n] == [n_1, n_2, n_3], then G_n = n_1*b_1 + n_2*b_2 + n_3*b_3)

Type:

list

coeffs[source]

The list of coefficients for each k-point and band for reconstructing the wavefunction. For non-spin-polarized, the first index corresponds to the kpoint and the second corresponds to the band (e.g. self.coeffs[kp][b] corresponds to k-point kp and band b). For spin-polarized calculations, the first index is for the spin. If the calculation was non-collinear, then self.coeffs[kp][b] will have two columns (one for each component of the spinor).

Type:

list

Acknowledgments:

This code is based upon the Fortran program, WaveTrans, written by R. M. Feenstra and M. Widom from the Dept. of Physics at Carnegie Mellon University. To see the original work, please visit: https://www.andrew.cmu.edu/user/feenstra/wavetrans/

Author: Mark Turiansky

Information is extracted from the given WAVECAR.

Parameters:
  • filename (str) – input file (default: WAVECAR)

  • verbose (bool) – determines whether processing information is shown

  • precision (str) – determines how fine the fft mesh is (normal or accurate), only the first letter matters

  • vasp_type (str) – determines the VASP type that is used, allowed values are [‘std’, ‘gam’, ‘ncl’] (only first letter is required)

evaluate_wavefunc(kpoint: int, band: int, r: ndarray, spin: int = 0, spinor: int = 0) complex64[source]

Evaluates the wavefunction for a given position, r.

The wavefunction is given by the k-point and band. It is evaluated at the given position by summing over the components. Formally,

psi_n^k (r) = sum_{i=1}^N c_i^{n,k} exp (i (k + G_i^{n,k}) cdot r)

where psi_n^k is the wavefunction for the nth band at k-point k, N is the number of plane waves, c_i^{n,k} is the ith coefficient that corresponds to the nth band and k-point k, and G_i^{n,k} is the ith G-point corresponding to k-point k.

NOTE: This function is very slow; a discrete fourier transform is the preferred method of evaluation (see Wavecar.fft_mesh).

Parameters:
  • kpoint (int) – the index of the kpoint where the wavefunction will be evaluated

  • band (int) – the index of the band where the wavefunction will be evaluated

  • r (np.array) – the position where the wavefunction will be evaluated

  • spin (int) – spin index for the desired wavefunction (only for ISPIN = 2, default = 0)

  • spinor (int) – component of the spinor that is evaluated (only used if vasp_type == ‘ncl’)

Returns:

a complex value corresponding to the evaluation of the wavefunction

fft_mesh(kpoint: int, band: int, spin: int = 0, spinor: int = 0, shift: bool = True) ndarray[source]

Places the coefficients of a wavefunction onto an fft mesh.

Once the mesh has been obtained, a discrete fourier transform can be used to obtain real-space evaluation of the wavefunction. The output of this function can be passed directly to numpy’s fft function. For .. rubric:: Example

mesh = Wavecar(‘WAVECAR’).fft_mesh(kpoint, band) evals = np.fft.ifftn(mesh)

Parameters:
  • kpoint (int) – the index of the kpoint where the wavefunction will be evaluated

  • band (int) – the index of the band where the wavefunction will be evaluated

  • spin (int) – the spin of the wavefunction for the desired wavefunction (only for ISPIN = 2, default = 0)

  • spinor (int) – component of the spinor that is evaluated (only used if vasp_type == ‘ncl’)

  • shift (bool) – determines if the zero frequency coefficient is placed at index (0, 0, 0) or centered

Returns:

a numpy ndarray representing the 3D mesh of coefficients

get_parchg(poscar: Poscar, kpoint: int, band: int, spin: int | None = None, spinor: int | None = None, phase: bool = False, scale: int = 2) Chgcar[source]

Generates a Chgcar object, which is the charge density of the specified wavefunction.

This function generates a Chgcar object with the charge density of the wavefunction specified by band and kpoint (and spin, if the WAVECAR corresponds to a spin-polarized calculation). The phase tag is a feature that is not present in VASP. For a real wavefunction, the phase tag being turned on means that the charge density is multiplied by the sign of the wavefunction at that point in space. A warning is generated if the phase tag is on and the chosen kpoint is not Gamma.

Note: Augmentation from the PAWs is NOT included in this function. The maximal charge density will differ from the PARCHG from VASP, but the qualitative shape of the charge density will match.

Parameters:
  • poscar (pymatgen.io.vasp.inputs.Poscar) – Poscar object that has the structure associated with the WAVECAR file

  • kpoint (int) – the index of the kpoint for the wavefunction

  • band (int) – the index of the band for the wavefunction

  • spin (int) – optional argument to specify the spin. If the Wavecar has ISPIN = 2, spin is None generates a Chgcar with total spin and magnetization, and spin == {0, 1} specifies just the spin up or down component.

  • spinor (int) – optional argument to specify the spinor component for noncollinear data wavefunctions (allowed values of None, 0, or 1)

  • phase (bool) – flag to determine if the charge density is multiplied by the sign of the wavefunction. Only valid for real wavefunctions.

  • scale (int) – scaling for the FFT grid. The default value of 2 is at least as fine as the VASP default.

Returns:

a pymatgen.io.vasp.outputs.Chgcar object

write_unks(directory: str) None[source]

Write the UNK files to the given directory.

Writes the cell-periodic part of the bloch wavefunctions from the WAVECAR file to each of the UNK files. There will be one UNK file for each of the kpoints in the WAVECAR file.

Note

wannier90 expects the full kpoint grid instead of the symmetry- reduced one that VASP stores the wavefunctions on. You should run a nscf calculation with ISYM=0 to obtain the correct grid.

Parameters:

directory (str) – directory where the UNK files are written

class Waveder(cder_real: ndarray, cder_imag: ndarray)[source]

Bases: MSONable

Representation of the WAVEDER file.

The LOPTICS tag produces a WAVEDER file which contains the derivative of the orbitals with respect to k. Since the data is complex, we need to split it into the real and imaginary parts for JSON serialization.

Note

The way that VASP writes the WAVEDER and WAVEDERF has slightly different logic when indexing the bands. This results in the formatted WAVDERF only indexing between filled bands. (i.e. all the matrix elements are between the states i=1:8 and j=1:8 in a two atom Si calculation, which is likely a VASP bug). As such, it is recommended to used the hidden LVEL=.True. flag in VASP which will force indexing over all bands.

The order of the indices of the data are: [

band index1, band index2, kpoint index, spin index, cartesian direction,

]

cder_real[source]

Real part of the derivative of the orbitals with respect to k.

Type:

numpy.ndarray

cder_imag[source]

Imaginary part of the derivative of the orbitals with respect to k.

Type:

numpy.ndarray

Author: Miguel Dias Costa, Kamal Choudhary, Jimmy-Xuan Shen

property cder[source]

Return the complex derivative of the orbitals with respect to k.

cder_imag: ndarray[source]
cder_real: ndarray[source]
classmethod from_binary(filename: str, data_type: str = 'complex64') Self[source]

Read the WAVEDER file and returns a Waveder object.

Parameters:
  • filename – Name of file containing WAVEDER.

  • data_type – Data type of the WAVEDER file. Default is complex64. If the file was generated with the “gamma” version of VASP, the data type can be either “float64” or “float32”.

Returns:

Waveder object.

classmethod from_formatted(filename: str) Self[source]

Reads the WAVEDERF file and returns a Waveder object.

Note: This file is only produced when LOPTICS is true AND vasp has been recompiled after uncommenting the line that calls WRT_CDER_BETWEEN_STATES_FORMATTED in linear_optics.F It is recommended to use from_binary instead since the binary file is much smaller and contains the same information.

Parameters:

filename (str) – The name of the WAVEDER file.

Returns:

A Waveder object.

get_orbital_derivative_between_states(band_i, band_j, kpoint, spin, cart_dir)[source]

Method returning a value between bands band_i and band_j for k-point index, spin-channel and Cartesian direction.

Parameters:
  • band_i (int) – Index of band i

  • band_j (int) – Index of band j

  • kpoint (int) – Index of k-point

  • spin (int) – Index of spin-channel (0 or 1)

  • cart_dir (int) – Index of Cartesian direction (0,1,2)

Returns:

a float value

property nbands[source]

Returns the number of bands.

property nkpoints[source]

Returns the number of k-points.

property nspin[source]

Returns the number of spin channels.

class Xdatcar(filename, ionicstep_start=1, ionicstep_end=None, comment=None)[source]

Bases: object

Class representing an XDATCAR file. Only tested with VASP 5.x files.

structures[source]

List of structures parsed from XDATCAR.

Type:

list

comment[source]

Optional comment string.

Type:

str

Authors: Ram Balachandran

Init a Xdatcar.

Parameters:
  • filename (str) – Filename of input XDATCAR file.

  • ionicstep_start (int) – Starting number of ionic step.

  • ionicstep_end (int) – Ending number of ionic step.

  • comment (str) – Optional comment attached to this set of structures.

concatenate(filename, ionicstep_start=1, ionicstep_end=None)[source]

Concatenate structures in file to Xdatcar.

Parameters:
  • filename (str) – Filename of XDATCAR file to be concatenated.

  • ionicstep_start (int) – Starting number of ionic step.

  • ionicstep_end (int) – Ending number of ionic step.

TODO (rambalachandran): Requires a check to ensure if the new concatenating file

has the same lattice structure and atoms as the Xdatcar class.

get_str(ionicstep_start: int = 1, ionicstep_end: int | None = None, significant_figures: int = 8) str[source]

Write Xdatcar class to a string.

Parameters:
  • ionicstep_start (int) – Starting number of ionic step.

  • ionicstep_end (int) – Ending number of ionic step.

  • significant_figures (int) – Number of significant figures.

property natoms[source]

Sequence of number of sites of each type associated with the Poscar. Similar to 7th line in vasp 5+ Xdatcar.

property site_symbols[source]

Sequence of symbols associated with the Xdatcar. Similar to 6th line in vasp 5+ Xdatcar.

write_file(filename, **kwargs)[source]

Write Xdatcar class into a file.

Parameters:
  • filename (str) – Filename of output XDATCAR file.

  • **kwargs – Supported kwargs are the same as those for the Xdatcar.get_str method and are passed through directly.

get_adjusted_fermi_level(efermi, cbm, band_structure)[source]

When running a band structure computations the Fermi level needs to be take from the static run that gave the charge density used for the non-self consistent band structure run. Sometimes this Fermi level is however a little too low because of the mismatch between the uniform grid used in the static run and the band structure k-points (e.g., the VBM is on Gamma and the Gamma point is not in the uniform mesh). Here we use a procedure consisting in looking for energy levels higher than the static Fermi level (but lower than the LUMO) if any of these levels make the band structure appears insulating and not metallic anymore, we keep this adjusted fermi level. This procedure has shown to detect correctly most insulators.

Parameters:
  • efermi (float) – The Fermi energy of the static run.

  • cbm (float) – The conduction band minimum of the static run.

  • band_structure (BandStructureSymmLine) – A band structure object.

Returns:

A new adjusted Fermi level.

Return type:

float

get_band_structure_from_vasp_multiple_branches(dir_name, efermi=None, projections=False)[source]

This method is used to get band structure info from a VASP directory. It takes into account that the run can be divided in several branches named “branch_x”. If the run has not been divided in branches the method will turn to parsing vasprun.xml directly.

The method returns None is there’s a parsing error

Parameters:
  • dir_name – Directory containing all bandstructure runs.

  • efermi – Efermi for bandstructure.

  • projections – True if you want to get the data on site projections if any. Note that this is sometimes very large

Returns:

A BandStructure Object

pymatgen.io.vasp.sets module

This module defines the VaspInputSet abstract base class and a concrete implementation for the parameters developed and tested by the core team of pymatgen, including the Materials Virtual Lab, Materials Project and the MIT high throughput project. The basic concept behind an input set is to specify a scheme to generate a consistent set of VASP inputs from a structure without further user intervention. This ensures comparability across runs.

Read the following carefully before implementing new input sets:

  1. 99% of what needs to be done can be done by specifying user_incar_settings to override some of the defaults of various input sets. Unless there is an extremely good reason to add a new set, do not add one. E.g., if you want to turn the Hubbard U off, just set “LDAU”: False as a user_incar_setting.

  2. All derivative input sets should inherit appropriate configurations (e.g., from MPRelaxSet), and more often than not, DictSet should be the superclass. Proper superclass delegation should be used where possible. In particular, you are not supposed to implement your own as_dict or from_dict for derivative sets unless you know what you are doing. Improper overriding the as_dict and from_dict protocols is the major cause of implementation headaches. If you need an example, look at how the MPStaticSet is initialized.

The above are recommendations. The following are UNBREAKABLE rules:

  1. All input sets must take in a structure, list of structures or None as the first argument. If None, the input set should perform a stateless initialization and before any output can be written, a structure must be set.

  2. user_incar_settings, user_kpoints_settings and user_<whatever>_settings are ABSOLUTE. Any new sets you implement must obey this. If a user wants to override your settings, you assume he knows what he is doing. Do not magically override user supplied settings. You can issue a warning if you think the user is wrong.

  3. All input sets must save all supplied args and kwargs as instance variables. E.g., self.arg = arg and self.kwargs = kwargs in the __init__. This ensures the as_dict and from_dict work correctly.

exception BadInputSetWarning[source]

Bases: UserWarning

Warning class for bad but legal VASP inputs.

class DictSet(structure: ~pymatgen.core.structure.Structure | None = <property object>, config_dict: dict = <factory>, files_to_transfer: dict = <factory>, user_incar_settings: dict = <factory>, user_kpoints_settings: dict = <factory>, user_potcar_settings: dict = <factory>, constrain_total_magmom: bool = False, sort_structure: bool = True, user_potcar_functional: ~typing.Literal['PBE', 'PBE_52', 'PBE_54', 'LDA', 'LDA_52', 'LDA_54', 'PW91', 'LDA_US', 'PW91_US'] | None = None, force_gamma: bool = False, reduce_structure: ~typing.Literal['niggli', 'LLL'] | None = None, vdw: str | None = None, use_structure_charge: bool = False, standardize: bool = False, sym_prec: float = 0.1, international_monoclinic: bool = True, validate_magmom: bool = True, inherit_incar: bool | list[str] = False, auto_ismear: bool = False, bandgap_tol: float = 0.0001, bandgap: float | None = None, prev_incar: str | dict | None = None, prev_kpoints: str | ~pymatgen.io.vasp.inputs.Kpoints | None = None)[source]

Bases: VaspInputSet

Concrete implementation of VaspInputSet that is initialized from a dict settings. This allows arbitrary settings to be input. In general, this is rarely used directly unless there is a source of settings in yaml format (e.g., from a REST interface). It is typically used by other VaspInputSets for initialization.

Special consideration should be paid to the way the MAGMOM initialization for the INCAR is done. The initialization differs depending on the type of structure and the configuration settings. The order in which the magmom is determined is as follows:

  1. If the site itself has a magmom setting (i.e. site.properties[“magmom”] = float),

    that is used. This can be set with structure.add_site_property().

  2. If the species of the site has a spin setting, that is used. This can be set

    with structure.add_spin_by_element().

  3. If the species itself has a particular setting in the config file, that is used, e.g., Mn3+ may have a different magmom than Mn4+.

  4. Lastly, the element symbol itself is checked in the config file. If there are no settings, a default value of 0.6 is used.

Parameters:
  • structure (Structure) – The Structure to create inputs for. If None, the input set is initialized without a Structure but one must be set separately before the inputs are generated.

  • config_dict (dict) – The config dictionary to use.

  • files_to_transfer (dict) – A dictionary of {filename: filepath}. This allows the transfer of files from a previous calculation.

  • user_incar_settings (dict) – User INCAR settings. This allows a user to override INCAR settings, e.g., setting a different MAGMOM for various elements or species. Note that in the new scheme, ediff_per_atom and hubbard_u are no longer args. Instead, the CONFIG supports EDIFF_PER_ATOM and EDIFF keys. The former scales with # of atoms, the latter does not. If both are present, EDIFF is preferred. To force such settings, just supply user_incar_settings={“EDIFF”: 1e-5, “LDAU”: False} for example. The keys ‘LDAUU’, ‘LDAUJ’, ‘LDAUL’ are special cases since pymatgen defines different values depending on what anions are present in the structure, so these keys can be defined in one of two ways, e.g. either {“LDAUU”:{“O”:{“Fe”:5}}} to set LDAUU for Fe to 5 in an oxide, or {“LDAUU”:{“Fe”:5}} to set LDAUU to 5 regardless of the input structure. If a None value is given, that key is unset. For example, {“ENCUT”: None} will remove ENCUT from the incar settings. Finally, KSPACING is a special setting and can be set to “auto” in which the KSPACING is set automatically based on the band gap.

  • user_kpoints_settings (dict or Kpoints) – Allow user to override kpoints setting by supplying a dict. E.g., {“reciprocal_density”: 1000}. User can also supply Kpoints object.

  • user_potcar_settings (dict) – Allow user to override POTCARs. E.g., {“Gd”: “Gd_3”}. This is generally not recommended.

  • constrain_total_magmom (bool) – Whether to constrain the total magmom (NUPDOWN in INCAR) to be the sum of the expected MAGMOM for all species.

  • sort_structure (bool) – Whether to sort the structure (using the default sort order of electronegativity) before generating input files. Defaults to True, the behavior you would want most of the time. This ensures that similar atomic species are grouped together.

  • user_potcar_functional (str) – Functional to use. Default (None) is to use the functional in the config dictionary. Valid values: “PBE”, “PBE_52”, “PBE_54”, “LDA”, “LDA_52”, “LDA_54”, “PW91”, “LDA_US”, “PW91_US”.

  • force_gamma (bool) – Force gamma centered kpoint generation. Default (False) is to use the Automatic Density kpoint scheme, which will use the Gamma centered generation scheme for hexagonal cells, and Monkhorst-Pack otherwise.

  • reduce_structure (None/str) – Before generating the input files, generate the reduced structure. Default (None), does not alter the structure. Valid values: None, “niggli”, “LLL”.

  • vdw – Adds default parameters for van-der-Waals functionals supported by VASP to INCAR. Supported functionals are: DFT-D2, undamped DFT-D3, DFT-D3 with Becke-Jonson damping, Tkatchenko-Scheffler, Tkatchenko-Scheffler with iterative Hirshfeld partitioning, MBD@rSC, dDsC, Dion’s vdW-DF, DF2, optPBE, optB88, optB86b and rVV10.

  • use_structure_charge (bool) – If set to True, then the overall charge of the structure (structure.charge) is used to set the NELECT variable in the INCAR. Default is False.

  • standardize (float) – Whether to standardize to a primitive standard cell. Defaults to False.

  • sym_prec (float) – Tolerance for symmetry finding.

  • international_monoclinic (bool) – Whether to use international convention (vs Curtarolo) for monoclinic. Defaults True.

  • validate_magmom (bool) – Ensure that the missing magmom values are filled in with the VASP default value of 1.0.

  • inherit_incar (bool) – Whether to inherit INCAR settings from previous calculation. This might be useful to port Custodian fixes to child jobs but can also be dangerous e.g. when switching from GGA to meta-GGA or relax to static jobs. Defaults to True.

  • auto_ismear (bool) – If true, the values for ISMEAR and SIGMA will be set automatically depending on the bandgap of the system. If the bandgap is not known (e.g., there is no previous VASP directory) then ISMEAR=0 and SIGMA=0.2; if the bandgap is zero (a metallic system) then ISMEAR=2 and SIGMA=0.2; if the system is an insulator, then ISMEAR=-5 (tetrahedron smearing). Note, this only works when generating the input set from a previous VASP directory.

  • bandgap_tol (float) – Tolerance for determining if a system is metallic when KSPACING is set to “auto”. If the bandgap is less than this value, the system is considered metallic. Defaults to 1e-4 (eV).

  • bandgap (float) – Used for determining KSPACING if KSPACING == “auto” or ISMEAR if auto_ismear == True. Set automatically when using from_prev_calc.

  • prev_incar (str or dict) – Previous INCAR used for setting parent INCAR when inherit_incar == True. Set automatically when using from_prev_calc.

  • prev_kpoints (str or Kpoints) – Previous Kpoints. Set automatically when using from_prev_calc.

auto_ismear: bool = False[source]
bandgap: float | None = None[source]
bandgap_tol: float = 0.0001[source]
calculate_ng(max_prime_factor: int = 7, must_inc_2: bool = True, custom_encut: float | None = None, custom_prec: str | None = None) tuple[source]

Calculates the NGX, NGY, and NGZ values using the information available in the INCAR and POTCAR This is meant to help with making initial guess for the FFT grid so we can interact with the Charge density API.

Parameters:
  • max_prime_factor (int) – the valid prime factors of the grid size in each direction VASP has many different setting for this to handle many compiling options. For typical MPI options all prime factors up to 7 are allowed

  • must_inc_2 (bool) – Whether 2 must be a prime factor of the result. Defaults to True.

  • custom_encut (float | None) – Calculates the FFT grid parameters using a custom ENCUT that may be different from what is generated by the input set. Defaults to None. Do not use this unless you know what you are doing.

  • custom_prec (str | None) – Calculates the FFT grid parameters using a custom prec that may be different from what is generated by the input set. Defaults to None. Do not use this unless you know what you are doing.

config_dict: dict[source]
constrain_total_magmom: bool = False[source]
estimate_nbands() int[source]

Estimate the number of bands that VASP will initialize a calculation with by default. Note that in practice this can depend on # of cores (if not set explicitly). Note that this formula is slightly different than the formula on the VASP wiki (as of July 2023). This is because the formula in the source code (main.F) is slightly different than what is on the wiki.

files_to_transfer: dict[source]
force_gamma: bool = False[source]
classmethod from_prev_calc(prev_calc_dir, **kwargs)[source]

Generate a set of VASP input files for static calculations from a directory of previous VASP run.

Parameters:
  • prev_calc_dir (str) – Directory containing the outputs( vasprun.xml and OUTCAR) of previous vasp run.

  • **kwargs – All kwargs supported by MPStaticSet, other than prev_incar and prev_structure and prev_kpoints which are determined from the prev_calc_dir.

get_input_set(structure: Structure | None = None, prev_dir: str | Path | None = None, potcar_spec: bool = False) VaspInput[source]

Get a VASP input set.

Note, if both structure and prev_dir are set, then the structure specified will be preferred over the final structure from the last VASP run.

Parameters:
  • structure (Structure) – A structure.

  • prev_dir (str or Path) – A previous directory to generate the input set from.

  • potcar_spec (bool) – Instead of generating a Potcar object, use a list of potcar symbols. This will be written as a “POTCAR.spec” file. This is intended to help sharing an input set with people who might not have a license to specific Potcar files. Given a “POTCAR.spec”, the specific POTCAR file can be re-generated using pymatgen with the “generate_potcar” function in the pymatgen CLI.

Returns:

A VASP input object.

Return type:

VaspInput

property incar: Incar[source]

Get the INCAR.

property incar_updates: dict[source]

Get updates to the INCAR config for this calculation type.

inherit_incar: bool | list[str] = False[source]
international_monoclinic: bool = True[source]
property kpoints: Kpoints | None[source]

Get the kpoints file.

property kpoints_updates: dict | Kpoints[source]

Get updates to the kpoints configuration for this calculation type.

Note, these updates will be ignored if the user has set user_kpoint_settings.

Returns:

A dictionary of updates to apply to the KPOINTS config

or a Kpoints object.

Return type:

dict or Kpoints

property nelect: float[source]

Gets the default number of electrons for a given structure.

override_from_prev_calc(prev_calc_dir='.')[source]

Update the input set to include settings from a previous calculation.

Parameters:

prev_calc_dir (str) – The path to the previous calculation directory.

Returns:

The input set with the settings (structure, k-points, incar, etc) updated using the previous VASP run.

property poscar: Poscar[source]

Poscar

property potcar: Potcar[source]

Potcar object

property potcar_functional: Literal['PBE', 'PBE_52', 'PBE_54', 'LDA', 'LDA_52', 'LDA_54', 'PW91', 'LDA_US', 'PW91_US'] | None[source]

Returns the functional used for POTCAR generation.

prev_incar: str | dict | None = None[source]
prev_kpoints: str | Kpoints | None = None[source]
reduce_structure: Literal['niggli', 'LLL'] | None = None[source]
sort_structure: bool = True[source]
standardize: bool = False[source]
property structure: Structure[source]

Structure

sym_prec: float = 0.1[source]
use_structure_charge: bool = False[source]
user_incar_settings: dict[source]
user_kpoints_settings: dict[source]
user_potcar_functional: UserPotcarFunctional = None[source]
user_potcar_settings: dict[source]
validate_magmom: bool = True[source]
vdw: str | None = None[source]
write_input(output_dir: str, make_dir_if_not_present: bool = True, include_cif: bool = False, potcar_spec: bool = False, zip_output: bool = False)[source]

Writes out all input to a directory.

Parameters:
  • output_dir (str) – Directory to output the VASP input files

  • make_dir_if_not_present (bool) – Set to True if you want the directory (and the whole path) to be created if it is not present.

  • include_cif (bool) – Whether to write a CIF file in the output directory for easier opening by VESTA.

  • potcar_spec (bool) – Instead of writing the POTCAR, write a “POTCAR.spec”. This is intended to help sharing an input set with people who might not have a license to specific Potcar files. Given a “POTCAR.spec”, the specific POTCAR file can be re-generated using pymatgen with the “generate_potcar” function in the pymatgen CLI.

  • zip_output (bool) – Whether to zip each VASP input file written to the output directory.

class LobsterSet(structure: ~pymatgen.core.structure.Structure | None = <property object>, config_dict: dict = <factory>, files_to_transfer: dict = <factory>, user_incar_settings: dict = <factory>, user_kpoints_settings: dict = <factory>, user_potcar_settings: dict = <factory>, constrain_total_magmom: bool = False, sort_structure: bool = True, user_potcar_functional: ~typing.Literal['PBE', 'PBE_52', 'PBE_54', 'LDA', 'LDA_52', 'LDA_54', 'PW91', 'LDA_US', 'PW91_US'] | None = 'PBE_54', force_gamma: bool = False, reduce_structure: ~typing.Literal['niggli', 'LLL'] | None = None, vdw: str | None = None, use_structure_charge: bool = False, standardize: bool = False, sym_prec: float = 0.1, international_monoclinic: bool = True, validate_magmom: bool = True, inherit_incar: bool | list[str] = False, auto_ismear: bool = False, bandgap_tol: float = 0.0001, bandgap: float | None = None, prev_incar: str | dict | None = None, prev_kpoints: str | ~pymatgen.io.vasp.inputs.Kpoints | None = None, isym: int = 0, ismear: int = -5, reciprocal_density: int | None = None, address_basis_file: str | None = None, user_supplied_basis: dict | None = None)[source]

Bases: DictSet

Input set to prepare VASP runs that can be digested by Lobster (See cohp.de).

Parameters:
  • structure (Structure) – input structure.

  • isym (int) – ISYM entry for INCAR, only isym=-1 and isym=0 are allowed

  • ismear (int) – ISMEAR entry for INCAR, only ismear=-5 and ismear=0 are allowed

  • reciprocal_density (int) – density of k-mesh by reciprocal volume

  • user_supplied_basis (dict) – dict including basis functions for all elements in structure, e.g. {“Fe”: “3d 3p 4s”, “O”: “2s 2p”}; if not supplied, a standard basis is used

  • address_basis_file (str) – address to a file similar to “BASIS_PBE_54_standard.yaml” in pymatgen.io.lobster.lobster_basis

  • user_potcar_settings (dict) – dict including potcar settings for all elements in structure, e.g. {“Fe”: “Fe_pv”, “O”: “O”}; if not supplied, a standard basis is used.

  • **kwargs – Other kwargs supported by DictSet.

CONFIG = {'INCAR': {'ALGO': 'FAST', 'EDIFF_PER_ATOM': 5e-05, 'ENCUT': 520, 'IBRION': 2, 'ISIF': 3, 'ISMEAR': -5, 'ISPIN': 2, 'LASPH': True, 'LDAU': True, 'LDAUJ': {'F': {'Co': 0, 'Cr': 0, 'Fe': 0, 'Mn': 0, 'Mo': 0, 'Ni': 0, 'V': 0, 'W': 0}, 'O': {'Co': 0, 'Cr': 0, 'Fe': 0, 'Mn': 0, 'Mo': 0, 'Ni': 0, 'V': 0, 'W': 0}}, 'LDAUL': {'F': {'Co': 2, 'Cr': 2, 'Fe': 2, 'Mn': 2, 'Mo': 2, 'Ni': 2, 'V': 2, 'W': 2}, 'O': {'Co': 2, 'Cr': 2, 'Fe': 2, 'Mn': 2, 'Mo': 2, 'Ni': 2, 'V': 2, 'W': 2}}, 'LDAUPRINT': 1, 'LDAUTYPE': 2, 'LDAUU': {'F': {'Co': 3.32, 'Cr': 3.7, 'Fe': 5.3, 'Mn': 3.9, 'Mo': 4.38, 'Ni': 6.2, 'V': 3.25, 'W': 6.2}, 'O': {'Co': 3.32, 'Cr': 3.7, 'Fe': 5.3, 'Mn': 3.9, 'Mo': 4.38, 'Ni': 6.2, 'V': 3.25, 'W': 6.2}}, 'LORBIT': 11, 'LREAL': 'AUTO', 'LWAVE': False, 'MAGMOM': {'Ce': 5, 'Ce3+': 1, 'Co': 0.6, 'Co3+': 0.6, 'Co4+': 1, 'Cr': 5, 'Dy3+': 5, 'Er3+': 3, 'Eu': 10, 'Eu2+': 7, 'Eu3+': 6, 'Fe': 5, 'Gd3+': 7, 'Ho3+': 4, 'La3+': 0.6, 'Lu3+': 0.6, 'Mn': 5, 'Mn3+': 4, 'Mn4+': 3, 'Mo': 5, 'Nd3+': 3, 'Ni': 5, 'Pm3+': 4, 'Pr3+': 2, 'Sm3+': 5, 'Tb3+': 6, 'Tm3+': 2, 'V': 5, 'W': 5, 'Yb3+': 1}, 'NELM': 100, 'NSW': 99, 'PREC': 'Accurate', 'SIGMA': 0.05}, 'KPOINTS': {'reciprocal_density': 64}, 'PARENT': 'VASPIncarBase', 'POTCAR': {'Ac': 'Ac', 'Ag': 'Ag', 'Al': 'Al', 'Ar': 'Ar', 'As': 'As', 'Au': 'Au', 'B': 'B', 'Ba': 'Ba_sv', 'Be': 'Be_sv', 'Bi': 'Bi', 'Br': 'Br', 'C': 'C', 'Ca': 'Ca_sv', 'Cd': 'Cd', 'Ce': 'Ce', 'Cl': 'Cl', 'Co': 'Co', 'Cr': 'Cr_pv', 'Cs': 'Cs_sv', 'Cu': 'Cu_pv', 'Dy': 'Dy_3', 'Er': 'Er_3', 'Eu': 'Eu', 'F': 'F', 'Fe': 'Fe_pv', 'Ga': 'Ga_d', 'Gd': 'Gd', 'Ge': 'Ge_d', 'H': 'H', 'He': 'He', 'Hf': 'Hf_pv', 'Hg': 'Hg', 'Ho': 'Ho_3', 'I': 'I', 'In': 'In_d', 'Ir': 'Ir', 'K': 'K_sv', 'Kr': 'Kr', 'La': 'La', 'Li': 'Li_sv', 'Lu': 'Lu_3', 'Mg': 'Mg_pv', 'Mn': 'Mn_pv', 'Mo': 'Mo_pv', 'N': 'N', 'Na': 'Na_pv', 'Nb': 'Nb_pv', 'Nd': 'Nd_3', 'Ne': 'Ne', 'Ni': 'Ni_pv', 'Np': 'Np', 'O': 'O', 'Os': 'Os_pv', 'P': 'P', 'Pa': 'Pa', 'Pb': 'Pb_d', 'Pd': 'Pd', 'Pm': 'Pm_3', 'Pr': 'Pr_3', 'Pt': 'Pt', 'Pu': 'Pu', 'Rb': 'Rb_sv', 'Re': 'Re_pv', 'Rh': 'Rh_pv', 'Ru': 'Ru_pv', 'S': 'S', 'Sb': 'Sb', 'Sc': 'Sc_sv', 'Se': 'Se', 'Si': 'Si', 'Sm': 'Sm_3', 'Sn': 'Sn_d', 'Sr': 'Sr_sv', 'Ta': 'Ta_pv', 'Tb': 'Tb_3', 'Tc': 'Tc_pv', 'Te': 'Te', 'Th': 'Th', 'Ti': 'Ti_pv', 'Tl': 'Tl_d', 'Tm': 'Tm_3', 'U': 'U', 'V': 'V_pv', 'W': 'W_pv', 'Xe': 'Xe', 'Y': 'Y_sv', 'Yb': 'Yb_2', 'Zn': 'Zn', 'Zr': 'Zr_sv'}, 'POTCAR_FUNCTIONAL': 'PBE'}[source]
address_basis_file: str | None = None[source]
property incar_updates: dict[source]

Get updates to the INCAR config for this calculation type.

ismear: int = -5[source]
isym: int = 0[source]
property kpoints_updates: dict | Kpoints[source]

Get updates to the kpoints configuration for this calculation type.

reciprocal_density: int | None = None[source]
user_potcar_functional: UserPotcarFunctional = 'PBE_54'[source]
user_supplied_basis: dict | None = None[source]
class MITMDSet(structure: ~pymatgen.core.structure.Structure | None = None, config_dict: dict = <factory>, files_to_transfer: dict = <factory>, user_incar_settings: dict = <factory>, user_kpoints_settings: dict = <factory>, user_potcar_settings: dict = <factory>, constrain_total_magmom: bool = False, sort_structure: bool = True, user_potcar_functional: ~typing.Literal['PBE', 'PBE_52', 'PBE_54', 'LDA', 'LDA_52', 'LDA_54', 'PW91', 'LDA_US', 'PW91_US'] | None = None, force_gamma: bool = False, reduce_structure: ~typing.Literal['niggli', 'LLL'] | None = None, vdw: str | None = None, use_structure_charge: bool = False, standardize: bool = False, sym_prec: float = 0.1, international_monoclinic: bool = True, validate_magmom: bool = True, inherit_incar: bool | list[str] = False, auto_ismear: bool = False, bandgap_tol: float = 0.0001, bandgap: float | None = None, prev_incar: str | dict | None = None, prev_kpoints: str | ~pymatgen.io.vasp.inputs.Kpoints | None = None, start_temp: float = 0.0, end_temp: float = 300.0, nsteps: int = 1000, time_step: float = 2, spin_polarized: bool = False)[source]

Bases: DictSet

Class for writing a vasp md run. This DOES NOT do multiple stage runs.

Parameters:
  • structure (Structure) – Input structure.

  • start_temp (float) – Starting temperature.

  • end_temp (float) – Final temperature.

  • nsteps (int) – Number of time steps for simulations. NSW parameter.

  • time_step (float) – The time step for the simulation. The POTIM parameter. Defaults to 2fs.

  • spin_polarized (bool) – Whether to do spin polarized calculations. The ISPIN parameter. Defaults to False.

  • **kwargs – Other kwargs supported by DictSet.

CONFIG = {'INCAR': {'ALGO': 'FAST', 'EDIFF': 1e-05, 'ENCUT': 520, 'IBRION': 2, 'ICHARG': 1, 'ISIF': 3, 'ISMEAR': -5, 'ISPIN': 2, 'ISYM': 0, 'LDAU': True, 'LDAUJ': {'F': {'Ag': 0, 'Co': 0, 'Cr': 0, 'Cu': 0, 'Fe': 0, 'Mn': 0, 'Mo': 0, 'Nb': 0, 'Ni': 0, 'Re': 0, 'Ta': 0, 'V': 0, 'W': 0}, 'O': {'Ag': 0, 'Co': 0, 'Cr': 0, 'Cu': 0, 'Fe': 0, 'Mn': 0, 'Mo': 0, 'Nb': 0, 'Ni': 0, 'Re': 0, 'Ta': 0, 'V': 0, 'W': 0}, 'S': {'Fe': 0, 'Mn': 0}}, 'LDAUL': {'F': {'Ag': 2, 'Co': 2, 'Cr': 2, 'Cu': 2, 'Fe': 2, 'Mn': 2, 'Mo': 2, 'Nb': 2, 'Ni': 2, 'Re': 2, 'Ta': 2, 'V': 2, 'W': 2}, 'O': {'Ag': 2, 'Co': 2, 'Cr': 2, 'Cu': 2, 'Fe': 2, 'Mn': 2, 'Mo': 2, 'Nb': 2, 'Ni': 2, 'Re': 2, 'Ta': 2, 'V': 2, 'W': 2}, 'S': {'Fe': 2, 'Mn': 2.5}}, 'LDAUPRINT': 1, 'LDAUTYPE': 2, 'LDAUU': {'F': {'Ag': 1.5, 'Co': 3.4, 'Cr': 3.5, 'Cu': 4, 'Fe': 4.0, 'Mn': 3.9, 'Mo': 4.38, 'Nb': 1.5, 'Ni': 6, 'Re': 2, 'Ta': 2, 'V': 3.1, 'W': 4.0}, 'O': {'Ag': 1.5, 'Co': 3.4, 'Cr': 3.5, 'Cu': 4, 'Fe': 4.0, 'Mn': 3.9, 'Mo': 4.38, 'Nb': 1.5, 'Ni': 6, 'Re': 2, 'Ta': 2, 'V': 3.1, 'W': 4.0}, 'S': {'Fe': 1.9, 'Mn': 2.5}}, 'LORBIT': '11', 'LREAL': 'AUTO', 'LWAVE': False, 'MAGMOM': {'Ce': 5, 'Ce3+': 1, 'Co': 0.6, 'Co3+': 0.6, 'Co4+': 1, 'Cr': 5, 'Dy3+': 5, 'Er3+': 3, 'Eu': 10, 'Eu2+': 7, 'Eu3+': 6, 'Fe': 5, 'Gd3+': 7, 'Ho3+': 4, 'La3+': 0.6, 'Lu3+': 0.6, 'Mn': 5, 'Mn3+': 4, 'Mn4+': 3, 'Mo': 5, 'Nd3+': 3, 'Ni': 5, 'Pm3+': 4, 'Pr3+': 2, 'Sm3+': 5, 'Tb3+': 6, 'Tm3+': 2, 'V': 5, 'W': 5, 'Yb3+': 1}, 'NELM': 200, 'NELMIN': 6, 'NSW': 99, 'PREC': 'Accurate', 'SIGMA': 0.05}, 'KPOINTS': {'length': 25}, 'PARENT': 'VASPIncarBase', 'POTCAR': {'Ac': {'hash': 'd6854224d20e3de6e6fd7399503791d1', 'symbol': 'Ac'}, 'Ag': {'hash': 'e8ffa02fe3f3a51338ac1ac91ae968b9', 'symbol': 'Ag'}, 'Al': {'hash': 'a6fd9a46aec185f4ad2acd0cbe4ae2fa', 'symbol': 'Al'}, 'Ar': {'hash': 'e782fc6292623b396091bf8b871c272f', 'symbol': 'Ar'}, 'As': {'hash': '8005364db225a254e52cba350bedd032', 'symbol': 'As'}, 'Au': {'hash': 'a9182d436a13194b744640ac940ab9b0', 'symbol': 'Au'}, 'B': {'hash': '18ed2875dfa6305324cec3d7d59273ae', 'symbol': 'B'}, 'Ba': {'hash': 'c0477913afb63dfae3439f3534fbf0ed', 'symbol': 'Ba_sv'}, 'Be': {'hash': 'fb974e44d56a8c62c6bbd1a1eb70c3a7', 'symbol': 'Be'}, 'Bi': {'hash': 'e29661c79d59abae3b3ba69eae24b1a5', 'symbol': 'Bi'}, 'Br': {'hash': '40f9594b4506684a69158c8975cfb9d6', 'symbol': 'Br'}, 'C': {'hash': 'c0a8167dbb174fe492a3db7f5006c0f8', 'symbol': 'C'}, 'Ca': {'hash': 'eb006721e214c04b3c13146e81b3a27d', 'symbol': 'Ca_sv'}, 'Cd': {'hash': '0506b2d0ac28d5fe2b5ced77a701aa86', 'symbol': 'Cd'}, 'Ce': {'hash': 'ff3a09f2ff91798e58eb4b9854e9be4a', 'symbol': 'Ce'}, 'Cl': {'hash': '779b9901046c78fe51c5d80224642aeb', 'symbol': 'Cl'}, 'Co': {'hash': 'b169bca4e137294d2ab3df8cbdd09083', 'symbol': 'Co'}, 'Cr': {'hash': '82c14307937c7509fda4e9bc023d243d', 'symbol': 'Cr'}, 'Cs': {'hash': '096b53a7d80cc0086976bcda50d536e5', 'symbol': 'Cs_sv'}, 'Cu': {'hash': '8ca4e43a30de0c397e51f16bbb20d678', 'symbol': 'Cu'}, 'Dy': {'hash': 'd4a05220ab0a2d4c03a76872ea724a1e', 'symbol': 'Dy_3'}, 'Er': {'hash': 'daa65a04877317f8c3c593ddeaa8a132', 'symbol': 'Er_3'}, 'Eu': {'hash': 'd466d046adf21f6146ee9644049ea268', 'symbol': 'Eu'}, 'F': {'hash': '180141c33d032bfbfff30b3bea9d23dd', 'symbol': 'F'}, 'Fe': {'hash': '9530da8244e4dac17580869b4adab115', 'symbol': 'Fe'}, 'Ga': {'hash': '6e0b9d58412b1bfcd7252aff13d476c2', 'symbol': 'Ga'}, 'Gd': {'hash': '1f0d42b1e5f6769d319d3f247992aeb9', 'symbol': 'Gd'}, 'Ge': {'hash': '79e788788c31e196a460553010512d3f', 'symbol': 'Ge'}, 'H': {'hash': 'bb43c666e3d36577264afe07669e9582', 'symbol': 'H'}, 'He': {'hash': '47f9434aa3db96c85d7c4b3e4c2df09b', 'symbol': 'He'}, 'Hf': {'hash': 'b113f150cbf9c736f8244a6c25b0482e', 'symbol': 'Hf'}, 'Hg': {'hash': 'c2f15dfb5fd53396c5427635e5019160', 'symbol': 'Hg'}, 'Ho': {'hash': '661891464a27e87cf7e1324dd1893b77', 'symbol': 'Ho_3'}, 'I': {'hash': 'f4ff16a495dd361ff5824ee61b418bb0', 'symbol': 'I'}, 'In': {'hash': '7df38c0cdb4e6d9a9b93f09d690bb3ae', 'symbol': 'In'}, 'Ir': {'hash': 'dbcf7dcc6f4fb40df7b3d26904f60a66', 'symbol': 'Ir'}, 'K': {'hash': '3e84f86d37f203a4fb01de36af57e430', 'symbol': 'K_sv'}, 'Kr': {'hash': '39b9b85ae3982e6c012fb549b2840ce5', 'symbol': 'Kr'}, 'La': {'hash': '9b3ce03d18f7c0b40471a817ff91b287', 'symbol': 'La'}, 'Li': {'hash': '65e83282d1707ec078c1012afbd05be8', 'symbol': 'Li'}, 'Lu': {'hash': 'd40a90babf1224b88ffb4c3273ac3848', 'symbol': 'Lu_3'}, 'Mg': {'hash': '1771eb72adbbfa6310d66e7517e49930', 'symbol': 'Mg'}, 'Mn': {'hash': 'd082dba29b57ab59b3165e605dbf71b8', 'symbol': 'Mn'}, 'Mo': {'hash': '84e18fd84a98e3d7fa8f055952410df0', 'symbol': 'Mo_pv'}, 'N': {'hash': 'b98fd027ddebc67da4063ff2cabbc04b', 'symbol': 'N'}, 'Na': {'hash': '1a89e79f7e21d99e8cf5788979f6a987', 'symbol': 'Na'}, 'Nb': {'hash': '7bcee99a4dc3094be0f9fd7961c02966', 'symbol': 'Nb_pv'}, 'Nd': {'hash': '0c64e63070cee837c967283fffa001df', 'symbol': 'Nd'}, 'Ne': {'hash': '52064eee378b9e37a295a674f1c278f0', 'symbol': 'Ne'}, 'Ni': {'hash': '653f5772e68b2c7fd87ffd1086c0d710', 'symbol': 'Ni'}, 'Np': {'hash': '20cb30b714200c4db870550b288ac4cd', 'symbol': 'Np'}, 'O': {'hash': '7a25bc5b9a5393f46600a4939d357982', 'symbol': 'O'}, 'Os': {'hash': '35c2cb48d48a9c38c40fb82bbe70626d', 'symbol': 'Os'}, 'P': {'hash': '7dc3393307131ae67785a0cdacb61d5f', 'symbol': 'P'}, 'Pa': {'hash': 'a1fdb1089d0727f415416ec8082246ba', 'symbol': 'Pa'}, 'Pb': {'hash': '704c2c967247d7f84090d2536c91877d', 'symbol': 'Pb'}, 'Pd': {'hash': 'a395eb3aaf2fcab12fac3030a1146f61', 'symbol': 'Pd'}, 'Pm': {'hash': 'a2c9485ea86b2a7cf175077e6e5c7b3e', 'symbol': 'Pm'}, 'Pr': {'hash': '92f191499bf5346ea652bb806350ad87', 'symbol': 'Pr'}, 'Pt': {'hash': 'a604ea3c6a9cc23c739b762f625cf449', 'symbol': 'Pt'}, 'Pu': {'hash': 'f1d01e845dccc52d448679911f301a73', 'symbol': 'Pu'}, 'Rb': {'hash': 'e447c648d870b066b3514e6b800727ab', 'symbol': 'Rb_pv'}, 'Re': {'hash': '72385e193c92a8acfe17ea49004c2be1', 'symbol': 'Re'}, 'Rh': {'hash': '2c3dba3fcc6058ca1b1cfa75e45084bc', 'symbol': 'Rh'}, 'Ru': {'hash': '7925f4d4b68076d70af7cd86eef9ba8d', 'symbol': 'Ru_pv'}, 'S': {'hash': 'd368db6899d8839859bbee4811a42a88', 'symbol': 'S'}, 'Sb': {'hash': 'd82c022b02fc5344e85bd1909f9ee3e7', 'symbol': 'Sb'}, 'Sc': {'hash': 'dc386f505ad0c43385a7715b4111cb75', 'symbol': 'Sc_sv'}, 'Se': {'hash': '67a8804ede9f1112726e3d136978ef19', 'symbol': 'Se'}, 'Si': {'hash': 'b2b0ea6feb62e7cde209616683b8f7f5', 'symbol': 'Si'}, 'Sm': {'hash': 'e5e274e7cd99602ca81d146155abdf88', 'symbol': 'Sm_3'}, 'Sn': {'hash': '849b0795e148f93113a06be8fd5f5001', 'symbol': 'Sn_d'}, 'Sr': {'hash': 'ca6a5429c120a0ab705824386a76fe5b', 'symbol': 'Sr_sv'}, 'Ta': {'hash': 'd4e2cfe9338ef80da592d5bb9dc782c7', 'symbol': 'Ta'}, 'Tb': {'hash': '0790955c547003956c0fd4f080f7f508', 'symbol': 'Tb_3'}, 'Tc': {'hash': '9592642886319309a39d55c5717c6f48', 'symbol': 'Tc'}, 'Te': {'hash': '72719856e22fb1d3032df6f96d98a0f2', 'symbol': 'Te'}, 'Th': {'hash': 'aea79f322180fa6f0bfa74cb2a156dcf', 'symbol': 'Th'}, 'Ti': {'hash': 'c617e8b539c3f44a0ab6e8da2a92d318', 'symbol': 'Ti'}, 'Tl': {'hash': '2aa0d5406aaab7ebfbc761da382f1352', 'symbol': 'Tl'}, 'Tm': {'hash': '94a07cb7949b01305cb161da0cbfb492', 'symbol': 'Tm_3'}, 'U': {'hash': '72702eabbb1bc02b4167590dc848ed5d', 'symbol': 'U'}, 'V': {'hash': '7f1297a2e1d963e2a4d81b61f85e4ded', 'symbol': 'V_pv'}, 'W': {'hash': '2a33e0d5c700640535f60ac0a12177ab', 'symbol': 'W_pv'}, 'Xe': {'hash': '338472e581f58b41d37c002a5e22353b', 'symbol': 'Xe'}, 'Y': {'hash': '4ed187e77cd54f198bb88020278b143d', 'symbol': 'Y_sv'}, 'Yb': {'hash': '9f472bd422f640710f7d93e2d9ce89f4', 'symbol': 'Yb'}, 'Zn': {'hash': 'e35ee27f8483a63bb68dbc236a343af3', 'symbol': 'Zn'}, 'Zr': {'hash': 'd221d2c0bac4f8e81af2f5c42a314274', 'symbol': 'Zr'}}, 'POTCAR_FUNCTIONAL': 'PBE'}[source]
end_temp: float = 300.0[source]
property incar_updates[source]

Get updates to the INCAR config for this calculation type.

property kpoints_updates: Kpoints | dict[source]

Get updates to the kpoints configuration for this calculation type.

nsteps: int = 1000[source]
spin_polarized: bool = False[source]
start_temp: float = 0.0[source]
structure: Structure | None = None[source]
time_step: float = 2[source]
class MITNEBSet(structures, unset_encut=False, **kwargs)[source]

Bases: DictSet

Class for writing NEB inputs.

Note that EDIFF is not on a per atom basis for this input set.

Parameters:
  • structures – List of Structure objects.

  • unset_encut (bool) – Whether to unset ENCUT.

  • **kwargs – Other kwargs supported by DictSet.

property poscar[source]

Poscar for structure of first end point.

property poscars[source]

List of Poscars.

write_input(output_dir, make_dir_if_not_present=True, write_cif=False, write_path_cif=False, write_endpoint_inputs=False)[source]

NEB inputs has a special directory structure where inputs are in 00, 01, 02, ….

Parameters:
  • output_dir (str) – Directory to output the VASP input files

  • make_dir_if_not_present (bool) – Set to True if you want the directory (and the whole path) to be created if it is not present.

  • write_cif (bool) – If true, writes a cif along with each POSCAR.

  • write_path_cif (bool) – If true, writes a cif for each image.

  • write_endpoint_inputs (bool) – If true, writes input files for running endpoint calculations.

class MITRelaxSet(structure: ~pymatgen.core.structure.Structure | None = <property object>, config_dict: dict = <factory>, files_to_transfer: dict = <factory>, user_incar_settings: dict = <factory>, user_kpoints_settings: dict = <factory>, user_potcar_settings: dict = <factory>, constrain_total_magmom: bool = False, sort_structure: bool = True, user_potcar_functional: ~typing.Literal['PBE', 'PBE_52', 'PBE_54', 'LDA', 'LDA_52', 'LDA_54', 'PW91', 'LDA_US', 'PW91_US'] | None = None, force_gamma: bool = False, reduce_structure: ~typing.Literal['niggli', 'LLL'] | None = None, vdw: str | None = None, use_structure_charge: bool = False, standardize: bool = False, sym_prec: float = 0.1, international_monoclinic: bool = True, validate_magmom: bool = True, inherit_incar: bool | list[str] = False, auto_ismear: bool = False, bandgap_tol: float = 0.0001, bandgap: float | None = None, prev_incar: str | dict | None = None, prev_kpoints: str | ~pymatgen.io.vasp.inputs.Kpoints | None = None)[source]

Bases: DictSet

Standard implementation of VaspInputSet utilizing parameters in the MIT High-throughput project. The parameters are chosen specifically for a high-throughput project, which means in general pseudopotentials with fewer electrons were chosen.

Parameters:
  • structure (Structure) – The Structure to create inputs for. If None, the input set is initialized without a Structure but one must be set separately before the inputs are generated.

  • **kwargs – Same as those supported by DictSet.

Please refer:

A Jain, G. Hautier, C. Moore, S. P. Ong, C. Fischer, T. Mueller,
K. A. Persson, G. Ceder. A high-throughput infrastructure for density
functional theory calculations. Computational Materials Science,
2011, 50(8), 2295-2310. doi:10.1016/j.commatsci.2011.02.023
CONFIG = {'INCAR': {'ALGO': 'FAST', 'EDIFF': 1e-05, 'ENCUT': 520, 'IBRION': 2, 'ICHARG': 1, 'ISIF': 3, 'ISMEAR': -5, 'ISPIN': 2, 'ISYM': 0, 'LDAU': True, 'LDAUJ': {'F': {'Ag': 0, 'Co': 0, 'Cr': 0, 'Cu': 0, 'Fe': 0, 'Mn': 0, 'Mo': 0, 'Nb': 0, 'Ni': 0, 'Re': 0, 'Ta': 0, 'V': 0, 'W': 0}, 'O': {'Ag': 0, 'Co': 0, 'Cr': 0, 'Cu': 0, 'Fe': 0, 'Mn': 0, 'Mo': 0, 'Nb': 0, 'Ni': 0, 'Re': 0, 'Ta': 0, 'V': 0, 'W': 0}, 'S': {'Fe': 0, 'Mn': 0}}, 'LDAUL': {'F': {'Ag': 2, 'Co': 2, 'Cr': 2, 'Cu': 2, 'Fe': 2, 'Mn': 2, 'Mo': 2, 'Nb': 2, 'Ni': 2, 'Re': 2, 'Ta': 2, 'V': 2, 'W': 2}, 'O': {'Ag': 2, 'Co': 2, 'Cr': 2, 'Cu': 2, 'Fe': 2, 'Mn': 2, 'Mo': 2, 'Nb': 2, 'Ni': 2, 'Re': 2, 'Ta': 2, 'V': 2, 'W': 2}, 'S': {'Fe': 2, 'Mn': 2.5}}, 'LDAUPRINT': 1, 'LDAUTYPE': 2, 'LDAUU': {'F': {'Ag': 1.5, 'Co': 3.4, 'Cr': 3.5, 'Cu': 4, 'Fe': 4.0, 'Mn': 3.9, 'Mo': 4.38, 'Nb': 1.5, 'Ni': 6, 'Re': 2, 'Ta': 2, 'V': 3.1, 'W': 4.0}, 'O': {'Ag': 1.5, 'Co': 3.4, 'Cr': 3.5, 'Cu': 4, 'Fe': 4.0, 'Mn': 3.9, 'Mo': 4.38, 'Nb': 1.5, 'Ni': 6, 'Re': 2, 'Ta': 2, 'V': 3.1, 'W': 4.0}, 'S': {'Fe': 1.9, 'Mn': 2.5}}, 'LORBIT': '11', 'LREAL': 'AUTO', 'LWAVE': False, 'MAGMOM': {'Ce': 5, 'Ce3+': 1, 'Co': 0.6, 'Co3+': 0.6, 'Co4+': 1, 'Cr': 5, 'Dy3+': 5, 'Er3+': 3, 'Eu': 10, 'Eu2+': 7, 'Eu3+': 6, 'Fe': 5, 'Gd3+': 7, 'Ho3+': 4, 'La3+': 0.6, 'Lu3+': 0.6, 'Mn': 5, 'Mn3+': 4, 'Mn4+': 3, 'Mo': 5, 'Nd3+': 3, 'Ni': 5, 'Pm3+': 4, 'Pr3+': 2, 'Sm3+': 5, 'Tb3+': 6, 'Tm3+': 2, 'V': 5, 'W': 5, 'Yb3+': 1}, 'NELM': 200, 'NELMIN': 6, 'NSW': 99, 'PREC': 'Accurate', 'SIGMA': 0.05}, 'KPOINTS': {'length': 25}, 'PARENT': 'VASPIncarBase', 'POTCAR': {'Ac': {'hash': 'd6854224d20e3de6e6fd7399503791d1', 'symbol': 'Ac'}, 'Ag': {'hash': 'e8ffa02fe3f3a51338ac1ac91ae968b9', 'symbol': 'Ag'}, 'Al': {'hash': 'a6fd9a46aec185f4ad2acd0cbe4ae2fa', 'symbol': 'Al'}, 'Ar': {'hash': 'e782fc6292623b396091bf8b871c272f', 'symbol': 'Ar'}, 'As': {'hash': '8005364db225a254e52cba350bedd032', 'symbol': 'As'}, 'Au': {'hash': 'a9182d436a13194b744640ac940ab9b0', 'symbol': 'Au'}, 'B': {'hash': '18ed2875dfa6305324cec3d7d59273ae', 'symbol': 'B'}, 'Ba': {'hash': 'c0477913afb63dfae3439f3534fbf0ed', 'symbol': 'Ba_sv'}, 'Be': {'hash': 'fb974e44d56a8c62c6bbd1a1eb70c3a7', 'symbol': 'Be'}, 'Bi': {'hash': 'e29661c79d59abae3b3ba69eae24b1a5', 'symbol': 'Bi'}, 'Br': {'hash': '40f9594b4506684a69158c8975cfb9d6', 'symbol': 'Br'}, 'C': {'hash': 'c0a8167dbb174fe492a3db7f5006c0f8', 'symbol': 'C'}, 'Ca': {'hash': 'eb006721e214c04b3c13146e81b3a27d', 'symbol': 'Ca_sv'}, 'Cd': {'hash': '0506b2d0ac28d5fe2b5ced77a701aa86', 'symbol': 'Cd'}, 'Ce': {'hash': 'ff3a09f2ff91798e58eb4b9854e9be4a', 'symbol': 'Ce'}, 'Cl': {'hash': '779b9901046c78fe51c5d80224642aeb', 'symbol': 'Cl'}, 'Co': {'hash': 'b169bca4e137294d2ab3df8cbdd09083', 'symbol': 'Co'}, 'Cr': {'hash': '82c14307937c7509fda4e9bc023d243d', 'symbol': 'Cr'}, 'Cs': {'hash': '096b53a7d80cc0086976bcda50d536e5', 'symbol': 'Cs_sv'}, 'Cu': {'hash': '8ca4e43a30de0c397e51f16bbb20d678', 'symbol': 'Cu'}, 'Dy': {'hash': 'd4a05220ab0a2d4c03a76872ea724a1e', 'symbol': 'Dy_3'}, 'Er': {'hash': 'daa65a04877317f8c3c593ddeaa8a132', 'symbol': 'Er_3'}, 'Eu': {'hash': 'd466d046adf21f6146ee9644049ea268', 'symbol': 'Eu'}, 'F': {'hash': '180141c33d032bfbfff30b3bea9d23dd', 'symbol': 'F'}, 'Fe': {'hash': '9530da8244e4dac17580869b4adab115', 'symbol': 'Fe'}, 'Ga': {'hash': '6e0b9d58412b1bfcd7252aff13d476c2', 'symbol': 'Ga'}, 'Gd': {'hash': '1f0d42b1e5f6769d319d3f247992aeb9', 'symbol': 'Gd'}, 'Ge': {'hash': '79e788788c31e196a460553010512d3f', 'symbol': 'Ge'}, 'H': {'hash': 'bb43c666e3d36577264afe07669e9582', 'symbol': 'H'}, 'He': {'hash': '47f9434aa3db96c85d7c4b3e4c2df09b', 'symbol': 'He'}, 'Hf': {'hash': 'b113f150cbf9c736f8244a6c25b0482e', 'symbol': 'Hf'}, 'Hg': {'hash': 'c2f15dfb5fd53396c5427635e5019160', 'symbol': 'Hg'}, 'Ho': {'hash': '661891464a27e87cf7e1324dd1893b77', 'symbol': 'Ho_3'}, 'I': {'hash': 'f4ff16a495dd361ff5824ee61b418bb0', 'symbol': 'I'}, 'In': {'hash': '7df38c0cdb4e6d9a9b93f09d690bb3ae', 'symbol': 'In'}, 'Ir': {'hash': 'dbcf7dcc6f4fb40df7b3d26904f60a66', 'symbol': 'Ir'}, 'K': {'hash': '3e84f86d37f203a4fb01de36af57e430', 'symbol': 'K_sv'}, 'Kr': {'hash': '39b9b85ae3982e6c012fb549b2840ce5', 'symbol': 'Kr'}, 'La': {'hash': '9b3ce03d18f7c0b40471a817ff91b287', 'symbol': 'La'}, 'Li': {'hash': '65e83282d1707ec078c1012afbd05be8', 'symbol': 'Li'}, 'Lu': {'hash': 'd40a90babf1224b88ffb4c3273ac3848', 'symbol': 'Lu_3'}, 'Mg': {'hash': '1771eb72adbbfa6310d66e7517e49930', 'symbol': 'Mg'}, 'Mn': {'hash': 'd082dba29b57ab59b3165e605dbf71b8', 'symbol': 'Mn'}, 'Mo': {'hash': '84e18fd84a98e3d7fa8f055952410df0', 'symbol': 'Mo_pv'}, 'N': {'hash': 'b98fd027ddebc67da4063ff2cabbc04b', 'symbol': 'N'}, 'Na': {'hash': '1a89e79f7e21d99e8cf5788979f6a987', 'symbol': 'Na'}, 'Nb': {'hash': '7bcee99a4dc3094be0f9fd7961c02966', 'symbol': 'Nb_pv'}, 'Nd': {'hash': '0c64e63070cee837c967283fffa001df', 'symbol': 'Nd'}, 'Ne': {'hash': '52064eee378b9e37a295a674f1c278f0', 'symbol': 'Ne'}, 'Ni': {'hash': '653f5772e68b2c7fd87ffd1086c0d710', 'symbol': 'Ni'}, 'Np': {'hash': '20cb30b714200c4db870550b288ac4cd', 'symbol': 'Np'}, 'O': {'hash': '7a25bc5b9a5393f46600a4939d357982', 'symbol': 'O'}, 'Os': {'hash': '35c2cb48d48a9c38c40fb82bbe70626d', 'symbol': 'Os'}, 'P': {'hash': '7dc3393307131ae67785a0cdacb61d5f', 'symbol': 'P'}, 'Pa': {'hash': 'a1fdb1089d0727f415416ec8082246ba', 'symbol': 'Pa'}, 'Pb': {'hash': '704c2c967247d7f84090d2536c91877d', 'symbol': 'Pb'}, 'Pd': {'hash': 'a395eb3aaf2fcab12fac3030a1146f61', 'symbol': 'Pd'}, 'Pm': {'hash': 'a2c9485ea86b2a7cf175077e6e5c7b3e', 'symbol': 'Pm'}, 'Pr': {'hash': '92f191499bf5346ea652bb806350ad87', 'symbol': 'Pr'}, 'Pt': {'hash': 'a604ea3c6a9cc23c739b762f625cf449', 'symbol': 'Pt'}, 'Pu': {'hash': 'f1d01e845dccc52d448679911f301a73', 'symbol': 'Pu'}, 'Rb': {'hash': 'e447c648d870b066b3514e6b800727ab', 'symbol': 'Rb_pv'}, 'Re': {'hash': '72385e193c92a8acfe17ea49004c2be1', 'symbol': 'Re'}, 'Rh': {'hash': '2c3dba3fcc6058ca1b1cfa75e45084bc', 'symbol': 'Rh'}, 'Ru': {'hash': '7925f4d4b68076d70af7cd86eef9ba8d', 'symbol': 'Ru_pv'}, 'S': {'hash': 'd368db6899d8839859bbee4811a42a88', 'symbol': 'S'}, 'Sb': {'hash': 'd82c022b02fc5344e85bd1909f9ee3e7', 'symbol': 'Sb'}, 'Sc': {'hash': 'dc386f505ad0c43385a7715b4111cb75', 'symbol': 'Sc_sv'}, 'Se': {'hash': '67a8804ede9f1112726e3d136978ef19', 'symbol': 'Se'}, 'Si': {'hash': 'b2b0ea6feb62e7cde209616683b8f7f5', 'symbol': 'Si'}, 'Sm': {'hash': 'e5e274e7cd99602ca81d146155abdf88', 'symbol': 'Sm_3'}, 'Sn': {'hash': '849b0795e148f93113a06be8fd5f5001', 'symbol': 'Sn_d'}, 'Sr': {'hash': 'ca6a5429c120a0ab705824386a76fe5b', 'symbol': 'Sr_sv'}, 'Ta': {'hash': 'd4e2cfe9338ef80da592d5bb9dc782c7', 'symbol': 'Ta'}, 'Tb': {'hash': '0790955c547003956c0fd4f080f7f508', 'symbol': 'Tb_3'}, 'Tc': {'hash': '9592642886319309a39d55c5717c6f48', 'symbol': 'Tc'}, 'Te': {'hash': '72719856e22fb1d3032df6f96d98a0f2', 'symbol': 'Te'}, 'Th': {'hash': 'aea79f322180fa6f0bfa74cb2a156dcf', 'symbol': 'Th'}, 'Ti': {'hash': 'c617e8b539c3f44a0ab6e8da2a92d318', 'symbol': 'Ti'}, 'Tl': {'hash': '2aa0d5406aaab7ebfbc761da382f1352', 'symbol': 'Tl'}, 'Tm': {'hash': '94a07cb7949b01305cb161da0cbfb492', 'symbol': 'Tm_3'}, 'U': {'hash': '72702eabbb1bc02b4167590dc848ed5d', 'symbol': 'U'}, 'V': {'hash': '7f1297a2e1d963e2a4d81b61f85e4ded', 'symbol': 'V_pv'}, 'W': {'hash': '2a33e0d5c700640535f60ac0a12177ab', 'symbol': 'W_pv'}, 'Xe': {'hash': '338472e581f58b41d37c002a5e22353b', 'symbol': 'Xe'}, 'Y': {'hash': '4ed187e77cd54f198bb88020278b143d', 'symbol': 'Y_sv'}, 'Yb': {'hash': '9f472bd422f640710f7d93e2d9ce89f4', 'symbol': 'Yb'}, 'Zn': {'hash': 'e35ee27f8483a63bb68dbc236a343af3', 'symbol': 'Zn'}, 'Zr': {'hash': 'd221d2c0bac4f8e81af2f5c42a314274', 'symbol': 'Zr'}}, 'POTCAR_FUNCTIONAL': 'PBE'}[source]
class MPAbsorptionSet(structure: ~pymatgen.core.structure.Structure | None = <property object>, config_dict: dict = <factory>, files_to_transfer: dict = <factory>, user_incar_settings: dict = <factory>, user_kpoints_settings: dict = <factory>, user_potcar_settings: dict = <factory>, constrain_total_magmom: bool = False, sort_structure: bool = True, user_potcar_functional: ~typing.Literal['PBE', 'PBE_52', 'PBE_54', 'LDA', 'LDA_52', 'LDA_54', 'PW91', 'LDA_US', 'PW91_US'] | None = None, force_gamma: bool = True, reduce_structure: ~typing.Literal['niggli', 'LLL'] | None = None, vdw: str | None = None, use_structure_charge: bool = False, standardize: bool = False, sym_prec: float = 0.1, international_monoclinic: bool = True, validate_magmom: bool = True, inherit_incar: bool = True, auto_ismear: bool = False, bandgap_tol: float = 0.0001, bandgap: float | None = None, prev_incar: str | dict | None = None, prev_kpoints: str | ~pymatgen.io.vasp.inputs.Kpoints | None = None, mode: str = 'IPA', copy_wavecar: bool = True, nbands_factor: float = 2, reciprocal_density: float = 400, nkred: tuple[int, int, int] | None = None, nedos: int = 2001, nbands: int | None = None)[source]

Bases: DictSet

MP input set for generating frequency dependent dielectrics.

Two modes are supported: “IPA” or “RPA”. A typical sequence is mode=”STATIC” -> mode=”IPA” -> mode=”RPA”(optional) For all steps other than the first one (static), the recommendation is to use from_prev_calculation on the preceding run in the series. It is important to ensure Gamma centred kpoints for the RPA step.

Parameters:
  • structure (Structure) – Input structure.

  • mode (str) – Supported modes are “IPA”, “RPA”

  • copy_wavecar (bool) – Whether to copy the WAVECAR from a previous run. Defaults to True.

  • nbands (int) – For subsequent calculations, it is generally recommended to perform NBANDS convergence starting from the NBANDS of the previous run for DIAG, and to use the exact same NBANDS for RPA. This parameter is used by from_previous_calculation to set nband.

  • nbands_factor (int) – Multiplicative factor for NBANDS when starting from a previous calculation. Only applies if mode==”IPA”. Need to be tested for convergence.

  • reciprocal_density – the k-points density

  • nkred – the reduced number of kpoints to calculate, equal to the k-mesh. Only applies in “RPA” mode because of the q->0 limit.

  • nedos – the density of DOS, default: 2001.

  • **kwargs – All kwargs supported by DictSet. Typically, user_incar_settings is a commonly used option.

CONFIG = {'INCAR': {'ALGO': 'FAST', 'EDIFF_PER_ATOM': 5e-05, 'ENCUT': 520, 'IBRION': 2, 'ISIF': 3, 'ISMEAR': -5, 'ISPIN': 2, 'LASPH': True, 'LDAU': True, 'LDAUJ': {'F': {'Co': 0, 'Cr': 0, 'Fe': 0, 'Mn': 0, 'Mo': 0, 'Ni': 0, 'V': 0, 'W': 0}, 'O': {'Co': 0, 'Cr': 0, 'Fe': 0, 'Mn': 0, 'Mo': 0, 'Ni': 0, 'V': 0, 'W': 0}}, 'LDAUL': {'F': {'Co': 2, 'Cr': 2, 'Fe': 2, 'Mn': 2, 'Mo': 2, 'Ni': 2, 'V': 2, 'W': 2}, 'O': {'Co': 2, 'Cr': 2, 'Fe': 2, 'Mn': 2, 'Mo': 2, 'Ni': 2, 'V': 2, 'W': 2}}, 'LDAUPRINT': 1, 'LDAUTYPE': 2, 'LDAUU': {'F': {'Co': 3.32, 'Cr': 3.7, 'Fe': 5.3, 'Mn': 3.9, 'Mo': 4.38, 'Ni': 6.2, 'V': 3.25, 'W': 6.2}, 'O': {'Co': 3.32, 'Cr': 3.7, 'Fe': 5.3, 'Mn': 3.9, 'Mo': 4.38, 'Ni': 6.2, 'V': 3.25, 'W': 6.2}}, 'LORBIT': 11, 'LREAL': 'AUTO', 'LWAVE': False, 'MAGMOM': {'Ce': 5, 'Ce3+': 1, 'Co': 0.6, 'Co3+': 0.6, 'Co4+': 1, 'Cr': 5, 'Dy3+': 5, 'Er3+': 3, 'Eu': 10, 'Eu2+': 7, 'Eu3+': 6, 'Fe': 5, 'Gd3+': 7, 'Ho3+': 4, 'La3+': 0.6, 'Lu3+': 0.6, 'Mn': 5, 'Mn3+': 4, 'Mn4+': 3, 'Mo': 5, 'Nd3+': 3, 'Ni': 5, 'Pm3+': 4, 'Pr3+': 2, 'Sm3+': 5, 'Tb3+': 6, 'Tm3+': 2, 'V': 5, 'W': 5, 'Yb3+': 1}, 'NELM': 100, 'NSW': 99, 'PREC': 'Accurate', 'SIGMA': 0.05}, 'KPOINTS': {'reciprocal_density': 64}, 'PARENT': 'VASPIncarBase', 'POTCAR': {'Ac': 'Ac', 'Ag': 'Ag', 'Al': 'Al', 'Ar': 'Ar', 'As': 'As', 'Au': 'Au', 'B': 'B', 'Ba': 'Ba_sv', 'Be': 'Be_sv', 'Bi': 'Bi', 'Br': 'Br', 'C': 'C', 'Ca': 'Ca_sv', 'Cd': 'Cd', 'Ce': 'Ce', 'Cl': 'Cl', 'Co': 'Co', 'Cr': 'Cr_pv', 'Cs': 'Cs_sv', 'Cu': 'Cu_pv', 'Dy': 'Dy_3', 'Er': 'Er_3', 'Eu': 'Eu', 'F': 'F', 'Fe': 'Fe_pv', 'Ga': 'Ga_d', 'Gd': 'Gd', 'Ge': 'Ge_d', 'H': 'H', 'He': 'He', 'Hf': 'Hf_pv', 'Hg': 'Hg', 'Ho': 'Ho_3', 'I': 'I', 'In': 'In_d', 'Ir': 'Ir', 'K': 'K_sv', 'Kr': 'Kr', 'La': 'La', 'Li': 'Li_sv', 'Lu': 'Lu_3', 'Mg': 'Mg_pv', 'Mn': 'Mn_pv', 'Mo': 'Mo_pv', 'N': 'N', 'Na': 'Na_pv', 'Nb': 'Nb_pv', 'Nd': 'Nd_3', 'Ne': 'Ne', 'Ni': 'Ni_pv', 'Np': 'Np', 'O': 'O', 'Os': 'Os_pv', 'P': 'P', 'Pa': 'Pa', 'Pb': 'Pb_d', 'Pd': 'Pd', 'Pm': 'Pm_3', 'Pr': 'Pr_3', 'Pt': 'Pt', 'Pu': 'Pu', 'Rb': 'Rb_sv', 'Re': 'Re_pv', 'Rh': 'Rh_pv', 'Ru': 'Ru_pv', 'S': 'S', 'Sb': 'Sb', 'Sc': 'Sc_sv', 'Se': 'Se', 'Si': 'Si', 'Sm': 'Sm_3', 'Sn': 'Sn_d', 'Sr': 'Sr_sv', 'Ta': 'Ta_pv', 'Tb': 'Tb_3', 'Tc': 'Tc_pv', 'Te': 'Te', 'Th': 'Th', 'Ti': 'Ti_pv', 'Tl': 'Tl_d', 'Tm': 'Tm_3', 'U': 'U', 'V': 'V_pv', 'W': 'W_pv', 'Xe': 'Xe', 'Y': 'Y_sv', 'Yb': 'Yb_2', 'Zn': 'Zn', 'Zr': 'Zr_sv'}, 'POTCAR_FUNCTIONAL': 'PBE'}[source]
SUPPORTED_MODES = ('IPA', 'RPA')[source]
copy_wavecar: bool = True[source]
force_gamma: bool = True[source]
property incar_updates: dict[source]

Get updates to the INCAR config for this calculation type.

inherit_incar: bool = True[source]
property kpoints_updates: dict | Kpoints[source]

Get updates to the kpoints configuration for this calculation type.

Generate gamma center k-points mesh grid for optical calculation. It is not mandatory for ‘ALGO = Exact’, but is requested by ‘ALGO = CHI’ calculation.

mode: str = 'IPA'[source]
nbands: int | None = None[source]
nbands_factor: float = 2[source]
nedos: int = 2001[source]
nkred: tuple[int, int, int] | None = None[source]
reciprocal_density: float = 400[source]
class MPHSEBSSet(structure: Structure | None = <property object>, config_dict: dict = <factory>, files_to_transfer: dict = <factory>, user_incar_settings: dict = <factory>, user_kpoints_settings: dict = <factory>, user_potcar_settings: dict = <factory>, constrain_total_magmom: bool = False, sort_structure: bool = True, user_potcar_functional: UserPotcarFunctional = None, force_gamma: bool = False, reduce_structure: Literal['niggli', 'LLL'] | None = None, vdw: str | None = None, use_structure_charge: bool = False, standardize: bool = False, sym_prec: float = 0.1, international_monoclinic: bool = True, validate_magmom: bool = True, inherit_incar: bool | list[str] = False, auto_ismear: bool = False, bandgap_tol: float = 0.0001, bandgap: float | None = None, prev_incar: str | dict | None = None, prev_kpoints: str | Kpoints | None = None, added_kpoints: list[Vector3D] = <factory>, mode: str = 'gap', reciprocal_density: float = 50, copy_chgcar: bool = True, kpoints_line_density: float = 20, nbands_factor: float = 1.2, zero_weighted_reciprocal_density: float = 100, dedos: float = 0.02, optics: bool = False)[source]

Bases: DictSet

Implementation of a VaspInputSet for HSE band structure computations.

Remember that HSE band structures must be self-consistent in VASP. A band structure along symmetry lines for instance needs BOTH a uniform grid with appropriate weights AND a path along the lines with weight 0.

Thus, the “uniform” mode is just like regular static SCF but allows adding custom kpoints (e.g., corresponding to known VBM/CBM) to the uniform grid that have zero weight (e.g., for better gap estimate).

The “gap” mode behaves just like the “uniform” mode, however, if starting from a previous calculation, the VBM and CBM k-points will automatically be added to added_kpoints.

The “line” mode is just like “uniform” mode, but additionally adds k-points along symmetry lines with zero weight.

The “uniform_dense” mode is like “uniform” mode but additionally adds a denser uniform mesh with zero weight. This can be useful when calculating Fermi surfaces or BoltzTraP/AMSET electronic transport using hybrid DFT.

Parameters:
  • structure (Structure) – Structure to compute

  • added_kpoints (list) – a list of kpoints (list of 3 number list) added to the run. The k-points are in fractional coordinates

  • mode (str) – “Line” - generate k-points along symmetry lines for bandstructure. “Uniform” - generate uniform k-points grid.

  • reciprocal_density (int) – k-point density to use for uniform mesh.

  • copy_chgcar (bool) – Whether to copy the CHGCAR of a previous run.

  • kpoints_line_density (int) – k-point density for high symmetry lines

  • dedos (float) – Energy difference used to set NEDOS, based on the total energy range.

  • optics (bool) – Whether to add LOPTICS (used for calculating optical response).

  • nbands_factor (float) – Multiplicative factor for NBANDS when starting from a previous calculation. Choose a higher number if you are doing an LOPTICS calculation.

  • **kwargs (dict) – Any other parameters to pass into DictSet.

CONFIG = {'INCAR': {'ALGO': 'All', 'EDIFF_PER_ATOM': 5e-05, 'ENCUT': 520, 'HFSCREEN': 0.2, 'IBRION': 2, 'ICHARG': 1, 'ISIF': 3, 'ISMEAR': 0, 'ISPIN': 2, 'LHFCALC': True, 'LORBIT': 11, 'LREAL': 'AUTO', 'LWAVE': False, 'MAGMOM': {'Ce': 5, 'Ce3+': 1, 'Co': 0.6, 'Co3+': 0.6, 'Co4+': 1, 'Cr': 5, 'Dy3+': 5, 'Er3+': 3, 'Eu': 10, 'Eu2+': 7, 'Eu3+': 6, 'Fe': 5, 'Gd3+': 7, 'Ho3+': 4, 'La3+': 0.6, 'Lu3+': 0.6, 'Mn': 5, 'Mn3+': 4, 'Mn4+': 3, 'Mo': 5, 'Nd3+': 3, 'Ni': 5, 'Pm3+': 4, 'Pr3+': 2, 'Sm3+': 5, 'Tb3+': 6, 'Tm3+': 2, 'V': 5, 'W': 5, 'Yb3+': 1}, 'NELM': 100, 'NSW': 99, 'PREC': 'Accurate', 'PRECFOCK': 'Fast', 'SIGMA': 0.05}, 'KPOINTS': {'reciprocal_density': 50}, 'PARENT': 'VASPIncarBase', 'POTCAR': {'Ac': 'Ac', 'Ag': 'Ag', 'Al': 'Al', 'Ar': 'Ar', 'As': 'As', 'Au': 'Au', 'B': 'B', 'Ba': 'Ba_sv', 'Be': 'Be_sv', 'Bi': 'Bi', 'Br': 'Br', 'C': 'C', 'Ca': 'Ca_sv', 'Cd': 'Cd', 'Ce': 'Ce', 'Cl': 'Cl', 'Co': 'Co', 'Cr': 'Cr_pv', 'Cs': 'Cs_sv', 'Cu': 'Cu_pv', 'Dy': 'Dy_3', 'Er': 'Er_3', 'Eu': 'Eu', 'F': 'F', 'Fe': 'Fe_pv', 'Ga': 'Ga_d', 'Gd': 'Gd', 'Ge': 'Ge_d', 'H': 'H', 'He': 'He', 'Hf': 'Hf_pv', 'Hg': 'Hg', 'Ho': 'Ho_3', 'I': 'I', 'In': 'In_d', 'Ir': 'Ir', 'K': 'K_sv', 'Kr': 'Kr', 'La': 'La', 'Li': 'Li_sv', 'Lu': 'Lu_3', 'Mg': 'Mg_pv', 'Mn': 'Mn_pv', 'Mo': 'Mo_pv', 'N': 'N', 'Na': 'Na_pv', 'Nb': 'Nb_pv', 'Nd': 'Nd_3', 'Ne': 'Ne', 'Ni': 'Ni_pv', 'Np': 'Np', 'O': 'O', 'Os': 'Os_pv', 'P': 'P', 'Pa': 'Pa', 'Pb': 'Pb_d', 'Pd': 'Pd', 'Pm': 'Pm_3', 'Pr': 'Pr_3', 'Pt': 'Pt', 'Pu': 'Pu', 'Rb': 'Rb_sv', 'Re': 'Re_pv', 'Rh': 'Rh_pv', 'Ru': 'Ru_pv', 'S': 'S', 'Sb': 'Sb', 'Sc': 'Sc_sv', 'Se': 'Se', 'Si': 'Si', 'Sm': 'Sm_3', 'Sn': 'Sn_d', 'Sr': 'Sr_sv', 'Ta': 'Ta_pv', 'Tb': 'Tb_3', 'Tc': 'Tc_pv', 'Te': 'Te', 'Th': 'Th', 'Ti': 'Ti_pv', 'Tl': 'Tl_d', 'Tm': 'Tm_3', 'U': 'U', 'V': 'V_pv', 'W': 'W_pv', 'Xe': 'Xe', 'Y': 'Y_sv', 'Yb': 'Yb_2', 'Zn': 'Zn', 'Zr': 'Zr_sv'}, 'POTCAR_FUNCTIONAL': 'PBE_52'}[source]
added_kpoints: list[Vector3D][source]
copy_chgcar: bool = True[source]
dedos: float = 0.02[source]
property incar_updates: dict[source]

Get updates to the INCAR config for this calculation type.

kpoints_line_density: float = 20[source]
property kpoints_updates: dict[source]

Get updates to the kpoints configuration for this calculation type.

mode: str = 'gap'[source]
nbands_factor: float = 1.2[source]
optics: bool = False[source]
reciprocal_density: float = 50[source]
zero_weighted_reciprocal_density: float = 100[source]
class MPHSERelaxSet(structure: ~pymatgen.core.structure.Structure | None = <property object>, config_dict: dict = <factory>, files_to_transfer: dict = <factory>, user_incar_settings: dict = <factory>, user_kpoints_settings: dict = <factory>, user_potcar_settings: dict = <factory>, constrain_total_magmom: bool = False, sort_structure: bool = True, user_potcar_functional: ~typing.Literal['PBE', 'PBE_52', 'PBE_54', 'LDA', 'LDA_52', 'LDA_54', 'PW91', 'LDA_US', 'PW91_US'] | None = None, force_gamma: bool = False, reduce_structure: ~typing.Literal['niggli', 'LLL'] | None = None, vdw: str | None = None, use_structure_charge: bool = False, standardize: bool = False, sym_prec: float = 0.1, international_monoclinic: bool = True, validate_magmom: bool = True, inherit_incar: bool | list[str] = False, auto_ismear: bool = False, bandgap_tol: float = 0.0001, bandgap: float | None = None, prev_incar: str | dict | None = None, prev_kpoints: str | ~pymatgen.io.vasp.inputs.Kpoints | None = None)[source]

Bases: DictSet

Same as the MPRelaxSet, but with HSE parameters.

CONFIG = {'INCAR': {'ALGO': 'All', 'EDIFF_PER_ATOM': 5e-05, 'ENCUT': 520, 'HFSCREEN': 0.2, 'IBRION': 2, 'ICHARG': 1, 'ISIF': 3, 'ISMEAR': 0, 'ISPIN': 2, 'LHFCALC': True, 'LORBIT': 11, 'LREAL': 'AUTO', 'LWAVE': False, 'MAGMOM': {'Ce': 5, 'Ce3+': 1, 'Co': 0.6, 'Co3+': 0.6, 'Co4+': 1, 'Cr': 5, 'Dy3+': 5, 'Er3+': 3, 'Eu': 10, 'Eu2+': 7, 'Eu3+': 6, 'Fe': 5, 'Gd3+': 7, 'Ho3+': 4, 'La3+': 0.6, 'Lu3+': 0.6, 'Mn': 5, 'Mn3+': 4, 'Mn4+': 3, 'Mo': 5, 'Nd3+': 3, 'Ni': 5, 'Pm3+': 4, 'Pr3+': 2, 'Sm3+': 5, 'Tb3+': 6, 'Tm3+': 2, 'V': 5, 'W': 5, 'Yb3+': 1}, 'NELM': 100, 'NSW': 99, 'PREC': 'Accurate', 'PRECFOCK': 'Fast', 'SIGMA': 0.05}, 'KPOINTS': {'reciprocal_density': 50}, 'PARENT': 'VASPIncarBase', 'POTCAR': {'Ac': 'Ac', 'Ag': 'Ag', 'Al': 'Al', 'Ar': 'Ar', 'As': 'As', 'Au': 'Au', 'B': 'B', 'Ba': 'Ba_sv', 'Be': 'Be_sv', 'Bi': 'Bi', 'Br': 'Br', 'C': 'C', 'Ca': 'Ca_sv', 'Cd': 'Cd', 'Ce': 'Ce', 'Cl': 'Cl', 'Co': 'Co', 'Cr': 'Cr_pv', 'Cs': 'Cs_sv', 'Cu': 'Cu_pv', 'Dy': 'Dy_3', 'Er': 'Er_3', 'Eu': 'Eu', 'F': 'F', 'Fe': 'Fe_pv', 'Ga': 'Ga_d', 'Gd': 'Gd', 'Ge': 'Ge_d', 'H': 'H', 'He': 'He', 'Hf': 'Hf_pv', 'Hg': 'Hg', 'Ho': 'Ho_3', 'I': 'I', 'In': 'In_d', 'Ir': 'Ir', 'K': 'K_sv', 'Kr': 'Kr', 'La': 'La', 'Li': 'Li_sv', 'Lu': 'Lu_3', 'Mg': 'Mg_pv', 'Mn': 'Mn_pv', 'Mo': 'Mo_pv', 'N': 'N', 'Na': 'Na_pv', 'Nb': 'Nb_pv', 'Nd': 'Nd_3', 'Ne': 'Ne', 'Ni': 'Ni_pv', 'Np': 'Np', 'O': 'O', 'Os': 'Os_pv', 'P': 'P', 'Pa': 'Pa', 'Pb': 'Pb_d', 'Pd': 'Pd', 'Pm': 'Pm_3', 'Pr': 'Pr_3', 'Pt': 'Pt', 'Pu': 'Pu', 'Rb': 'Rb_sv', 'Re': 'Re_pv', 'Rh': 'Rh_pv', 'Ru': 'Ru_pv', 'S': 'S', 'Sb': 'Sb', 'Sc': 'Sc_sv', 'Se': 'Se', 'Si': 'Si', 'Sm': 'Sm_3', 'Sn': 'Sn_d', 'Sr': 'Sr_sv', 'Ta': 'Ta_pv', 'Tb': 'Tb_3', 'Tc': 'Tc_pv', 'Te': 'Te', 'Th': 'Th', 'Ti': 'Ti_pv', 'Tl': 'Tl_d', 'Tm': 'Tm_3', 'U': 'U', 'V': 'V_pv', 'W': 'W_pv', 'Xe': 'Xe', 'Y': 'Y_sv', 'Yb': 'Yb_2', 'Zn': 'Zn', 'Zr': 'Zr_sv'}, 'POTCAR_FUNCTIONAL': 'PBE_52'}[source]
class MPMDSet(structure: ~pymatgen.core.structure.Structure | None = <property object>, config_dict: dict = <factory>, files_to_transfer: dict = <factory>, user_incar_settings: dict = <factory>, user_kpoints_settings: dict = <factory>, user_potcar_settings: dict = <factory>, constrain_total_magmom: bool = False, sort_structure: bool = True, user_potcar_functional: ~typing.Literal['PBE', 'PBE_52', 'PBE_54', 'LDA', 'LDA_52', 'LDA_54', 'PW91', 'LDA_US', 'PW91_US'] | None = None, force_gamma: bool = False, reduce_structure: ~typing.Literal['niggli', 'LLL'] | None = None, vdw: str | None = None, use_structure_charge: bool = False, standardize: bool = False, sym_prec: float = 0.1, international_monoclinic: bool = True, validate_magmom: bool = True, inherit_incar: bool | list[str] = False, auto_ismear: bool = False, bandgap_tol: float = 0.0001, bandgap: float | None = None, prev_incar: str | dict | None = None, prev_kpoints: str | ~pymatgen.io.vasp.inputs.Kpoints | None = None, start_temp: float = 0.0, end_temp: float = 300.0, nsteps: int = 1000, time_step: float | None = None, spin_polarized: bool = False)[source]

Bases: DictSet

This a modified version of the old MITMDSet pre 2018/03/12.

This set serves as the basis for the amorphous skyline paper.

  1. Aykol, M.; Dwaraknath, S. S.; Sun, W.; Persson, K. A. Thermodynamic Limit for Synthesis of Metastable Inorganic Materials. Sci. Adv. 2018, 4 (4).

Class for writing a vasp md run. This DOES NOT do multiple stage runs. Precision remains normal, to increase accuracy of stress tensor.

Parameters:
  • structure (Structure) – Input structure.

  • start_temp (int) – Starting temperature.

  • end_temp (int) – Final temperature.

  • nsteps (int) – Number of time steps for simulations. NSW parameter.

  • time_step (float) – The time step for the simulation. The POTIM parameter. Defaults to None, which will set it automatically to 2.0 fs for non-hydrogen containing structures and 0.5 fs for hydrogen containing structures.

  • spin_polarized (bool) – Whether to do spin polarized calculations. The ISPIN parameter. Defaults to False.

  • **kwargs – Other kwargs supported by DictSet.

CONFIG = {'INCAR': {'ALGO': 'FAST', 'EDIFF_PER_ATOM': 5e-05, 'ENCUT': 520, 'IBRION': 2, 'ISIF': 3, 'ISMEAR': -5, 'ISPIN': 2, 'LASPH': True, 'LDAU': True, 'LDAUJ': {'F': {'Co': 0, 'Cr': 0, 'Fe': 0, 'Mn': 0, 'Mo': 0, 'Ni': 0, 'V': 0, 'W': 0}, 'O': {'Co': 0, 'Cr': 0, 'Fe': 0, 'Mn': 0, 'Mo': 0, 'Ni': 0, 'V': 0, 'W': 0}}, 'LDAUL': {'F': {'Co': 2, 'Cr': 2, 'Fe': 2, 'Mn': 2, 'Mo': 2, 'Ni': 2, 'V': 2, 'W': 2}, 'O': {'Co': 2, 'Cr': 2, 'Fe': 2, 'Mn': 2, 'Mo': 2, 'Ni': 2, 'V': 2, 'W': 2}}, 'LDAUPRINT': 1, 'LDAUTYPE': 2, 'LDAUU': {'F': {'Co': 3.32, 'Cr': 3.7, 'Fe': 5.3, 'Mn': 3.9, 'Mo': 4.38, 'Ni': 6.2, 'V': 3.25, 'W': 6.2}, 'O': {'Co': 3.32, 'Cr': 3.7, 'Fe': 5.3, 'Mn': 3.9, 'Mo': 4.38, 'Ni': 6.2, 'V': 3.25, 'W': 6.2}}, 'LORBIT': 11, 'LREAL': 'AUTO', 'LWAVE': False, 'MAGMOM': {'Ce': 5, 'Ce3+': 1, 'Co': 0.6, 'Co3+': 0.6, 'Co4+': 1, 'Cr': 5, 'Dy3+': 5, 'Er3+': 3, 'Eu': 10, 'Eu2+': 7, 'Eu3+': 6, 'Fe': 5, 'Gd3+': 7, 'Ho3+': 4, 'La3+': 0.6, 'Lu3+': 0.6, 'Mn': 5, 'Mn3+': 4, 'Mn4+': 3, 'Mo': 5, 'Nd3+': 3, 'Ni': 5, 'Pm3+': 4, 'Pr3+': 2, 'Sm3+': 5, 'Tb3+': 6, 'Tm3+': 2, 'V': 5, 'W': 5, 'Yb3+': 1}, 'NELM': 100, 'NSW': 99, 'PREC': 'Accurate', 'SIGMA': 0.05}, 'KPOINTS': {'reciprocal_density': 64}, 'PARENT': 'VASPIncarBase', 'POTCAR': {'Ac': 'Ac', 'Ag': 'Ag', 'Al': 'Al', 'Ar': 'Ar', 'As': 'As', 'Au': 'Au', 'B': 'B', 'Ba': 'Ba_sv', 'Be': 'Be_sv', 'Bi': 'Bi', 'Br': 'Br', 'C': 'C', 'Ca': 'Ca_sv', 'Cd': 'Cd', 'Ce': 'Ce', 'Cl': 'Cl', 'Co': 'Co', 'Cr': 'Cr_pv', 'Cs': 'Cs_sv', 'Cu': 'Cu_pv', 'Dy': 'Dy_3', 'Er': 'Er_3', 'Eu': 'Eu', 'F': 'F', 'Fe': 'Fe_pv', 'Ga': 'Ga_d', 'Gd': 'Gd', 'Ge': 'Ge_d', 'H': 'H', 'He': 'He', 'Hf': 'Hf_pv', 'Hg': 'Hg', 'Ho': 'Ho_3', 'I': 'I', 'In': 'In_d', 'Ir': 'Ir', 'K': 'K_sv', 'Kr': 'Kr', 'La': 'La', 'Li': 'Li_sv', 'Lu': 'Lu_3', 'Mg': 'Mg_pv', 'Mn': 'Mn_pv', 'Mo': 'Mo_pv', 'N': 'N', 'Na': 'Na_pv', 'Nb': 'Nb_pv', 'Nd': 'Nd_3', 'Ne': 'Ne', 'Ni': 'Ni_pv', 'Np': 'Np', 'O': 'O', 'Os': 'Os_pv', 'P': 'P', 'Pa': 'Pa', 'Pb': 'Pb_d', 'Pd': 'Pd', 'Pm': 'Pm_3', 'Pr': 'Pr_3', 'Pt': 'Pt', 'Pu': 'Pu', 'Rb': 'Rb_sv', 'Re': 'Re_pv', 'Rh': 'Rh_pv', 'Ru': 'Ru_pv', 'S': 'S', 'Sb': 'Sb', 'Sc': 'Sc_sv', 'Se': 'Se', 'Si': 'Si', 'Sm': 'Sm_3', 'Sn': 'Sn_d', 'Sr': 'Sr_sv', 'Ta': 'Ta_pv', 'Tb': 'Tb_3', 'Tc': 'Tc_pv', 'Te': 'Te', 'Th': 'Th', 'Ti': 'Ti_pv', 'Tl': 'Tl_d', 'Tm': 'Tm_3', 'U': 'U', 'V': 'V_pv', 'W': 'W_pv', 'Xe': 'Xe', 'Y': 'Y_sv', 'Yb': 'Yb_2', 'Zn': 'Zn', 'Zr': 'Zr_sv'}, 'POTCAR_FUNCTIONAL': 'PBE'}[source]
end_temp: float = 300.0[source]
property incar_updates: dict[source]

Get updates to the INCAR config for this calculation type.

property kpoints_updates: dict | Kpoints[source]

Get updates to the kpoints configuration for this calculation type.

nsteps: int = 1000[source]
spin_polarized: bool = False[source]
start_temp: float = 0.0[source]
time_step: float | None = None[source]
class MPMetalRelaxSet(structure: ~pymatgen.core.structure.Structure | None = <property object>, config_dict: dict = <factory>, files_to_transfer: dict = <factory>, user_incar_settings: dict = <factory>, user_kpoints_settings: dict = <factory>, user_potcar_settings: dict = <factory>, constrain_total_magmom: bool = False, sort_structure: bool = True, user_potcar_functional: ~typing.Literal['PBE', 'PBE_52', 'PBE_54', 'LDA', 'LDA_52', 'LDA_54', 'PW91', 'LDA_US', 'PW91_US'] | None = None, force_gamma: bool = False, reduce_structure: ~typing.Literal['niggli', 'LLL'] | None = None, vdw: str | None = None, use_structure_charge: bool = False, standardize: bool = False, sym_prec: float = 0.1, international_monoclinic: bool = True, validate_magmom: bool = True, inherit_incar: bool | list[str] = False, auto_ismear: bool = False, bandgap_tol: float = 0.0001, bandgap: float | None = None, prev_incar: str | dict | None = None, prev_kpoints: str | ~pymatgen.io.vasp.inputs.Kpoints | None = None)[source]

Bases: DictSet

Implementation of VaspInputSet utilizing parameters in the public Materials Project, but with tuning for metals. Key things are a denser k point density, and a.

CONFIG = {'INCAR': {'ALGO': 'FAST', 'EDIFF_PER_ATOM': 5e-05, 'ENCUT': 520, 'IBRION': 2, 'ISIF': 3, 'ISMEAR': -5, 'ISPIN': 2, 'LASPH': True, 'LDAU': True, 'LDAUJ': {'F': {'Co': 0, 'Cr': 0, 'Fe': 0, 'Mn': 0, 'Mo': 0, 'Ni': 0, 'V': 0, 'W': 0}, 'O': {'Co': 0, 'Cr': 0, 'Fe': 0, 'Mn': 0, 'Mo': 0, 'Ni': 0, 'V': 0, 'W': 0}}, 'LDAUL': {'F': {'Co': 2, 'Cr': 2, 'Fe': 2, 'Mn': 2, 'Mo': 2, 'Ni': 2, 'V': 2, 'W': 2}, 'O': {'Co': 2, 'Cr': 2, 'Fe': 2, 'Mn': 2, 'Mo': 2, 'Ni': 2, 'V': 2, 'W': 2}}, 'LDAUPRINT': 1, 'LDAUTYPE': 2, 'LDAUU': {'F': {'Co': 3.32, 'Cr': 3.7, 'Fe': 5.3, 'Mn': 3.9, 'Mo': 4.38, 'Ni': 6.2, 'V': 3.25, 'W': 6.2}, 'O': {'Co': 3.32, 'Cr': 3.7, 'Fe': 5.3, 'Mn': 3.9, 'Mo': 4.38, 'Ni': 6.2, 'V': 3.25, 'W': 6.2}}, 'LORBIT': 11, 'LREAL': 'AUTO', 'LWAVE': False, 'MAGMOM': {'Ce': 5, 'Ce3+': 1, 'Co': 0.6, 'Co3+': 0.6, 'Co4+': 1, 'Cr': 5, 'Dy3+': 5, 'Er3+': 3, 'Eu': 10, 'Eu2+': 7, 'Eu3+': 6, 'Fe': 5, 'Gd3+': 7, 'Ho3+': 4, 'La3+': 0.6, 'Lu3+': 0.6, 'Mn': 5, 'Mn3+': 4, 'Mn4+': 3, 'Mo': 5, 'Nd3+': 3, 'Ni': 5, 'Pm3+': 4, 'Pr3+': 2, 'Sm3+': 5, 'Tb3+': 6, 'Tm3+': 2, 'V': 5, 'W': 5, 'Yb3+': 1}, 'NELM': 100, 'NSW': 99, 'PREC': 'Accurate', 'SIGMA': 0.05}, 'KPOINTS': {'reciprocal_density': 64}, 'PARENT': 'VASPIncarBase', 'POTCAR': {'Ac': 'Ac', 'Ag': 'Ag', 'Al': 'Al', 'Ar': 'Ar', 'As': 'As', 'Au': 'Au', 'B': 'B', 'Ba': 'Ba_sv', 'Be': 'Be_sv', 'Bi': 'Bi', 'Br': 'Br', 'C': 'C', 'Ca': 'Ca_sv', 'Cd': 'Cd', 'Ce': 'Ce', 'Cl': 'Cl', 'Co': 'Co', 'Cr': 'Cr_pv', 'Cs': 'Cs_sv', 'Cu': 'Cu_pv', 'Dy': 'Dy_3', 'Er': 'Er_3', 'Eu': 'Eu', 'F': 'F', 'Fe': 'Fe_pv', 'Ga': 'Ga_d', 'Gd': 'Gd', 'Ge': 'Ge_d', 'H': 'H', 'He': 'He', 'Hf': 'Hf_pv', 'Hg': 'Hg', 'Ho': 'Ho_3', 'I': 'I', 'In': 'In_d', 'Ir': 'Ir', 'K': 'K_sv', 'Kr': 'Kr', 'La': 'La', 'Li': 'Li_sv', 'Lu': 'Lu_3', 'Mg': 'Mg_pv', 'Mn': 'Mn_pv', 'Mo': 'Mo_pv', 'N': 'N', 'Na': 'Na_pv', 'Nb': 'Nb_pv', 'Nd': 'Nd_3', 'Ne': 'Ne', 'Ni': 'Ni_pv', 'Np': 'Np', 'O': 'O', 'Os': 'Os_pv', 'P': 'P', 'Pa': 'Pa', 'Pb': 'Pb_d', 'Pd': 'Pd', 'Pm': 'Pm_3', 'Pr': 'Pr_3', 'Pt': 'Pt', 'Pu': 'Pu', 'Rb': 'Rb_sv', 'Re': 'Re_pv', 'Rh': 'Rh_pv', 'Ru': 'Ru_pv', 'S': 'S', 'Sb': 'Sb', 'Sc': 'Sc_sv', 'Se': 'Se', 'Si': 'Si', 'Sm': 'Sm_3', 'Sn': 'Sn_d', 'Sr': 'Sr_sv', 'Ta': 'Ta_pv', 'Tb': 'Tb_3', 'Tc': 'Tc_pv', 'Te': 'Te', 'Th': 'Th', 'Ti': 'Ti_pv', 'Tl': 'Tl_d', 'Tm': 'Tm_3', 'U': 'U', 'V': 'V_pv', 'W': 'W_pv', 'Xe': 'Xe', 'Y': 'Y_sv', 'Yb': 'Yb_2', 'Zn': 'Zn', 'Zr': 'Zr_sv'}, 'POTCAR_FUNCTIONAL': 'PBE'}[source]
property incar_updates: dict[source]

Get updates to the INCAR config for this calculation type.

property kpoints_updates: dict | Kpoints[source]

Get updates to the kpoints configuration for this calculation type.

class MPNMRSet(structure: ~pymatgen.core.structure.Structure | None = <property object>, config_dict: dict = <factory>, files_to_transfer: dict = <factory>, user_incar_settings: dict = <factory>, user_kpoints_settings: dict = <factory>, user_potcar_settings: dict = <factory>, constrain_total_magmom: bool = False, sort_structure: bool = True, user_potcar_functional: ~typing.Literal['PBE', 'PBE_52', 'PBE_54', 'LDA', 'LDA_52', 'LDA_54', 'PW91', 'LDA_US', 'PW91_US'] | None = None, force_gamma: bool = False, reduce_structure: ~typing.Literal['niggli', 'LLL'] | None = None, vdw: str | None = None, use_structure_charge: bool = False, standardize: bool = False, sym_prec: float = 0.1, international_monoclinic: bool = True, validate_magmom: bool = True, inherit_incar: bool = True, auto_ismear: bool = False, bandgap_tol: float = 0.0001, bandgap: float | None = None, prev_incar: str | dict | None = None, prev_kpoints: str | ~pymatgen.io.vasp.inputs.Kpoints | None = None, mode: ~typing.Literal['cs', 'efg'] = 'cs', isotopes: list = <factory>, reciprocal_density: int = 100, small_gap_multiply: tuple[float, float] | None = None)[source]

Bases: DictSet

Init a MPNMRSet.

Parameters:
  • structure (Structure) – Structure from previous run.

  • mode (str) – The NMR calculation to run “cs”: for Chemical Shift “efg” for Electric Field Gradient

  • isotopes (list) – list of Isotopes for quadrupole moments

  • reciprocal_density (int) – density of k-mesh by reciprocal volume.

  • lepsilon (bool) – Whether to add static dielectric calculation

  • lcalcpol (bool) – Whether to turn on evaluation of the Berry phase approximations for electronic polarization

  • reciprocal_density – For static calculations, we usually set the reciprocal density by volume. This is a convenience arg to change that, rather than using user_kpoints_settings. Defaults to 100, which is ~50% more than that of standard relaxation calculations.

  • small_gap_multiply ([float, float]) – If the gap is less than 1st index, multiply the default reciprocal_density by the 2nd index.

  • **kwargs – kwargs supported by MPRelaxSet.

CONFIG = {'INCAR': {'ALGO': 'FAST', 'EDIFF_PER_ATOM': 5e-05, 'ENCUT': 520, 'IBRION': 2, 'ISIF': 3, 'ISMEAR': -5, 'ISPIN': 2, 'LASPH': True, 'LDAU': True, 'LDAUJ': {'F': {'Co': 0, 'Cr': 0, 'Fe': 0, 'Mn': 0, 'Mo': 0, 'Ni': 0, 'V': 0, 'W': 0}, 'O': {'Co': 0, 'Cr': 0, 'Fe': 0, 'Mn': 0, 'Mo': 0, 'Ni': 0, 'V': 0, 'W': 0}}, 'LDAUL': {'F': {'Co': 2, 'Cr': 2, 'Fe': 2, 'Mn': 2, 'Mo': 2, 'Ni': 2, 'V': 2, 'W': 2}, 'O': {'Co': 2, 'Cr': 2, 'Fe': 2, 'Mn': 2, 'Mo': 2, 'Ni': 2, 'V': 2, 'W': 2}}, 'LDAUPRINT': 1, 'LDAUTYPE': 2, 'LDAUU': {'F': {'Co': 3.32, 'Cr': 3.7, 'Fe': 5.3, 'Mn': 3.9, 'Mo': 4.38, 'Ni': 6.2, 'V': 3.25, 'W': 6.2}, 'O': {'Co': 3.32, 'Cr': 3.7, 'Fe': 5.3, 'Mn': 3.9, 'Mo': 4.38, 'Ni': 6.2, 'V': 3.25, 'W': 6.2}}, 'LORBIT': 11, 'LREAL': 'AUTO', 'LWAVE': False, 'MAGMOM': {'Ce': 5, 'Ce3+': 1, 'Co': 0.6, 'Co3+': 0.6, 'Co4+': 1, 'Cr': 5, 'Dy3+': 5, 'Er3+': 3, 'Eu': 10, 'Eu2+': 7, 'Eu3+': 6, 'Fe': 5, 'Gd3+': 7, 'Ho3+': 4, 'La3+': 0.6, 'Lu3+': 0.6, 'Mn': 5, 'Mn3+': 4, 'Mn4+': 3, 'Mo': 5, 'Nd3+': 3, 'Ni': 5, 'Pm3+': 4, 'Pr3+': 2, 'Sm3+': 5, 'Tb3+': 6, 'Tm3+': 2, 'V': 5, 'W': 5, 'Yb3+': 1}, 'NELM': 100, 'NSW': 99, 'PREC': 'Accurate', 'SIGMA': 0.05}, 'KPOINTS': {'reciprocal_density': 64}, 'PARENT': 'VASPIncarBase', 'POTCAR': {'Ac': 'Ac', 'Ag': 'Ag', 'Al': 'Al', 'Ar': 'Ar', 'As': 'As', 'Au': 'Au', 'B': 'B', 'Ba': 'Ba_sv', 'Be': 'Be_sv', 'Bi': 'Bi', 'Br': 'Br', 'C': 'C', 'Ca': 'Ca_sv', 'Cd': 'Cd', 'Ce': 'Ce', 'Cl': 'Cl', 'Co': 'Co', 'Cr': 'Cr_pv', 'Cs': 'Cs_sv', 'Cu': 'Cu_pv', 'Dy': 'Dy_3', 'Er': 'Er_3', 'Eu': 'Eu', 'F': 'F', 'Fe': 'Fe_pv', 'Ga': 'Ga_d', 'Gd': 'Gd', 'Ge': 'Ge_d', 'H': 'H', 'He': 'He', 'Hf': 'Hf_pv', 'Hg': 'Hg', 'Ho': 'Ho_3', 'I': 'I', 'In': 'In_d', 'Ir': 'Ir', 'K': 'K_sv', 'Kr': 'Kr', 'La': 'La', 'Li': 'Li_sv', 'Lu': 'Lu_3', 'Mg': 'Mg_pv', 'Mn': 'Mn_pv', 'Mo': 'Mo_pv', 'N': 'N', 'Na': 'Na_pv', 'Nb': 'Nb_pv', 'Nd': 'Nd_3', 'Ne': 'Ne', 'Ni': 'Ni_pv', 'Np': 'Np', 'O': 'O', 'Os': 'Os_pv', 'P': 'P', 'Pa': 'Pa', 'Pb': 'Pb_d', 'Pd': 'Pd', 'Pm': 'Pm_3', 'Pr': 'Pr_3', 'Pt': 'Pt', 'Pu': 'Pu', 'Rb': 'Rb_sv', 'Re': 'Re_pv', 'Rh': 'Rh_pv', 'Ru': 'Ru_pv', 'S': 'S', 'Sb': 'Sb', 'Sc': 'Sc_sv', 'Se': 'Se', 'Si': 'Si', 'Sm': 'Sm_3', 'Sn': 'Sn_d', 'Sr': 'Sr_sv', 'Ta': 'Ta_pv', 'Tb': 'Tb_3', 'Tc': 'Tc_pv', 'Te': 'Te', 'Th': 'Th', 'Ti': 'Ti_pv', 'Tl': 'Tl_d', 'Tm': 'Tm_3', 'U': 'U', 'V': 'V_pv', 'W': 'W_pv', 'Xe': 'Xe', 'Y': 'Y_sv', 'Yb': 'Yb_2', 'Zn': 'Zn', 'Zr': 'Zr_sv'}, 'POTCAR_FUNCTIONAL': 'PBE'}[source]
property incar_updates: dict[source]

Get updates to the INCAR config for this calculation type.

inherit_incar: bool = True[source]
isotopes: list[source]
property kpoints_updates: dict[source]

Get updates to the kpoints configuration for this calculation type.

mode: Literal['cs', 'efg'] = 'cs'[source]
reciprocal_density: int = 100[source]
small_gap_multiply: tuple[float, float] | None = None[source]
class MPNonSCFSet(structure: ~pymatgen.core.structure.Structure | None = <property object>, config_dict: dict = <factory>, files_to_transfer: dict = <factory>, user_incar_settings: dict = <factory>, user_kpoints_settings: dict = <factory>, user_potcar_settings: dict = <factory>, constrain_total_magmom: bool = False, sort_structure: bool = True, user_potcar_functional: ~typing.Literal['PBE', 'PBE_52', 'PBE_54', 'LDA', 'LDA_52', 'LDA_54', 'PW91', 'LDA_US', 'PW91_US'] | None = None, force_gamma: bool = False, reduce_structure: ~typing.Literal['niggli', 'LLL'] | None = None, vdw: str | None = None, use_structure_charge: bool = False, standardize: bool = False, sym_prec: float = 0.1, international_monoclinic: bool = True, validate_magmom: bool = True, inherit_incar: bool = True, auto_ismear: bool = False, bandgap_tol: float = 0.0001, bandgap: float | None = None, prev_incar: str | dict | None = None, prev_kpoints: str | ~pymatgen.io.vasp.inputs.Kpoints | None = None, mode: str = 'line', nedos: int = 2001, dedos: float = 0.005, reciprocal_density: float = 100, kpoints_line_density: float = 20, optics: bool = False, copy_chgcar: bool = True, nbands_factor: float = 1.2, small_gap_multiply: tuple[float, float] | None = None)[source]

Bases: DictSet

Init a MPNonSCFSet. Typically, you would use the classmethod from_prev_calc to initialize from a previous SCF run.

Parameters:
  • structure (Structure) – Structure to compute

  • mode (str) – Line, Uniform or Boltztrap mode supported.

  • nedos (int) – nedos parameter. Default to 2001.

  • dedos (float) – setting nedos=0 and uniform mode in from_prev_calc, an automatic nedos will be calculated using the total energy range divided by the energy step dedos

  • reciprocal_density (int) – density of k-mesh by reciprocal volume (defaults to 100)

  • kpoints_line_density (int) – Line density for Line mode.

  • optics (bool) – whether to add dielectric function

  • copy_chgcar – Whether to copy the old CHGCAR when starting from a previous calculation.

  • nbands_factor (float) – Multiplicative factor for NBANDS when starting from a previous calculation. Choose a higher number if you are doing an LOPTICS calculation.

  • small_gap_multiply ([float, float]) – When starting from a previous calculation, if the gap is less than 1st index, multiply the default reciprocal_density by the 2nd index.

  • **kwargs – kwargs supported by MPRelaxSet.

CONFIG = {'INCAR': {'ALGO': 'FAST', 'EDIFF_PER_ATOM': 5e-05, 'ENCUT': 520, 'IBRION': 2, 'ISIF': 3, 'ISMEAR': -5, 'ISPIN': 2, 'LASPH': True, 'LDAU': True, 'LDAUJ': {'F': {'Co': 0, 'Cr': 0, 'Fe': 0, 'Mn': 0, 'Mo': 0, 'Ni': 0, 'V': 0, 'W': 0}, 'O': {'Co': 0, 'Cr': 0, 'Fe': 0, 'Mn': 0, 'Mo': 0, 'Ni': 0, 'V': 0, 'W': 0}}, 'LDAUL': {'F': {'Co': 2, 'Cr': 2, 'Fe': 2, 'Mn': 2, 'Mo': 2, 'Ni': 2, 'V': 2, 'W': 2}, 'O': {'Co': 2, 'Cr': 2, 'Fe': 2, 'Mn': 2, 'Mo': 2, 'Ni': 2, 'V': 2, 'W': 2}}, 'LDAUPRINT': 1, 'LDAUTYPE': 2, 'LDAUU': {'F': {'Co': 3.32, 'Cr': 3.7, 'Fe': 5.3, 'Mn': 3.9, 'Mo': 4.38, 'Ni': 6.2, 'V': 3.25, 'W': 6.2}, 'O': {'Co': 3.32, 'Cr': 3.7, 'Fe': 5.3, 'Mn': 3.9, 'Mo': 4.38, 'Ni': 6.2, 'V': 3.25, 'W': 6.2}}, 'LORBIT': 11, 'LREAL': 'AUTO', 'LWAVE': False, 'MAGMOM': {'Ce': 5, 'Ce3+': 1, 'Co': 0.6, 'Co3+': 0.6, 'Co4+': 1, 'Cr': 5, 'Dy3+': 5, 'Er3+': 3, 'Eu': 10, 'Eu2+': 7, 'Eu3+': 6, 'Fe': 5, 'Gd3+': 7, 'Ho3+': 4, 'La3+': 0.6, 'Lu3+': 0.6, 'Mn': 5, 'Mn3+': 4, 'Mn4+': 3, 'Mo': 5, 'Nd3+': 3, 'Ni': 5, 'Pm3+': 4, 'Pr3+': 2, 'Sm3+': 5, 'Tb3+': 6, 'Tm3+': 2, 'V': 5, 'W': 5, 'Yb3+': 1}, 'NELM': 100, 'NSW': 99, 'PREC': 'Accurate', 'SIGMA': 0.05}, 'KPOINTS': {'reciprocal_density': 64}, 'PARENT': 'VASPIncarBase', 'POTCAR': {'Ac': 'Ac', 'Ag': 'Ag', 'Al': 'Al', 'Ar': 'Ar', 'As': 'As', 'Au': 'Au', 'B': 'B', 'Ba': 'Ba_sv', 'Be': 'Be_sv', 'Bi': 'Bi', 'Br': 'Br', 'C': 'C', 'Ca': 'Ca_sv', 'Cd': 'Cd', 'Ce': 'Ce', 'Cl': 'Cl', 'Co': 'Co', 'Cr': 'Cr_pv', 'Cs': 'Cs_sv', 'Cu': 'Cu_pv', 'Dy': 'Dy_3', 'Er': 'Er_3', 'Eu': 'Eu', 'F': 'F', 'Fe': 'Fe_pv', 'Ga': 'Ga_d', 'Gd': 'Gd', 'Ge': 'Ge_d', 'H': 'H', 'He': 'He', 'Hf': 'Hf_pv', 'Hg': 'Hg', 'Ho': 'Ho_3', 'I': 'I', 'In': 'In_d', 'Ir': 'Ir', 'K': 'K_sv', 'Kr': 'Kr', 'La': 'La', 'Li': 'Li_sv', 'Lu': 'Lu_3', 'Mg': 'Mg_pv', 'Mn': 'Mn_pv', 'Mo': 'Mo_pv', 'N': 'N', 'Na': 'Na_pv', 'Nb': 'Nb_pv', 'Nd': 'Nd_3', 'Ne': 'Ne', 'Ni': 'Ni_pv', 'Np': 'Np', 'O': 'O', 'Os': 'Os_pv', 'P': 'P', 'Pa': 'Pa', 'Pb': 'Pb_d', 'Pd': 'Pd', 'Pm': 'Pm_3', 'Pr': 'Pr_3', 'Pt': 'Pt', 'Pu': 'Pu', 'Rb': 'Rb_sv', 'Re': 'Re_pv', 'Rh': 'Rh_pv', 'Ru': 'Ru_pv', 'S': 'S', 'Sb': 'Sb', 'Sc': 'Sc_sv', 'Se': 'Se', 'Si': 'Si', 'Sm': 'Sm_3', 'Sn': 'Sn_d', 'Sr': 'Sr_sv', 'Ta': 'Ta_pv', 'Tb': 'Tb_3', 'Tc': 'Tc_pv', 'Te': 'Te', 'Th': 'Th', 'Ti': 'Ti_pv', 'Tl': 'Tl_d', 'Tm': 'Tm_3', 'U': 'U', 'V': 'V_pv', 'W': 'W_pv', 'Xe': 'Xe', 'Y': 'Y_sv', 'Yb': 'Yb_2', 'Zn': 'Zn', 'Zr': 'Zr_sv'}, 'POTCAR_FUNCTIONAL': 'PBE'}[source]
copy_chgcar: bool = True[source]
dedos: float = 0.005[source]
property incar_updates: dict[source]

Get updates to the INCAR config for this calculation type.

inherit_incar: bool = True[source]
kpoints_line_density: float = 20[source]
property kpoints_updates: dict[source]

Get updates to the kpoints configuration for this calculation type.

mode: str = 'line'[source]
nbands_factor: float = 1.2[source]
nedos: int = 2001[source]
optics: bool = False[source]
reciprocal_density: float = 100[source]
small_gap_multiply: tuple[float, float] | None = None[source]
class MPRelaxSet(structure: ~pymatgen.core.structure.Structure | None = <property object>, config_dict: dict = <factory>, files_to_transfer: dict = <factory>, user_incar_settings: dict = <factory>, user_kpoints_settings: dict = <factory>, user_potcar_settings: dict = <factory>, constrain_total_magmom: bool = False, sort_structure: bool = True, user_potcar_functional: ~typing.Literal['PBE', 'PBE_52', 'PBE_54', 'LDA', 'LDA_52', 'LDA_54', 'PW91', 'LDA_US', 'PW91_US'] | None = None, force_gamma: bool = False, reduce_structure: ~typing.Literal['niggli', 'LLL'] | None = None, vdw: str | None = None, use_structure_charge: bool = False, standardize: bool = False, sym_prec: float = 0.1, international_monoclinic: bool = True, validate_magmom: bool = True, inherit_incar: bool | list[str] = False, auto_ismear: bool = False, bandgap_tol: float = 0.0001, bandgap: float | None = None, prev_incar: str | dict | None = None, prev_kpoints: str | ~pymatgen.io.vasp.inputs.Kpoints | None = None)[source]

Bases: DictSet

Implementation of VaspInputSet utilizing parameters in the public Materials Project. Typically, the pseudopotentials chosen contain more electrons than the MIT parameters, and the k-point grid is ~50% more dense. The LDAUU parameters are also different due to the different PSPs used, which result in different fitted values.

Parameters:
  • structure (Structure) – The Structure to create inputs for. If None, the input set is initialized without a Structure but one must be set separately before the inputs are generated.

  • **kwargs – Same as those supported by DictSet.

CONFIG = {'INCAR': {'ALGO': 'FAST', 'EDIFF_PER_ATOM': 5e-05, 'ENCUT': 520, 'IBRION': 2, 'ISIF': 3, 'ISMEAR': -5, 'ISPIN': 2, 'LASPH': True, 'LDAU': True, 'LDAUJ': {'F': {'Co': 0, 'Cr': 0, 'Fe': 0, 'Mn': 0, 'Mo': 0, 'Ni': 0, 'V': 0, 'W': 0}, 'O': {'Co': 0, 'Cr': 0, 'Fe': 0, 'Mn': 0, 'Mo': 0, 'Ni': 0, 'V': 0, 'W': 0}}, 'LDAUL': {'F': {'Co': 2, 'Cr': 2, 'Fe': 2, 'Mn': 2, 'Mo': 2, 'Ni': 2, 'V': 2, 'W': 2}, 'O': {'Co': 2, 'Cr': 2, 'Fe': 2, 'Mn': 2, 'Mo': 2, 'Ni': 2, 'V': 2, 'W': 2}}, 'LDAUPRINT': 1, 'LDAUTYPE': 2, 'LDAUU': {'F': {'Co': 3.32, 'Cr': 3.7, 'Fe': 5.3, 'Mn': 3.9, 'Mo': 4.38, 'Ni': 6.2, 'V': 3.25, 'W': 6.2}, 'O': {'Co': 3.32, 'Cr': 3.7, 'Fe': 5.3, 'Mn': 3.9, 'Mo': 4.38, 'Ni': 6.2, 'V': 3.25, 'W': 6.2}}, 'LORBIT': 11, 'LREAL': 'AUTO', 'LWAVE': False, 'MAGMOM': {'Ce': 5, 'Ce3+': 1, 'Co': 0.6, 'Co3+': 0.6, 'Co4+': 1, 'Cr': 5, 'Dy3+': 5, 'Er3+': 3, 'Eu': 10, 'Eu2+': 7, 'Eu3+': 6, 'Fe': 5, 'Gd3+': 7, 'Ho3+': 4, 'La3+': 0.6, 'Lu3+': 0.6, 'Mn': 5, 'Mn3+': 4, 'Mn4+': 3, 'Mo': 5, 'Nd3+': 3, 'Ni': 5, 'Pm3+': 4, 'Pr3+': 2, 'Sm3+': 5, 'Tb3+': 6, 'Tm3+': 2, 'V': 5, 'W': 5, 'Yb3+': 1}, 'NELM': 100, 'NSW': 99, 'PREC': 'Accurate', 'SIGMA': 0.05}, 'KPOINTS': {'reciprocal_density': 64}, 'PARENT': 'VASPIncarBase', 'POTCAR': {'Ac': 'Ac', 'Ag': 'Ag', 'Al': 'Al', 'Ar': 'Ar', 'As': 'As', 'Au': 'Au', 'B': 'B', 'Ba': 'Ba_sv', 'Be': 'Be_sv', 'Bi': 'Bi', 'Br': 'Br', 'C': 'C', 'Ca': 'Ca_sv', 'Cd': 'Cd', 'Ce': 'Ce', 'Cl': 'Cl', 'Co': 'Co', 'Cr': 'Cr_pv', 'Cs': 'Cs_sv', 'Cu': 'Cu_pv', 'Dy': 'Dy_3', 'Er': 'Er_3', 'Eu': 'Eu', 'F': 'F', 'Fe': 'Fe_pv', 'Ga': 'Ga_d', 'Gd': 'Gd', 'Ge': 'Ge_d', 'H': 'H', 'He': 'He', 'Hf': 'Hf_pv', 'Hg': 'Hg', 'Ho': 'Ho_3', 'I': 'I', 'In': 'In_d', 'Ir': 'Ir', 'K': 'K_sv', 'Kr': 'Kr', 'La': 'La', 'Li': 'Li_sv', 'Lu': 'Lu_3', 'Mg': 'Mg_pv', 'Mn': 'Mn_pv', 'Mo': 'Mo_pv', 'N': 'N', 'Na': 'Na_pv', 'Nb': 'Nb_pv', 'Nd': 'Nd_3', 'Ne': 'Ne', 'Ni': 'Ni_pv', 'Np': 'Np', 'O': 'O', 'Os': 'Os_pv', 'P': 'P', 'Pa': 'Pa', 'Pb': 'Pb_d', 'Pd': 'Pd', 'Pm': 'Pm_3', 'Pr': 'Pr_3', 'Pt': 'Pt', 'Pu': 'Pu', 'Rb': 'Rb_sv', 'Re': 'Re_pv', 'Rh': 'Rh_pv', 'Ru': 'Ru_pv', 'S': 'S', 'Sb': 'Sb', 'Sc': 'Sc_sv', 'Se': 'Se', 'Si': 'Si', 'Sm': 'Sm_3', 'Sn': 'Sn_d', 'Sr': 'Sr_sv', 'Ta': 'Ta_pv', 'Tb': 'Tb_3', 'Tc': 'Tc_pv', 'Te': 'Te', 'Th': 'Th', 'Ti': 'Ti_pv', 'Tl': 'Tl_d', 'Tm': 'Tm_3', 'U': 'U', 'V': 'V_pv', 'W': 'W_pv', 'Xe': 'Xe', 'Y': 'Y_sv', 'Yb': 'Yb_2', 'Zn': 'Zn', 'Zr': 'Zr_sv'}, 'POTCAR_FUNCTIONAL': 'PBE'}[source]
class MPSOCSet(structure: Structure | None = <property object>, config_dict: dict = <factory>, files_to_transfer: dict = <factory>, user_incar_settings: dict = <factory>, user_kpoints_settings: dict = <factory>, user_potcar_settings: dict = <factory>, constrain_total_magmom: bool = False, sort_structure: bool = True, user_potcar_functional: UserPotcarFunctional = None, force_gamma: bool = False, reduce_structure: Literal['niggli', 'LLL'] | None = None, vdw: str | None = None, use_structure_charge: bool = False, standardize: bool = False, sym_prec: float = 0.1, international_monoclinic: bool = True, validate_magmom: bool = True, inherit_incar: bool = True, auto_ismear: bool = False, bandgap_tol: float = 0.0001, bandgap: float | None = None, prev_incar: str | dict | None = None, prev_kpoints: str | Kpoints | None = None, saxis: tuple[int, int, int] = (0, 0, 1), nbands_factor: float = 1.2, lepsilon: bool = False, lcalcpol: bool = False, reciprocal_density: float = 100, small_gap_multiply: tuple[float, float] | None = None, magmom: list[Vector3D] | None = None, copy_chgcar: bool = True)[source]

Bases: DictSet

An input set for running spin-orbit coupling (SOC) calculations.

Parameters:
  • structure (Structure) – the structure must have the ‘magmom’ site property and each magnetic moment value must have 3 components. eg: magmom = [[0,0,2], ...]

  • saxis (tuple) – magnetic moment orientation

  • copy_chgcar – Whether to copy the old CHGCAR. Defaults to True.

  • nbands_factor (float) – Multiplicative factor for NBANDS. Choose a higher number if you are doing an LOPTICS calculation.

  • reciprocal_density (int) – density of k-mesh by reciprocal volume.

  • small_gap_multiply ([float, float]) – If the gap is less than 1st index, multiply the default reciprocal_density by the 2nd index.

  • lepsilon (bool) – Whether to add static dielectric calculation

  • lcalcpol (bool) – Whether to turn on evaluation of the Berry phase approximations for electronic polarization

  • magmom (list[list[float]]) – Override for the structure magmoms.

  • **kwargs – kwargs supported by DictSet.

CONFIG = {'INCAR': {'ALGO': 'FAST', 'EDIFF_PER_ATOM': 5e-05, 'ENCUT': 520, 'IBRION': 2, 'ISIF': 3, 'ISMEAR': -5, 'ISPIN': 2, 'LASPH': True, 'LDAU': True, 'LDAUJ': {'F': {'Co': 0, 'Cr': 0, 'Fe': 0, 'Mn': 0, 'Mo': 0, 'Ni': 0, 'V': 0, 'W': 0}, 'O': {'Co': 0, 'Cr': 0, 'Fe': 0, 'Mn': 0, 'Mo': 0, 'Ni': 0, 'V': 0, 'W': 0}}, 'LDAUL': {'F': {'Co': 2, 'Cr': 2, 'Fe': 2, 'Mn': 2, 'Mo': 2, 'Ni': 2, 'V': 2, 'W': 2}, 'O': {'Co': 2, 'Cr': 2, 'Fe': 2, 'Mn': 2, 'Mo': 2, 'Ni': 2, 'V': 2, 'W': 2}}, 'LDAUPRINT': 1, 'LDAUTYPE': 2, 'LDAUU': {'F': {'Co': 3.32, 'Cr': 3.7, 'Fe': 5.3, 'Mn': 3.9, 'Mo': 4.38, 'Ni': 6.2, 'V': 3.25, 'W': 6.2}, 'O': {'Co': 3.32, 'Cr': 3.7, 'Fe': 5.3, 'Mn': 3.9, 'Mo': 4.38, 'Ni': 6.2, 'V': 3.25, 'W': 6.2}}, 'LORBIT': 11, 'LREAL': 'AUTO', 'LWAVE': False, 'MAGMOM': {'Ce': 5, 'Ce3+': 1, 'Co': 0.6, 'Co3+': 0.6, 'Co4+': 1, 'Cr': 5, 'Dy3+': 5, 'Er3+': 3, 'Eu': 10, 'Eu2+': 7, 'Eu3+': 6, 'Fe': 5, 'Gd3+': 7, 'Ho3+': 4, 'La3+': 0.6, 'Lu3+': 0.6, 'Mn': 5, 'Mn3+': 4, 'Mn4+': 3, 'Mo': 5, 'Nd3+': 3, 'Ni': 5, 'Pm3+': 4, 'Pr3+': 2, 'Sm3+': 5, 'Tb3+': 6, 'Tm3+': 2, 'V': 5, 'W': 5, 'Yb3+': 1}, 'NELM': 100, 'NSW': 99, 'PREC': 'Accurate', 'SIGMA': 0.05}, 'KPOINTS': {'reciprocal_density': 64}, 'PARENT': 'VASPIncarBase', 'POTCAR': {'Ac': 'Ac', 'Ag': 'Ag', 'Al': 'Al', 'Ar': 'Ar', 'As': 'As', 'Au': 'Au', 'B': 'B', 'Ba': 'Ba_sv', 'Be': 'Be_sv', 'Bi': 'Bi', 'Br': 'Br', 'C': 'C', 'Ca': 'Ca_sv', 'Cd': 'Cd', 'Ce': 'Ce', 'Cl': 'Cl', 'Co': 'Co', 'Cr': 'Cr_pv', 'Cs': 'Cs_sv', 'Cu': 'Cu_pv', 'Dy': 'Dy_3', 'Er': 'Er_3', 'Eu': 'Eu', 'F': 'F', 'Fe': 'Fe_pv', 'Ga': 'Ga_d', 'Gd': 'Gd', 'Ge': 'Ge_d', 'H': 'H', 'He': 'He', 'Hf': 'Hf_pv', 'Hg': 'Hg', 'Ho': 'Ho_3', 'I': 'I', 'In': 'In_d', 'Ir': 'Ir', 'K': 'K_sv', 'Kr': 'Kr', 'La': 'La', 'Li': 'Li_sv', 'Lu': 'Lu_3', 'Mg': 'Mg_pv', 'Mn': 'Mn_pv', 'Mo': 'Mo_pv', 'N': 'N', 'Na': 'Na_pv', 'Nb': 'Nb_pv', 'Nd': 'Nd_3', 'Ne': 'Ne', 'Ni': 'Ni_pv', 'Np': 'Np', 'O': 'O', 'Os': 'Os_pv', 'P': 'P', 'Pa': 'Pa', 'Pb': 'Pb_d', 'Pd': 'Pd', 'Pm': 'Pm_3', 'Pr': 'Pr_3', 'Pt': 'Pt', 'Pu': 'Pu', 'Rb': 'Rb_sv', 'Re': 'Re_pv', 'Rh': 'Rh_pv', 'Ru': 'Ru_pv', 'S': 'S', 'Sb': 'Sb', 'Sc': 'Sc_sv', 'Se': 'Se', 'Si': 'Si', 'Sm': 'Sm_3', 'Sn': 'Sn_d', 'Sr': 'Sr_sv', 'Ta': 'Ta_pv', 'Tb': 'Tb_3', 'Tc': 'Tc_pv', 'Te': 'Te', 'Th': 'Th', 'Ti': 'Ti_pv', 'Tl': 'Tl_d', 'Tm': 'Tm_3', 'U': 'U', 'V': 'V_pv', 'W': 'W_pv', 'Xe': 'Xe', 'Y': 'Y_sv', 'Yb': 'Yb_2', 'Zn': 'Zn', 'Zr': 'Zr_sv'}, 'POTCAR_FUNCTIONAL': 'PBE'}[source]
copy_chgcar: bool = True[source]
property incar_updates: dict[source]

Get updates to the INCAR config for this calculation type.

inherit_incar: bool = True[source]
property kpoints_updates: dict[source]

Get updates to the kpoints configuration for this calculation type.

lcalcpol: bool = False[source]
lepsilon: bool = False[source]
magmom: list[Vector3D] | None = None[source]
nbands_factor: float = 1.2[source]
reciprocal_density: float = 100[source]
saxis: tuple[int, int, int] = (0, 0, 1)[source]
small_gap_multiply: tuple[float, float] | None = None[source]
property structure: Structure[source]

Structure

class MPScanRelaxSet(structure: ~pymatgen.core.structure.Structure | None = <property object>, config_dict: dict = <factory>, files_to_transfer: dict = <factory>, user_incar_settings: dict = <factory>, user_kpoints_settings: dict = <factory>, user_potcar_settings: dict = <factory>, constrain_total_magmom: bool = False, sort_structure: bool = True, user_potcar_functional: ~typing.Literal['PBE', 'PBE_52', 'PBE_54', 'LDA', 'LDA_52', 'LDA_54', 'PW91', 'LDA_US', 'PW91_US'] | None = 'PBE_54', force_gamma: bool = False, reduce_structure: ~typing.Literal['niggli', 'LLL'] | None = None, vdw: str | None = None, use_structure_charge: bool = False, standardize: bool = False, sym_prec: float = 0.1, international_monoclinic: bool = True, validate_magmom: bool = True, inherit_incar: bool | list[str] = False, auto_ismear: bool = True, bandgap_tol: float = 0.0001, bandgap: float | None = None, prev_incar: str | dict | None = None, prev_kpoints: str | ~pymatgen.io.vasp.inputs.Kpoints | None = None)[source]

Bases: DictSet

Class for writing a relaxation input set using the accurate and numerically efficient r2SCAN variant of the Strongly Constrained and Appropriately Normed (SCAN) metaGGA density functional.

Notes

1. This functional is officially supported in VASP 6.0.0 and above. On older version, source code may be obtained by contacting the authors of the referenced manuscript. The original SCAN functional, available from VASP 5.4.3 onwards, maybe used instead by passing user_incar_settings={“METAGGA”: “SCAN”} when instantiating this InputSet. r2SCAN and SCAN are expected to yield very similar results.

2. Meta-GGA calculations require POTCAR files that include information on the kinetic energy density of the core-electrons, i.e. “PBE_52” or “PBE_54”. Make sure the POTCARs include the following lines (see VASP wiki for more details):

$ grep kinetic POTCAR kinetic energy-density mkinetic energy-density pseudized kinetic energy density (partial)

Parameters:
  • bandgap (float) – Bandgap of the structure in eV. The bandgap is used to compute the appropriate k-point density and determine the smearing settings. Metallic systems (default, bandgap = 0) use a KSPACING value of 0.22 and Methfessel-Paxton order 2 smearing (ISMEAR=2, SIGMA=0.2). Non-metallic systems (bandgap > 0) use the tetrahedron smearing method (ISMEAR=-5, SIGMA=0.05). The KSPACING value is calculated from the bandgap via Eqs. 25 and 29 of Wisesa, McGill, and Mueller [1] (see References). Note that if ‘user_incar_settings’ or ‘user_kpoints_settings’ override KSPACING, the calculation from bandgap is not performed.

  • vdw (str) – set “rVV10” to enable SCAN+rVV10, which is a versatile van der Waals density functional by combing the SCAN functional with the rVV10 non-local correlation functional. rvv10 is the only dispersion correction available for SCAN at this time.

  • **kwargs – Same as those supported by DictSet.

  • References – [1] P. Wisesa, K.A. McGill, T. Mueller, Efficient generation of generalized Monkhorst-Pack grids through the use of informatics, Phys. Rev. B. 93 (2016) 1-10. doi:10.1103/PhysRevB.93.155109.

References

James W. Furness, Aaron D. Kaplan, Jinliang Ning, John P. Perdew, and Jianwei Sun. Accurate and Numerically Efficient r2SCAN Meta-Generalized Gradient Approximation. The Journal of Physical Chemistry Letters 0, 11 DOI: 10.1021/acs.jpclett.0c02405

CONFIG = {'INCAR': {'ALGO': 'ALL', 'EDIFF': 1e-05, 'EDIFFG': -0.02, 'ENAUG': 1360, 'ENCUT': 680, 'IBRION': 2, 'ISIF': 3, 'ISMEAR': 0, 'ISPIN': 2, 'KSPACING': 0.22, 'LAECHG': True, 'LASPH': True, 'LCHARG': True, 'LELF': False, 'LMIXTAU': True, 'LORBIT': 11, 'LREAL': 'Auto', 'LVTOT': True, 'LWAVE': False, 'MAGMOM': {'Ce': 5, 'Ce3+': 1, 'Co': 0.6, 'Co3+': 0.6, 'Co4+': 1, 'Cr': 5, 'Dy3+': 5, 'Er3+': 3, 'Eu': 10, 'Eu2+': 7, 'Eu3+': 6, 'Fe': 5, 'Gd3+': 7, 'Ho3+': 4, 'La3+': 0.6, 'Lu3+': 0.6, 'Mn': 5, 'Mn3+': 4, 'Mn4+': 3, 'Mo': 5, 'Nd3+': 3, 'Ni': 5, 'Pm3+': 4, 'Pr3+': 2, 'Sm3+': 5, 'Tb3+': 6, 'Tm3+': 2, 'V': 5, 'W': 5, 'Yb3+': 1}, 'METAGGA': 'R2SCAN', 'NELM': 200, 'NSW': 99, 'PREC': 'Accurate', 'SIGMA': 0.05}, 'PARENT': 'VASPIncarBase', 'POTCAR': {'Ac': 'Ac', 'Ag': 'Ag', 'Al': 'Al', 'Am': 'Am', 'Ar': 'Ar', 'As': 'As', 'At': 'At', 'Au': 'Au', 'B': 'B', 'Ba': 'Ba_sv', 'Be': 'Be_sv', 'Bi': 'Bi', 'Br': 'Br', 'C': 'C', 'Ca': 'Ca_sv', 'Cd': 'Cd', 'Ce': 'Ce', 'Cf': 'Cf', 'Cl': 'Cl', 'Cm': 'Cm', 'Co': 'Co', 'Cr': 'Cr_pv', 'Cs': 'Cs_sv', 'Cu': 'Cu_pv', 'Dy': 'Dy_3', 'Er': 'Er_3', 'Eu': 'Eu', 'F': 'F', 'Fe': 'Fe_pv', 'Fr': 'Fr_sv', 'Ga': 'Ga_d', 'Gd': 'Gd', 'Ge': 'Ge_d', 'H': 'H', 'He': 'He', 'Hf': 'Hf_pv', 'Hg': 'Hg', 'Ho': 'Ho_3', 'I': 'I', 'In': 'In_d', 'Ir': 'Ir', 'K': 'K_sv', 'Kr': 'Kr', 'La': 'La', 'Li': 'Li_sv', 'Lu': 'Lu_3', 'Mg': 'Mg_pv', 'Mn': 'Mn_pv', 'Mo': 'Mo_pv', 'N': 'N', 'Na': 'Na_pv', 'Nb': 'Nb_pv', 'Nd': 'Nd_3', 'Ne': 'Ne', 'Ni': 'Ni_pv', 'Np': 'Np', 'O': 'O', 'Os': 'Os_pv', 'P': 'P', 'Pa': 'Pa', 'Pb': 'Pb_d', 'Pd': 'Pd', 'Pm': 'Pm_3', 'Po': 'Po_d', 'Pr': 'Pr_3', 'Pt': 'Pt', 'Pu': 'Pu', 'Ra': 'Ra_sv', 'Rb': 'Rb_sv', 'Re': 'Re_pv', 'Rh': 'Rh_pv', 'Rn': 'Rn', 'Ru': 'Ru_pv', 'S': 'S', 'Sb': 'Sb', 'Sc': 'Sc_sv', 'Se': 'Se', 'Si': 'Si', 'Sm': 'Sm_3', 'Sn': 'Sn_d', 'Sr': 'Sr_sv', 'Ta': 'Ta_pv', 'Tb': 'Tb_3', 'Tc': 'Tc_pv', 'Te': 'Te', 'Th': 'Th', 'Ti': 'Ti_pv', 'Tl': 'Tl_d', 'Tm': 'Tm_3', 'U': 'U', 'V': 'V_pv', 'W': 'W_sv', 'Xe': 'Xe', 'Y': 'Y_sv', 'Yb': 'Yb_3', 'Zn': 'Zn', 'Zr': 'Zr_sv'}, 'POTCAR_FUNCTIONAL': 'PBE_54'}[source]
auto_ismear: bool = True[source]
bandgap: float | None = None[source]
property incar_updates: dict[source]

Get updates to the INCAR config for this calculation type.

user_potcar_functional: UserPotcarFunctional = 'PBE_54'[source]
class MPScanStaticSet(structure: ~pymatgen.core.structure.Structure | None = <property object>, config_dict: dict = <factory>, files_to_transfer: dict = <factory>, user_incar_settings: dict = <factory>, user_kpoints_settings: dict = <factory>, user_potcar_settings: dict = <factory>, constrain_total_magmom: bool = False, sort_structure: bool = True, user_potcar_functional: ~typing.Literal['PBE', 'PBE_52', 'PBE_54', 'LDA', 'LDA_52', 'LDA_54', 'PW91', 'LDA_US', 'PW91_US'] | None = 'PBE_54', force_gamma: bool = False, reduce_structure: ~typing.Literal['niggli', 'LLL'] | None = None, vdw: str | None = None, use_structure_charge: bool = False, standardize: bool = False, sym_prec: float = 0.1, international_monoclinic: bool = True, validate_magmom: bool = True, inherit_incar: bool = True, auto_ismear: bool = True, bandgap_tol: float = 0.0001, bandgap: float | None = None, prev_incar: str | dict | None = None, prev_kpoints: str | ~pymatgen.io.vasp.inputs.Kpoints | None = None, lepsilon: bool = False, lcalcpol: bool = False)[source]

Bases: MPScanRelaxSet

Creates input files for a static calculation using the accurate and numerically efficient r2SCAN variant of the Strongly Constrained and Appropriately Normed (SCAN) metaGGA functional.

Parameters:
  • structure (Structure) – Structure from previous run.

  • bandgap (float) – Bandgap of the structure in eV. The bandgap is used to compute the appropriate k-point density and determine the smearing settings.

  • lepsilon (bool) – Whether to add static dielectric calculation

  • lcalcpol (bool) – Whether to turn on evaluation of the Berry phase approximations for electronic polarization.

  • **kwargs – kwargs supported by MPScanRelaxSet.

property incar_updates: dict[source]

Get updates to the INCAR config for this calculation type.

inherit_incar: bool = True[source]
lcalcpol: bool = False[source]
lepsilon: bool = False[source]
class MPStaticSet(structure: ~pymatgen.core.structure.Structure | None = <property object>, config_dict: dict = <factory>, files_to_transfer: dict = <factory>, user_incar_settings: dict = <factory>, user_kpoints_settings: dict = <factory>, user_potcar_settings: dict = <factory>, constrain_total_magmom: bool = False, sort_structure: bool = True, user_potcar_functional: ~typing.Literal['PBE', 'PBE_52', 'PBE_54', 'LDA', 'LDA_52', 'LDA_54', 'PW91', 'LDA_US', 'PW91_US'] | None = None, force_gamma: bool = False, reduce_structure: ~typing.Literal['niggli', 'LLL'] | None = None, vdw: str | None = None, use_structure_charge: bool = False, standardize: bool = False, sym_prec: float = 0.1, international_monoclinic: bool = True, validate_magmom: bool = True, inherit_incar: bool = True, auto_ismear: bool = False, bandgap_tol: float = 0.0001, bandgap: float | None = None, prev_incar: str | dict | None = None, prev_kpoints: str | ~pymatgen.io.vasp.inputs.Kpoints | None = None, lepsilon: bool = False, lcalcpol: bool = False, reciprocal_density: int = 100, small_gap_multiply: tuple[float, float] | None = None)[source]

Bases: DictSet

Creates input files for a static calculation.

Parameters:
  • structure (Structure) – Structure from previous run.

  • lepsilon (bool) – Whether to add static dielectric calculation

  • lcalcpol (bool) – Whether to turn on evaluation of the Berry phase approximations for electronic polarization

  • reciprocal_density (int) – For static calculations, we usually set the reciprocal density by volume. This is a convenience arg to change that, rather than using user_kpoints_settings. Defaults to 100, which is ~50% more than that of standard relaxation calculations.

  • small_gap_multiply ([float, float]) – If the gap is less than 1st index, multiply the default reciprocal_density by the 2nd index.

  • **kwargs – kwargs supported by MPRelaxSet.

CONFIG = {'INCAR': {'ALGO': 'FAST', 'EDIFF_PER_ATOM': 5e-05, 'ENCUT': 520, 'IBRION': 2, 'ISIF': 3, 'ISMEAR': -5, 'ISPIN': 2, 'LASPH': True, 'LDAU': True, 'LDAUJ': {'F': {'Co': 0, 'Cr': 0, 'Fe': 0, 'Mn': 0, 'Mo': 0, 'Ni': 0, 'V': 0, 'W': 0}, 'O': {'Co': 0, 'Cr': 0, 'Fe': 0, 'Mn': 0, 'Mo': 0, 'Ni': 0, 'V': 0, 'W': 0}}, 'LDAUL': {'F': {'Co': 2, 'Cr': 2, 'Fe': 2, 'Mn': 2, 'Mo': 2, 'Ni': 2, 'V': 2, 'W': 2}, 'O': {'Co': 2, 'Cr': 2, 'Fe': 2, 'Mn': 2, 'Mo': 2, 'Ni': 2, 'V': 2, 'W': 2}}, 'LDAUPRINT': 1, 'LDAUTYPE': 2, 'LDAUU': {'F': {'Co': 3.32, 'Cr': 3.7, 'Fe': 5.3, 'Mn': 3.9, 'Mo': 4.38, 'Ni': 6.2, 'V': 3.25, 'W': 6.2}, 'O': {'Co': 3.32, 'Cr': 3.7, 'Fe': 5.3, 'Mn': 3.9, 'Mo': 4.38, 'Ni': 6.2, 'V': 3.25, 'W': 6.2}}, 'LORBIT': 11, 'LREAL': 'AUTO', 'LWAVE': False, 'MAGMOM': {'Ce': 5, 'Ce3+': 1, 'Co': 0.6, 'Co3+': 0.6, 'Co4+': 1, 'Cr': 5, 'Dy3+': 5, 'Er3+': 3, 'Eu': 10, 'Eu2+': 7, 'Eu3+': 6, 'Fe': 5, 'Gd3+': 7, 'Ho3+': 4, 'La3+': 0.6, 'Lu3+': 0.6, 'Mn': 5, 'Mn3+': 4, 'Mn4+': 3, 'Mo': 5, 'Nd3+': 3, 'Ni': 5, 'Pm3+': 4, 'Pr3+': 2, 'Sm3+': 5, 'Tb3+': 6, 'Tm3+': 2, 'V': 5, 'W': 5, 'Yb3+': 1}, 'NELM': 100, 'NSW': 99, 'PREC': 'Accurate', 'SIGMA': 0.05}, 'KPOINTS': {'reciprocal_density': 64}, 'PARENT': 'VASPIncarBase', 'POTCAR': {'Ac': 'Ac', 'Ag': 'Ag', 'Al': 'Al', 'Ar': 'Ar', 'As': 'As', 'Au': 'Au', 'B': 'B', 'Ba': 'Ba_sv', 'Be': 'Be_sv', 'Bi': 'Bi', 'Br': 'Br', 'C': 'C', 'Ca': 'Ca_sv', 'Cd': 'Cd', 'Ce': 'Ce', 'Cl': 'Cl', 'Co': 'Co', 'Cr': 'Cr_pv', 'Cs': 'Cs_sv', 'Cu': 'Cu_pv', 'Dy': 'Dy_3', 'Er': 'Er_3', 'Eu': 'Eu', 'F': 'F', 'Fe': 'Fe_pv', 'Ga': 'Ga_d', 'Gd': 'Gd', 'Ge': 'Ge_d', 'H': 'H', 'He': 'He', 'Hf': 'Hf_pv',