pymatgen.command_line package

Submodules

pymatgen.command_line.bader_caller module

This module implements an interface to the Henkelmann et al.’s excellent Fortran code for calculating a Bader charge analysis.

This module depends on a compiled bader executable available in the path. Please download the library at https://theory.cm.utexas.edu/henkelman/code/bader/ and follow the instructions to compile the executable.

If you use this module, please cite:

G. Henkelman, A. Arnaldsson, and H. Jonsson, “A fast and robust algorithm for Bader decomposition of charge density”, Comput. Mater. Sci. 36, 254-360 (2006).

class BaderAnalysis(chgcar_filename: str = '', potcar_filename: str = '', chgref_filename: str = '', cube_filename: str = '', bader_path: str | None = None, parse_atomic_densities: bool = False)[source]

Bases: object

Perform Bader charge analysis for Cube files or VASP outputs.

data[source]

Atomic data parsed from bader analysis. Each dictionary in the list has the keys: “atomic_vol”, “min_dist”, “charge”, “x”, “y”, “z”.

Type:

list[dict]

vacuum_volume[source]

Vacuum volume of the Bader analysis.

Type:

float

vacuum_charge[source]

Vacuum charge of the Bader analysis.

Type:

float

nelectrons[source]

Number of electrons of the Bader analysis.

Type:

int

chgcar[source]

Chgcar object associated with input CHGCAR file.

Type:

Chgcar

atomic_densities[source]

List of charge densities for each atom centered on the atom. Each dictionary has the keys: “data”, “shift”, “dim”, where “data” is the charge density array, “shift” is the shift used to center the atomic charge density, and “dim” is the dimension of the original charge density.

Type:

list[dict]

Initialize the Bader caller.

Parameters:

• chgcar_filename (str) – The filename of the CHGCAR.

• potcar_filename (str) – The filename of the POTCAR.

• chgref_filename (str) – The filename of the reference charge density.

• cube_filename (str, optional) – The filename of the cube file.

• bader_path (str, optional) – The path to the bader executable.

• parse_atomic_densities (bool, optional) – Enable atomic partition of the charge density. Charge densities are atom centered. Defaults to False.

classmethod from_path(path: str, suffix: str = '') → Self[source]

Convenient constructor that takes in the path name of VASP run to perform Bader analysis.

Parameters:

• path (str) – Name of directory where VASP output files are stored.

• suffix (str) – specific suffix to look for (e.g. ‘.relax1’ for ‘CHGCAR.relax1.gz’).

Returns:

BaderAnalysis

get_charge(atom_index: int) → float[source]

Convenience method to get the charge on a particular atom. This is the “raw” charge generated by the Bader program, not a partial atomic charge. If the cube file is a spin-density file, then this will return the spin density per atom with positive being spin up and negative being spin down.

Parameters:

atom_index (int) – Index of atom.

Returns:

Charge associated with atom from the Bader analysis.

Return type:

float

get_charge_decorated_structure() → Structure | IStructure[source]

Get a charge decorated structure.

Note, this assumes that the Bader analysis was correctly performed on a file with electron densities

get_charge_transfer(atom_index: int, nelect: int | None = None) → float[source]

Get the charge transferred for a particular atom. A positive value means that the site has gained electron density (i.e. exhibits anionic character) whereas a negative value means the site has lost electron density (i.e. exhibits cationic character). If the arg nelect is not supplied, then POTCAR must be supplied to determine nelect.

Parameters:

• atom_index (int) – Index of atom.

• nelect (int) – number of electrons associated with an isolated atom at this index. For most DFT codes this corresponds to the number of valence electrons associated with the pseudopotential (e.g. ZVAL for VASP).

Returns:

Charge transfer associated with atom from the Bader analysis. Given by bader charge on atom - nelect for associated atom.

get_decorated_structure(property_name: str, average: bool = False) → Structure[source]

Get a property-decorated structure from the Bader analysis.

This is distinct from getting charge decorated structure, which assumes the “standard” Bader analysis of electron densities followed by converting electron count to charge. The expected way to use this is to call Bader on a non-charge density file such as a spin density file, electrostatic potential file, etc., while using the charge density file as the reference (chgref_filename) so that the partitioning is determined via the charge, but averaging or integrating is done for another property.

User warning: Bader analysis cannot automatically determine what property is inside of the file. So if you want to use this for a non-conventional property like spin, you must ensure that you have the file is for the appropriate property and you have an appropriate reference file.

Parameters:

• property_name (str) – name of the property to assign to the structure, note that if name is “spin” this is handled as a special case, and the appropriate spin properties are set on the species in the structure

• average (bool) – whether or not to return the average of this property, rather than the total, by dividing by the atomic volume.

Returns:

structure with site properties assigned via Bader Analysis

get_oxidation_state_decorated_structure(nelects: list[int] | None = None) → Structure[source]

Get an oxidation state decorated structure based on bader analysis results. Each site is assigned a charge based on the computed partial atomic charge from bader.

Note, this assumes that the Bader analysis was correctly performed on a file with electron densities.

Parameters:

nelects (list[int]) – number of electrons associated with an isolated atom at this index.

Returns:

with bader-analysis-based oxidation states.

Return type:

Structure

get_partial_charge(atom_index: int, nelect: int | None = None) → float[source]

Convenience method to get the partial charge on a particular atom. This is simply the negative value of the charge transferred. A positive value indicates that the atom has cationic character, whereas a negative value indicates the site has anionic character.

Parameters:

• atom_index (int) – Index of atom.

• nelect (int) – number of electrons associated with an isolated atom at this index. For most DFT codes this corresponds to the number of valence electrons associated with the pseudopotential (e.g. ZVAL for VASP).

Returns:

Charge associated with atom from the Bader analysis.

property summary: dict[str, Any][source]

Dict summary of key analysis, e.g. atomic volume, charge, etc.

bader_analysis_from_objects(chgcar: Chgcar, potcar: Potcar | None = None, aeccar0: Chgcar | None = None, aeccar2: Chgcar | None = None) → dict[str, Any][source]

Convenience method to run Bader analysis from a set of pymatgen Chgcar and Potcar objects.

This method will:

1. If aeccar objects are present, constructs a temporary reference file as AECCAR0 + AECCAR2 2. Runs Bader analysis twice: once for charge, and a second time for the charge difference (magnetization density).

Parameters:

• chgcar – Chgcar object

• potcar – (optional) Potcar object

• aeccar0 – (optional) Chgcar object from aeccar0 file

• aeccar2 – (optional) Chgcar object from aeccar2 file

Returns:

summary dict

bader_analysis_from_path(path: str, suffix: str = '') → dict[str, Any][source]

Convenience method to run Bader analysis on a folder containing typical VASP output files.

This method will:

1. Look for files CHGCAR, AECCAR0, AECCAR2, POTCAR or their gzipped counterparts. 2. If AECCAR* files are present, constructs a temporary reference file as AECCAR0 + AECCAR2 3. Runs Bader analysis twice: once for charge, and a second time for the charge difference (magnetization density).

Parameters:

• path – path to folder to search in

• suffix – specific suffix to look for (e.g. ‘.relax1’ for ‘CHGCAR.relax1.gz’

Returns:

summary dict

pymatgen.command_line.chargemol_caller module

This module implements an interface to Thomas Manz’s Chargemol code https://sourceforge.net/projects/ddec for calculating DDEC3, DDEC6, and CM5 population analyses.

This module depends on a compiled chargemol executable being available in the path. If you use this module, please cite the following based on which modules you use:

Chargemol: (1) T. A. Manz and N. Gabaldon Limas, Chargemol program for performing DDEC analysis, Version 3.5, 2017, ddec.sourceforge.net.

DDEC6 Charges: (1) T. A. Manz and N. Gabaldon Limas, “Introducing DDEC6 atomic population analysis: part 1. Charge partitioning theory and methodology,” RSC Adv., 6 (2016) 47771-47801. (2) N. Gabaldon Limas and T. A. Manz, “Introducing DDEC6 atomic population analysis: part 2. Computed results for a wide range of periodic and nonperiodic materials,” (3) N. Gabaldon Limas and T. A. Manz, “Introducing DDEC6 atomic population analysis: part 4. Efficient parallel computation of net atomic charges, atomic spin moments, bond orders, and more,” RSC Adv., 8 (2018) 2678-2707.

CM5 Charges: (1) A.V. Marenich, S.V. Jerome, C.J. Cramer, D.G. Truhlar, “Charge Model 5: An Extension of Hirshfeld Population Analysis for the Accurate Description of Molecular Interactions in Gaseous and Condensed Phases”, J. Chem. Theory. Comput., 8 (2012) 527-541.

Spin Moments: (1) T. A. Manz and D. S. Sholl, “Methods for Computing Accurate Atomic Spin Moments for Collinear and Noncollinear Magnetism in Periodic and Nonperiodic Materials,” J. Chem. Theory Comput. 7 (2011) 4146-4164.

Bond Orders: (1) “Introducing DDEC6 atomic population analysis: part 3. Comprehensive method to compute bond orders,” RSC Adv., 7 (2017) 45552-45581.

DDEC3 Charges: (1) T. A. Manz and D. S. Sholl, “Improved Atoms-in-Molecule Charge Partitioning Functional for Simultaneously Reproducing the Electrostatic Potential and Chemical States in Periodic and Non-Periodic Materials,” J. Chem. Theory Comput. 8 (2012) 2844-2867. (2) T. A. Manz and D. S. Sholl, “Chemically Meaningful Atomic Charges that Reproduce the Electrostatic Potential in Periodic and Nonperiodic Materials,” J. Chem. Theory Comput. 6 (2010) 2455-2468.

class ChargemolAnalysis(path: str | Path | None = None, atomic_densities_path: str | Path | None = None, run_chargemol: bool = True)[source]

Bases: object

Chargemol analysis for DDEC3, DDEC6, and/or CM5 population analyses, including the calculation of partial atomic charges, atomic spin moments, bond orders, and related properties.

Initialize the Chargemol Analysis.

Parameters:

• path (str) – Path to the CHGCAR, POTCAR, AECCAR0, and AECCAR files. The files can be gzipped or not. Default: None (current working directory).

• atomic_densities_path (str | None) – Path to the atomic densities directory required by Chargemol. If None, Pymatgen assumes that this is defined in a “DDEC6_ATOMIC_DENSITIES_DIR” environment variable. Only used if run_chargemol is True. Default: None.

• run_chargemol (bool) – Whether to run the Chargemol analysis. If False, the existing Chargemol output files will be read from path. Default: True.

get_bond_order(index_from, index_to)[source]

Convenience method to get the bond order between two atoms.

Parameters:

• index_from (int) – Index of atom to get bond order from.

• index_to (int) – Index of atom to get bond order to.

Returns:

bond order between atoms

Return type:

float

get_charge(atom_index, nelect=None, charge_type: Literal['ddec', 'cm5'] = 'ddec')[source]

Convenience method to get the charge on a particular atom using the same sign convention as the BaderAnalysis. Note that this is not the partial atomic charge. This value is nelect (e.g. ZVAL from the POTCAR) + the charge transferred. If you want the partial atomic charge, use get_partial_charge().

Parameters:

• atom_index (int) – Index of atom to get charge for.

• nelect (int) – number of electrons associated with an isolated atom at this index.

• electrons (For most DFT codes this corresponds to the number of valence)

• None (associated with the pseudopotential. If)

• automatically (this value will be)

• POTCAR (obtained from the) – Default: None.

• charge_type (str) – Type of charge to use (“ddec” or “cm5”).

Returns:

charge on atom_index

Return type:

float

get_charge_transfer(atom_index, charge_type='ddec')[source]

Get the charge transferred for a particular atom. A positive value means that the site has gained electron density (i.e. exhibits anionic character) whereas a negative value means the site has lost electron density (i.e. exhibits cationic character). This is the same thing as the negative of the partial atomic charge.

Parameters:

• atom_index (int) – Index of atom to get charge transfer for.

• charge_type (str) – Type of charge to use (“ddec” or “cm5”).

Returns:

charge transferred at atom_index

Return type:

float

get_partial_charge(atom_index, charge_type: Literal['ddec', 'cm5'] = 'ddec')[source]

Convenience method to get the partial atomic charge on a particular atom. This is the value printed in the Chargemol analysis.

Parameters:

• atom_index (int) – Index of atom to get charge for.

• charge_type (str) – Type of charge to use (“ddec” or “cm5”).

get_property_decorated_structure()[source]

Takes CHGCAR’s structure object and updates it with properties from the Chargemol analysis.

Returns:

Pymatgen structure with site properties added

property summary[source]

A dictionary summary of the Chargemol analysis {

“ddec”: {

“partial_charges”: list[float], “spin_moments”: list[float], “dipoles”: list[float], “rsquared_moments”: list[float], “rcubed_moments”: list[float], “rfourth_moments”: list[float], “bond_order_dict”: dict

}, “cm5”: {

“partial_charges”: list[float],

}

}.

pymatgen.command_line.critic2_caller module

This module implements an interface to the critic2 Bader analysis code.

For most Bader analysis purposes, users are referred to pymatgen.command_line.bader_caller instead, this module is for advanced usage requiring identification of critical points in the charge density.

This module depends on a compiled critic2 executable available in the path. Please follow the instructions at https://github.com/aoterodelaroza/critic2 to compile.

New users are strongly encouraged to read the critic2 manual first.

In brief, * critic2 searches for critical points in charge density * a critical point can be one of four types: nucleus, bond, ring or cage * it does this by seeding locations for likely critical points and then searching in these regions * there are two lists of critical points in the output, a list of non-equivalent points (with in-depth information about the field at those points), and a full list of points generated by the appropriate symmetry operations * connectivity between these points is also provided when appropriate (e.g. the two nucleus critical points linked to

a bond critical point)

• critic2 can do many other things besides

If you use this module, please cite:

A. Otero-de-la-Roza, E. R. Johnson and V. Luaña, Comput. Phys. Communications 185, 1007-1018 (2014) (https://doi.org/10.1016/j.cpc.2013.10.026)

A. Otero-de-la-Roza, M. A. Blanco, A. Martín Pendás and V. Luaña, Comput. Phys. Communications 180, 157-166 (2009) (https://doi.org/10.1016/j.cpc.2008.07.018)

class Critic2Analysis(structure: Structure, stdout: str | None = None, stderr: str | None = None, cpreport: dict | None = None, yt: dict | None = None, zpsp: dict | None = None)[source]

Bases: MSONable

Process the standard output from critic2 into pymatgen-compatible objects.

This class is used to store results from the Critic2Caller.

To explore the bond graph, use the “structure_graph” method, which returns a user-friendly StructureGraph class with bonding information. By default, this returns a StructureGraph with edge weights as bond lengths, but can optionally return a graph with edge weights as any property supported by the CriticalPoint class, such as bond ellipticity.

This class also provides an interface to explore just the non-symmetrically-equivalent critical points via the critical_points attribute, and also all critical points (via nodes dict) and connections between them (via edges dict). The user should be familiar with critic2 before trying to understand these.

Indexes of nucleus critical points in the nodes dict are the same as the corresponding sites in structure, with indices of other critical points arbitrarily assigned.

Only one of (stdout, cpreport) required, with cpreport preferred since this is a new, native JSON output from critic2.

Parameters:

• structure (Structure) – associated Structure

• stdout (str, optional) – stdout from running critic2 in automatic mode

• stderr (str, optional) – stderr from running critic2 in automatic mode

• cpreport (dict, optional) – JSON output from CPREPORT command

• yt (dict, optional) – JSON output from YT command

• zpsp (dict, optional) – Dict of element/symbol name to number of electrons (ZVAL in VASP pseudopotential), with which to calculate charge transfer. Optional.

• structure – Associated Structure.

• stdout – stdout from running critic2 in automatic mode.

• stderr – stderr from running critic2 in automatic mode.

• cpreport – JSON output from CPREPORT command. Either this or stdout required.

• yt – JSON output from YT command.

• zpsp – Dict of element/symbol name to number of electrons (ZVAL in VASP pseudopotential), with which to calculate charge transfer. Optional.

Raises:

ValueError – If one of cpreport or stdout is not provided.

get_critical_point_for_site(n: int)[source]

Parameters:

n (int) – Site index.

Returns:

CriticalPoint

get_volume_and_charge_for_site(idx)[source]

Parameters:

idx – Site index.

Returns:

with “volume” and “charge” keys, or None if YT integration not performed

Return type:

dict

structure_graph(include_critical_points=('bond', 'ring', 'cage'))[source]

A StructureGraph object describing bonding information in the crystal.

Parameters:

include_critical_points – add DummySpecies for the critical points themselves, a list of “nucleus”, “bond”, “ring”, “cage”, set to None to disable

Returns:

StructureGraph

class Critic2Caller(input_script: str)[source]

Bases: object

Call critic2 and store standard output for further processing.

Run Critic2 on a given input script.

Parameters:

input_script – string defining the critic2 input

classmethod from_chgcar(structure, chgcar=None, chgcar_ref=None, user_input_settings=None, write_cml=False, write_json=True, zpsp=None) → Self[source]

Run Critic2 in automatic mode on a supplied structure, charge density (chgcar) and reference charge density (chgcar_ref).

The reason for a separate reference field is that in VASP, the CHGCAR charge density only contains valence electrons and may be missing substantial charge at nuclei leading to misleading results. Thus, a reference field is commonly constructed from the sum of AECCAR0 and AECCAR2 which is the total charge density, but then the valence charge density is used for the final analysis.

If chgcar_ref is not supplied, chgcar will be used as the reference field. If chgcar is not supplied, the promolecular charge density will be used as the reference field – this can often still give useful results if only topological information is wanted.

User settings is a dictionary that can contain: * GRADEPS, float (field units), gradient norm threshold * CPEPS, float (Bohr units in crystals), minimum distance between

critical points for them to be equivalent

• NUCEPS, same as CPEPS but specifically for nucleus critical points (critic2 default is dependent on grid dimensions)

• NUCEPSH, same as NUCEPS but specifically for hydrogen nuclei since associated charge density can be significantly displaced from hydrogen nucleus

• EPSDEGEN, float (field units), discard critical point if any element of the diagonal of the Hessian is below this value, useful for discarding points in vacuum regions

• DISCARD, float (field units), discard critical points with field value below this value, useful for discarding points in vacuum regions

• SEED, list of strings, strategies for seeding points, default is [‘WS 1’, ‘PAIR 10’] which seeds critical points by sub-dividing the Wigner-Seitz cell and between every atom pair closer than 10 Bohr, see critic2 manual for more options

Parameters:

• structure – Structure to analyze

• chgcar – Charge density to use for analysis. If None, will use promolecular density. Should be a Chgcar object or path (string).

• chgcar_ref – Reference charge density. If None, will use chgcar as reference. Should be a Chgcar object or path (string).

• user_input_settings (dict) – as explained above

• write_cml (bool) – Useful for debug, if True will write all critical points to a file ‘table.cml’ in the working directory useful for visualization

• write_json (bool) – Whether to write out critical points and YT JSON. YT integration will be performed with this setting.

• zpsp (dict) – Dict of element/symbol name to number of electrons (ZVAL in VASP pseudopotential), with which to properly augment core regions and calculate charge transfer. Optional.

classmethod from_path(path, suffix='', zpsp=None) → Self[source]

Convenience method to run critic2 analysis on a folder with typical VASP output files.

This method will:

1. Look for files CHGCAR, AECAR0, AECAR2, POTCAR or their gzipped counterparts.

2. If AECCAR* files are present, constructs a temporary reference file as AECCAR0 + AECCAR2.

3. Runs critic2 analysis twice: once for charge, and a second time for the charge difference (magnetization density).

Parameters:

• path – path to folder to search in

• suffix – specific suffix to look for (e.g. ‘.relax1’ for ‘CHGCAR.relax1.gz’)

• zpsp – manually specify ZPSP if POTCAR not present

class CriticalPoint(index, type, frac_coords, point_group, multiplicity, field, field_gradient, coords=None, field_hessian=None)[source]

Bases: MSONable

Access information about a critical point and the field values at that point.

Characterize a critical point from a topological analysis of electron charge density.

Note this class is usually associated with a Structure, so has information on multiplicity/point group symmetry.

Parameters:

• index – index of point

• type – type of point, given as a string

• coords – Cartesian coordinates in Angstroms

• frac_coords – fractional coordinates

• point_group – point group associated with critical point

• multiplicity – number of equivalent critical points

• field – value of field at point (f)

• field_gradient – gradient of field at point (grad f)

• field_hessian – hessian of field at point (del^2 f)

property ellipticity[source]

Most meaningful for bond critical points, can be physically interpreted as e.g. degree of pi-bonding in organic molecules. Consult literature for more info.

Returns:

The ellipticity of the field at the critical point.

Return type:

float

property laplacian: float[source]

The Laplacian of the field at the critical point.

property type: CriticalPointType[source]

Instance of CriticalPointType.

class CriticalPointType(value)[source]

Bases: Enum

Enum type for the different varieties of critical point.

bond = 'bond'[source]

cage = 'cage'[source]

nnattr = 'nnattr'[source]

nucleus = 'nucleus'[source]

ring = 'ring'[source]

get_filepath(filename, warning, path, suffix)[source]

Parameters:

• filename – Filename

• warning – Warning message

• path – Path to search

• suffix – Suffixes to search.

pymatgen.command_line.enumlib_caller module

This module implements an interface to enumlib, Gus Hart’s excellent Fortran code for enumerating derivative structures.

This module depends on a compiled enumlib with the executables enum.x and makestr.x available in the path. Please download the library at https://github.com/msg-byu/enumlib and follow the instructions in the README to compile these two executables accordingly.

If you use this module, please cite:

• Gus L. W. Hart and Rodney W. Forcade, “Algorithm for generating derivative

structures,” Phys. Rev. B 77 224115 (26 June 2008)

• Gus L. W. Hart and Rodney W. Forcade, “Generating derivative structures from

multilattices: Application to hcp alloys,” Phys. Rev. B 80 014120 (July 2009)

• Gus L. W. Hart, Lance J. Nelson, and Rodney W. Forcade, “Generating

derivative structures at a fixed concentration,” Comp. Mat. Sci. 59 101-107 (March 2012)

• Wiley S. Morgan, Gus L. W. Hart, Rodney W. Forcade, “Generating derivative

superstructures for systems with high configurational freedom,” Comp. Mat. Sci. 136 144-149 (May 2017)

exception EnumError[source]

Bases: BaseException

Error subclass for enumeration errors.

EnumlibAdaptor(structure: Structure | IStructure, min_cell_size: int = 1, max_cell_size: int = 1, symm_prec: float = 0.1, enum_precision_parameter: float = 0.001, refine_structure: bool = False, check_ordered_symmetry: bool = True, timeout: float | None = None) → None[source]

An adaptor for enumlib.

structures[source]

all enumerated structures.

Type:

list[Structure]

pymatgen.command_line.gulp_caller module

Interface with command line GULP. https://gulp.curtin.edu.au/index.html WARNING: you need to have GULP installed on your system.

class BuckinghamPotential(bush_lewis_flag, pot_file)[source]

Bases: object

Generate the Buckingham Potential Table from the bush.lib or lewis.lib.

Ref: T.S.Bush, J.D.Gale, C.R.A.Catlow and P.D. Battle, J. Mater Chem., 4, 831-837 (1994). G.V. Lewis and C.R.A. Catlow, J. Phys. C: Solid State Phys., 18, 1149-1161 (1985)

Parameters:

• bush_lewis_flag (str) – Flag for using Bush or Lewis potential.

• pot_file – The potential file, either bush.lib or lewis.lib.

class GulpCaller(cmd='gulp')[source]

Bases: object

Run gulp from command line.

Initialize with the executable if not in the standard path.

Parameters:

cmd – Command. Defaults to gulp.

run(gin)[source]

Run GULP using the gin as input.

Parameters:

gin – GULP input string

Returns:

GULP output string

Return type:

gout

exception GulpConvergenceError(msg='')[source]

Bases: Exception

Exception class for GULP. Raised when proper convergence is not reached in Mott-Littleton defect energy optimization procedure in GULP.

Parameters:

msg (str) – Message.

exception GulpError(msg)[source]

Bases: Exception

Exception class for GULP. Raised when the GULP gives an error.

Parameters:

msg (str) – Message.

class GulpIO[source]

Bases: object

To generate GULP input and process output.

buckingham_input(structure: Structure, keywords, library=None, uc=True, valence_dict=None)[source]

Get a GULP input for an oxide structure and buckingham potential from library.

Parameters:

• structure – pymatgen Structure

• keywords – GULP first line keywords.

• library (Default=None) – File containing the species and potential.

• uc (Default=True) – Unit Cell Flag.

• valence_dict – {El: valence}

static buckingham_potential(structure, val_dict=None)[source]

Generate species, buckingham, and spring options for an oxide structure using the parameters in default libraries.

Ref:

1. G.V. Lewis and C.R.A. Catlow, J. Phys. C: Solid State Phys., 18, 1149-1161 (1985)

2. T.S.Bush, J.D.Gale, C.R.A.Catlow and P.D. Battle, J. Mater Chem., 4, 831-837 (1994)

Parameters:

• structure – pymatgen Structure

• val_dict (Needed if structure is not charge neutral) – {El:valence} dict, where El is element.

static get_energy(gout: str)[source]

Parameters:

gout (str) – GULP output string.

Returns:

Energy

static get_relaxed_structure(gout: str)[source]

Parameters:

gout (str) – GULP output string.

Returns:

relaxed structure.

Return type:

Structure

static keyword_line(*args)[source]

Check if the input args are proper gulp keywords and generates the 1st line of gulp input. Full keywords are expected.

Parameters:

args – 1st line keywords

static library_line(file_name)[source]

Specify GULP library file to read species and potential parameters. If using library don’t specify species and potential in the input file and vice versa. Make sure the elements of structure are in the library file.

Parameters:

file_name – Name of GULP library file

Returns:

GULP input string specifying library option

static specie_potential_lines(structure, potential, **kwargs)[source]

Generate GULP input species and potential string for pymatgen structure.

Parameters:

• structure – pymatgen Structure object

• potential – String specifying the type of potential used

• kwargs – Additional parameters related to potential. For potential == “buckingham”, anion_shell_flg (default = False): If True, anions are considered polarizable. anion_core_chrg=float anion_shell_chrg=float cation_shell_flg (default = False): If True, cations are considered polarizable. cation_core_chrg=float cation_shell_chrg=float

Returns:

containing species and potential for GULP input

Return type:

str

static structure_lines(structure: Structure, cell_flg: bool = True, frac_flg: bool = True, anion_shell_flg: bool = True, cation_shell_flg: bool = False, symm_flg: bool = True)[source]

Generate GULP input string corresponding to pymatgen structure.

Parameters:

• structure – pymatgen Structure object

• cell_flg (default = True) – Option to use lattice parameters.

• frac_flg (default = True) – If True, fractional coordinates are used. Else, Cartesian coordinates in Angstroms are used. ** GULP convention is to use fractional coordinates for periodic structures and Cartesian coordinates for non-periodic structures. **

• anion_shell_flg (default = True) – If True, anions are considered polarizable.

• cation_shell_flg (default = False) – If True, cations are considered polarizable.

• symm_flg (default = True) – If True, symmetry information is also written.

Returns:

containing structure for GULP input

Return type:

str

tersoff_input(structure: Structure, periodic=False, uc=True, *keywords)[source]

Get a GULP input with Tersoff potential for an oxide structure.

Parameters:

• structure – pymatgen Structure

• periodic (Default=False) – Flag denoting whether periodic boundary conditions are used

• library (Default=None) – File containing the species and potential.

• uc (Default=True) – Unit Cell Flag.

• keywords – GULP first line keywords.

static tersoff_potential(structure)[source]

Generate the species, Tersoff potential lines for an oxide structure.

Parameters:

structure – pymatgen Structure

class TersoffPotential(pot_file)[source]

Bases: object

Generate Tersoff Potential Table from “OxideTersoffPotentialentials” file.

Init TersoffPotential.

get_energy_buckingham(structure, gulp_cmd='gulp', keywords=('optimise', 'conp', 'qok'), valence_dict=None)[source]

Compute the energy of a structure using Buckingham potential.

Parameters:

• structure – pymatgen Structure

• gulp_cmd – GULP command if not in standard place

• keywords – GULP first line keywords

• valence_dict – {El: valence}. Needed if the structure is not charge neutral.

get_energy_relax_structure_buckingham(structure, gulp_cmd='gulp', keywords=('optimise', 'conp'), valence_dict=None)[source]

Relax a structure and compute the energy using Buckingham potential.

Parameters:

• structure – pymatgen Structure

• gulp_cmd – GULP command if not in standard place

• keywords – GULP first line keywords

• valence_dict – {El: valence}. Needed if the structure is not charge neutral.

get_energy_tersoff(structure, gulp_cmd='gulp')[source]

Compute the energy of a structure using Tersoff potential.

Parameters:

• structure – pymatgen Structure

• gulp_cmd – GULP command if not in standard place

pymatgen.command_line.mcsqs_caller module

Module to call mcsqs, distributed with AT-AT https://www.brown.edu/Departments/Engineering/Labs/avdw/atat/.

class Sqs(bestsqs: Structure | IStructure, objective_function: float | str, allsqs: list, clusters: list | str, directory: str)[source]

Bases: NamedTuple

Return type for run_mcsqs.

Create new instance of Sqs(bestsqs, objective_function, allsqs, clusters, directory)

allsqs: list[source]

Alias for field number 2

bestsqs: Structure | IStructure[source]

Alias for field number 0

clusters: list | str[source]

Alias for field number 3

directory: str[source]

Alias for field number 4

objective_function: float | str[source]

Alias for field number 1

run_mcsqs(structure: Structure | IStructure, clusters: dict[int, float], scaling: int | list[int] = 1, search_time: float = 60, directory: str | None = None, instances: int | None = None, temperature: float = 1, wr: float = 1, wn: float = 1, wd: float = 0.5, tol: float = 0.001) → Sqs[source]

Helper function for calling mcsqs with different arguments :param structure: Disordered pymatgen Structure object :type structure: Structure :param clusters: Dictionary of cluster interactions with entries in the form

number of atoms: cutoff in angstroms

Parameters:

• scaling (int or list) –

  Scaling factor to determine supercell. Two options are possible:

  1. (preferred) Scales number of atoms, e.g. for a structure with 8 atoms, scaling=4 would lead to a 32 atom supercell

  2. A sequence of three scaling factors, e.g. [2, 1, 1], which specifies that the supercell should have dimensions 2a x b x c

  Defaults to 1.

• search_time (float) – Time spent looking for the ideal SQS in minutes (default: 60)

• directory (str) – Directory to run mcsqs calculation and store files (default: None runs calculations in a temp directory)

• instances (int) – Specifies the number of parallel instances of mcsqs to run (default: number of cpu cores detected by Python)

• temperature (float) – Monte Carlo temperature (default: 1), “T” in atat code

• wr (float) – Weight assigned to range of perfect correlation match in objective function (default = 1)

• wn (float) – Multiplicative decrease in weight per additional point in cluster (default: 1)

• wd (float) – Exponent of decay in weight as function of cluster diameter (default: 0.5)

• tol (float) – Tolerance for matching correlations (default: 1e-3).

Returns:

Pymatgen structure SQS of the input structure, the mcsqs objective function,

list of all SQS structures, and the directory where calculations are run

Return type:

tuple

pymatgen.command_line.vampire_caller module

This module implements an interface to the VAMPIRE code for atomistic simulations of magnetic materials.

This module depends on a compiled vampire executable available in the path. Please download at https://vampire.york.ac.uk/download/ and follow the instructions to compile the executable.

If you use this module, please cite:

“Atomistic spin model simulations of magnetic nanomaterials.” R. F. L. Evans, W. J. Fan, P. Chureemart, T. A. Ostler, M. O. A. Ellis and R. W. Chantrell. J. Phys.: Condens. Matter 26, 103202 (2014)

class VampireCaller(ordered_structures=None, energies=None, mc_box_size=4.0, equil_timesteps=2000, mc_timesteps=4000, save_inputs=False, hm=None, avg=True, user_input_settings=None)[source]

Bases: object

Run Vampire on a material with magnetic ordering and exchange parameter information to compute the critical temperature with classical Monte Carlo.

sgraph[source]

Ground state graph.

Type:

StructureGraph

unique_site_ids[source]

Maps each site to its unique identifier

Type:

dict

nn_interactions[source]

{i: j} pairs of NN interactions between unique sites.

Type:

dict

ex_params[source]

Exchange parameter values (meV/atom)

Type:

dict

mft_t[source]

Mean field theory estimate of critical T

Type:

float

mat_name[source]

Formula unit label for input files

Type:

str

mat_id_dict[source]

Maps sites to material id # for vampire indexing.

Type:

dict

user_input_settings is a dictionary that can contain: * start_t (int): Start MC sim at this temp, defaults to 0 K. * end_t (int): End MC sim at this temp, defaults to 1500 K. * temp_increment (int): Temp step size, defaults to 25 K.

Parameters:

• ordered_structures (list) – Structure objects with magmoms.

• energies (list) – Energies of each relaxed magnetic structure.

• mc_box_size (float) – x=y=z dimensions (nm) of MC simulation box

• equil_timesteps (int) – number of MC steps for equilibrating

• mc_timesteps (int) – number of MC steps for averaging

• save_inputs (bool) – if True, save scratch dir of vampire input files

• hm (HeisenbergModel) – object already fit to low energy magnetic orderings.

• avg (bool) – If True, simply use <J> exchange parameter estimate. If False, attempt to use NN, NNN, etc. interactions.

• user_input_settings (dict) – optional commands for VAMPIRE Monte Carlo

static parse_stdout(vamp_stdout, n_mats: int) → tuple[source]

Parse stdout from Vampire.

Parameters:

• vamp_stdout (txt file) – Vampire ‘output’ file.

• n_mats (int) – Number of materials in Vampire simulation.

Returns:

MSONable vampire output. critical_temp (float): Calculated critical temp.

Return type:

parsed_out (DataFrame)

class VampireOutput(parsed_out=None, nmats=None, critical_temp=None)[source]

Bases: MSONable

This class processes results from a Vampire Monte Carlo simulation and parses the critical temperature.

Parameters:

• parsed_out (str) – JSON rep of parsed stdout DataFrame.

• nmats (int) – Number of distinct materials (1 for each specie and up/down spin).

• critical_temp (float) – Monte Carlo Tc result.