pymatgen.io.cp2k package

Submodules

pymatgen.io.cp2k.inputs module

This module defines the building blocks of a CP2K input file. The cp2k input structure is essentially a collection of “sections” which are similar to dictionary objects that activate modules of the cp2k executable, and then “keywords” which adjust variables inside of those modules. For example, FORCE_EVAL section will activate CP2K’s ability to calculate forces, and inside FORCE_EVAL, the Keyword “METHOD can be set to “QS” to set the method of force evaluation to be the quickstep (DFT) module.

A quick overview of the module:

– Section class defines the basis of Cp2k input and contains methods for manipulating these

objects similarly to Dicts.

– Keyword class defines the keywords used inside of Section objects that changes variables in

Cp2k programs.

– SectionList and KeywordList classes are lists of Section and Keyword objects that have

the same dictionary key. This deals with repeated sections and keywords.

– Cp2kInput class is special instantiation of Section that is used to represent the full cp2k

calculation input.

– The rest of the classes are children of Section intended to make initialization of common

sections easier.

class AtomicMetadata(info: ~pymatgen.io.cp2k.inputs.BasisInfo | ~pymatgen.io.cp2k.inputs.PotentialInfo | None = None, element: ~pymatgen.core.periodic_table.Element | None = None, potential: ~typing.Literal['All Electron', 'Pseudopotential'] | None = None, name: str | None = None, alias_names: list = <factory>, filename: str | None = None, version: str | None = None)[source]

Bases: MSONable

Metadata for basis sets and potentials in cp2k.

info[source]

Info about this object

Type:

pymatgen.io.cp2k.inputs.BasisInfo | pymatgen.io.cp2k.inputs.PotentialInfo | None

element[source]

Element for this object

Type:

pymatgen.core.periodic_table.Element | None

potential[source]

The potential for this object

Type:

Literal[‘All Electron’, ‘Pseudopotential’] | None

name[source]

Name of the object

Type:

str | None

alias_names[source]

Optional aliases

Type:

list

filename[source]

Name of the file containing this object

Type:

str | None

version[source]

Version

Type:

str | None

alias_names: list[source]

element: Element | None = None[source]

filename: str | None = None[source]

get_hash() → str[source]

Get a hash of this object.

get_str() → str[source]

Get string representation.

info: BasisInfo | PotentialInfo | None = None[source]

name: str | None = None[source]

potential: Literal['All Electron', 'Pseudopotential'] | None = None[source]

softmatch(other)[source]

Soft matching to see if a desired basis/potential matches requirements.

Does soft matching on the “info” attribute first. Then soft matches against the element and name/aliases.

version: str | None = None[source]

class Band_Structure(kpoint_sets: Sequence[KpointSet], filename: str = 'BAND.bs', added_mos: int = -1, keywords: dict | None = None, subsections: dict | None = None)[source]

Bases: Section

Specifies high symmetry paths for outputting the band structure in CP2K.

Parameters:

• kpoint_sets – Sequence of KpointSet objects for the band structure calculation.

• filename – Filename for the band structure output

• added_mos – Added (unoccupied) molecular orbitals for the calculation.

• keywords – additional keywords

• subsections – additional subsections.

static from_kpoints(kpoints: Kpoints, kpoints_line_density=20)[source]

Initialize band structure section from a line-mode Kpoint object.

Parameters:

• kpoints – a kpoint object from the vasp module, which was constructed in line mode

• kpoints_line_density – Number of kpoints along each path

class BasisFile(objects: Sequence | None = None)[source]

Bases: DataFile

Data file for basis sets only.

classmethod from_str(string)[source]

Initialize from a string representation.

class BasisInfo(electrons: int | None = None, core: int | None = None, valence: int | None = None, polarization: int | None = None, diffuse: int | None = None, cc: bool | None = False, pc: bool | None = False, sr: bool | None = False, molopt: bool | None = False, admm: bool | None = False, lri: bool | None = False, contracted: bool | None = None, xc: str | None = None)[source]

Bases: MSONable

Summary info about a basis set.

electrons[source]

Number of electrons

Type:

int | None

core[source]

Number of basis functions per core electron

Type:

int | None

valence[source]

Number of basis functions per valence electron OR number of exp if it is a FIT formatted admm basis

Type:

int | None

polarization[source]

Number of polarization functions

Type:

int | None

diffuse[source]

Number of added, diffuse/augmentation functions

Type:

int | None

cc[source]

Correlation consistent

Type:

bool | None

pc[source]

Polarization consistent

Type:

bool | None

sr[source]

Short-range optimized

Type:

bool | None

molopt[source]

Optimized for molecules/solids

Type:

bool | None

admm[source]

Whether this is an auxiliary basis set for ADMM

Type:

bool | None

lri[source]

Whether this is a local resolution of identity auxiliary basis

Type:

bool | None

contracted[source]

Whether this basis set is contracted

Type:

bool | None

xc[source]

Exchange correlation functional used for creating this potential

Type:

str | None

admm: bool | None = False[source]

cc: bool | None = False[source]

contracted: bool | None = None[source]

core: int | None = None[source]

diffuse: int | None = None[source]

electrons: int | None = None[source]

classmethod from_str(string: str) → BasisInfo[source]

Get summary info from a string.

lri: bool | None = False[source]

molopt: bool | None = False[source]

pc: bool | None = False[source]

polarization: int | None = None[source]

softmatch(other)[source]

Soft matching to see if two basis sets match.

Will only match those attributes which are defined for this basis info object (one way checking)

sr: bool | None = False[source]

valence: int | None = None[source]

xc: str | None = None[source]

class BrokenSymmetry(l_alpha: Sequence = (-1,), n_alpha: Sequence = (0,), nel_alpha: Sequence = (-1,), l_beta: Sequence = (-1,), n_beta: Sequence = (0,), nel_beta: Sequence = (-1,))[source]

Bases: Section

Define the required atomic orbital occupation assigned in initialization of the density matrix, by adding or subtracting electrons from specific angular momentum channels. It works only with GUESS ATOMIC.

Initialize the broken symmetry section.

Parameters:

• l_alpha – Angular momentum quantum number of the orbitals whose occupation is changed

• n_alpha – Principal quantum number of the orbitals whose occupation is changed. Default is the first not occupied

• nel_alpha – Orbital occupation change per angular momentum quantum number. In unrestricted calculations applied to spin alpha

• l_beta – Same as L_alpha for beta channel

• n_beta – Same as N_alpha for beta channel

• nel_beta – Same as NEL_alpha for beta channel

classmethod from_el(el, oxi_state=0, spin=0)[source]

Create section from element, oxidation state, and spin.

class Cell(lattice: Lattice, keywords: dict | None = None, **kwargs)[source]

Bases: Section

Defines the simulation cell (lattice).

Initialize the cell section.

Parameters:

• lattice – pymatgen lattice object

• keywords – additional keywords

class Coord(structure: Structure | Molecule, aliases: dict | None = None, keywords: dict | None = None, subsections: dict | None = None, **kwargs)[source]

Bases: Section

Specifies the coordinates of the atoms using a pymatgen structure object.

Parameters:

• structure – Pymatgen structure object

• alias (bool) – whether or not to identify the sites by Element + number so you can do things like assign unique magnetization do different elements.

• keywords – additional keywords

• subsections – additional subsections.

class Cp2kInput(name: str = 'CP2K_INPUT', subsections: dict | None = None, **kwargs)[source]

Bases: Section

Special instance of ‘Section’ class that is meant to represent the overall cp2k input. Distinguishes itself from Section by overriding get_str() to not print this section’s title and by implementing the file i/o.

Initialize Cp2kInput by calling the super.

classmethod from_file(filename: str)[source]

Initialize from a file.

classmethod from_lines(lines: list | tuple)[source]

Helper method to read lines of file.

classmethod from_str(s: str)[source]

Initialize from a string.

get_str()[source]

Get string representation of the Cp2kInput.

write_file(input_filename: str = 'cp2k.inp', output_dir: str = '.', make_dir_if_not_present: bool = True)[source]

Write input to a file.

Parameters:

• input_filename (str, optional) – Defaults to “cp2k.inp”.

• output_dir (str, optional) – Defaults to “.”.

• make_dir_if_not_present (bool, optional) – Defaults to True.

class DOS(ndigits: int = 6, keywords: dict | None = None, subsections: dict | None = None, **kwargs)[source]

Bases: Section

Controls printing of the density of states.

Initialize the DOS section.

Parameters:

• ndigits – how many digits of precision to print. As of 2022.1, this is necessary to not lose information.

• keywords – additional keywords

• subsections – additional subsections

class DataFile(objects: Sequence | None = None)[source]

Bases: MSONable

A data file for a cp2k calc.

classmethod from_file(filename)[source]

Load from a file.

classmethod from_str()[source]

Initialize from a string.

get_str() → str[source]

Get string representation.

objects: Sequence | None = None[source]

write_file(filename)[source]

Write to a file.

class Davidson(new_prec_each: int = 20, preconditioner: str = 'FULL_SINGLE_INVERSE', keywords: dict | None = None, subsections: dict | None = None, **kwargs)[source]

Bases: Section

Parameters for davidson diagonalization.

Parameters:

• new_prec_each (int) – How often to recalculate the preconditioner.

• preconditioner (str) –

  Preconditioner to use. “FULL_ALL”: Most effective state selective preconditioner based on diagonalization,

  requires the ENERGY_GAP parameter to be an underestimate of the HOMO-LUMO gap. This preconditioner is recommended for almost all systems, except very large systems where make_preconditioner would dominate the total computational cost.

  ”FULL_KINETIC”: Cholesky inversion of S and T, fast construction, robust, use for

  very large systems.

  ”FULL_SINGLE”: Based on H-eS diagonalization, not as good as FULL_ALL, but

  somewhat cheaper to apply.

  ”FULL_SINGLE_INVERSE”: Based on H-eS cholesky inversion, similar to FULL_SINGLE

  in preconditioning efficiency but cheaper to construct, might be somewhat less robust. Recommended for large systems.

  ”FULL_S_INVERSE”: Cholesky inversion of S, not as good as FULL_KINETIC,

  yet equally expensive.

  ”NONE”: skip preconditioning

• keywords – additional keywords

• subsections – additional subsections.

class Dft(basis_set_filenames: Iterable = ('BASIS_MOLOPT',), potential_filename='GTH_POTENTIALS', uks: bool = True, wfn_restart_file_name: str | None = None, keywords: dict | None = None, subsections: dict | None = None, **kwargs)[source]

Bases: Section

Controls the DFT parameters in Cp2k.

Initialize the DFT section.

Parameters:

• basis_set_filenames – Name of the file that contains the basis set information. Defaults to “BASIS_MOLOPT”.

• potential_filename – Name of the file that contains the pseudopotential information. Defaults to “GTH_POTENTIALS”.

• uks – Whether to run unrestricted Kohn Sham (spin polarized). Defaults to True.

• wfn_restart_file_name – Defaults to None.

• keywords – additional keywords to add.

• subsections – Any subsections to initialize with. Defaults to None.

class DftPlusU(eps_u_ramping=1e-05, init_u_ramping_each_scf=False, l=-1, u_minus_j=0, u_ramping=0, keywords: dict | None = None, subsections: dict | None = None, **kwargs)[source]

Bases: Section

Controls DFT+U for an atom kind.

Initialize the DftPlusU section.

Parameters:

• eps_u_ramping – (float) SCF convergence threshold at which to start ramping the U value

• init_u_ramping_each_scf – (bool) Whether or not to do u_ramping each scf cycle

• l – (int) angular moment of the orbital to apply the +U correction

• u_minus_j – (float) the effective U parameter, Ueff = U-J

• u_ramping – (float) stepwise amount to increase during ramping until u_minus_j is reached

• keywords – additional keywords

• subsections – additional subsections

class Diagonalization(eps_adapt: float = 0, eps_iter: float = 1e-08, eps_jacobi: float = 0, jacobi_threshold: float = 1e-07, keywords: dict | None = None, subsections: dict | None = None, **kwargs)[source]

Bases: Section

Controls diagonalization settings (if using traditional diagonalization).

Initialize the diagonalization section.

class E_Density_Cube(keywords: dict | None = None, subsections: dict | None = None, **kwargs)[source]

Bases: Section

Controls printing of the electron density cube file.

Basic object representing a CP2K Section. Sections activate different parts of the calculation. For example, FORCE_EVAL section will activate CP2K’s ability to calculate forces.

Parameters:

• name – The name of the section (must match name in CP2K)

• subsections – A dictionary of subsections that are nested in this section. Format is {‘NAME’: Section(args, **kwargs). The name you chose for ‘NAME’ to index that subsection does not *have to be the same as the section’s true name, but we recommend matching them. You can specify a blank dictionary if there are no subsections, or if you want to insert the subsections later.

• repeats – Whether or not this section can be repeated. Most sections cannot. Default=False.

• description – Description of this section for easier readability

• keywords – the keywords to be set for this section. Each element should be a Keyword object. This can be more cumbersome than simply using kwargs for building a class in a script, but is more convenient for the class instantiations of CP2K sections (see below).

• section_parameters – the section parameters for this section. Section parameters are specialized keywords that modify the behavior of the section overall. Most sections do not have section parameters, but some do. Unlike normal Keywords, these are specified as strings and not as Keyword objects.

• location – the path to the section in the form ‘SECTION/SUBSECTION1/SUBSECTION3’, example for QS module: ‘FORCE_EVAL/DFT/QS’. This location is used to automatically determine if a subsection requires a supersection to be activated.

• verbose – Controls how much is printed to Cp2k input files (Also see Keyword). If True, then a description of the section will be printed with it as a comment (if description is set). Default=True.

• alias – An alias for this class to use in place of the name.

• keyword (kwargs are interpreted as) –

• as (value pairs and added to the keywords array) –

• objects (Keyword) –

class ForceEval(keywords: dict | None = None, subsections: dict | None = None, **kwargs)[source]

Bases: Section

Controls the calculation of energy and forces in Cp2k.

Initialize the ForceEval section.

class GaussianTypeOrbitalBasisSet(info: ~pymatgen.io.cp2k.inputs.BasisInfo | None = None, element: ~pymatgen.core.periodic_table.Element | None = None, potential: ~typing.Literal['All Electron', 'Pseudopotential'] | None = None, name: str | None = None, alias_names: list = <factory>, filename: str | None = None, version: str | None = None, nset: int | None = None, n: list[int] | None = None, lmax: list[int] | None = None, lmin: list[int] | None = None, nshell: list[dict[int, int]] | None = None, exponents: list[list[float]] | None = None, coefficients: list[dict[int, dict[int, dict[int, float]]]] | None = None)[source]

Bases: AtomicMetadata

Model definition of a GTO basis set.

info[source]

Cardinality of this basis

Type:

pymatgen.io.cp2k.inputs.BasisInfo | None

nset[source]

Number of exponent sets

Type:

int | None

n[source]

Principle quantum number for each set

Type:

list[int] | None

lmax[source]

Maximum angular momentum quantum number for each set

Type:

list[int] | None

lmin[source]

Minimum angular momentum quantum number for each set

Type:

list[int] | None

nshell[source]

Number of shells for angular momentum l for each set

Type:

list[dict[int, int]] | None

exponents[source]

Exponents for each set

Type:

list[list[float]] | None

coefficients[source]

Contraction coefficients for each set. Dict[exp->l->shell]

Type:

list[dict[int, dict[int, dict[int, float]]]] | None

coefficients: list[dict[int, dict[int, dict[int, float]]]] | None = None[source]

exponents: list[list[float]] | None = None[source]

classmethod from_str(string: str) → GaussianTypeOrbitalBasisSet[source]

Read from standard cp2k GTO formatted string.

get_keyword() → Keyword[source]

Convert basis to keyword object.

get_str() → str[source]

Get standard cp2k GTO formatted string.

info: BasisInfo | None = None[source]

lmax: list[int] | None = None[source]

lmin: list[int] | None = None[source]

n: list[int] | None = None[source]

property nexp[source]

Number of exponents.

nset: int | None = None[source]

nshell: list[dict[int, int]] | None = None[source]

class Global(project_name: str = 'CP2K', run_type: str = 'ENERGY_FORCE', keywords: dict | None = None, **kwargs)[source]

Bases: Section

Controls ‘global’ settings for cp2k execution such as RUN_TYPE and PROJECT_NAME.

Initialize the global section.

Parameters:

• project_name – Defaults to “CP2K”.

• run_type – what type of calculation to run

• keywords – Additional keywords to add

class GthPotential(info: ~pymatgen.io.cp2k.inputs.PotentialInfo = None, element: ~pymatgen.core.periodic_table.Element | None = None, potential: ~typing.Literal['All Electron', 'Pseudopotential'] | None = None, name: str | None = None, alias_names: list = <factory>, filename: str | None = None, version: str | None = None, n_elecs: dict[int, int] | None = None, r_loc: float | None = None, nexp_ppl: int | None = None, c_exp_ppl: ~collections.abc.Sequence | None = None, radii: dict[int, float] | None = None, nprj: int | None = None, nprj_ppnl: dict[int, int] | None = None, hprj_ppnl: dict[int, dict[int, dict[int, float]]] | None = None)[source]

Bases: AtomicMetadata

Representation of GTH-type (pseudo)potential.

info[source]

Info about this potential

Type:

pymatgen.io.cp2k.inputs.PotentialInfo

n_elecs[source]

Number of electrons for each quantum number

Type:

dict[int, int] | None

r_loc[source]

Radius of local projectors

Type:

float | None

nexp_ppl[source]

Number of the local pseudopotential functions

Type:

int | None

c_exp_ppl[source]

Sequence = field(None, description=”Coefficients of the local pseudopotential functions

Type:

collections.abc.Sequence | None

radii[source]

Radius of the nonlocal part for angular momentum quantum number l defined by the Gaussian function exponents alpha_prj_ppnl

Type:

dict[int, float] | None

nprj[source]

Number of projectors

Type:

int | None

nprj_ppnl[source]

Number of the non-local projectors for the angular momentum quantum number

Type:

dict[int, int] | None

hprj_ppnl[source]

Coefficients of the non-local projector functions. Coeff ij for ang momentum l

Type:

dict[int, dict[int, dict[int, float]]] | None

)

c_exp_ppl: Sequence | None = None[source]

classmethod from_section(section: Section) → GthPotential[source]

Extract GTH-formatted string from a section and convert it to model.

classmethod from_str(string)[source]

Initialize model from a GTH formatted string.

get_keyword() → Keyword[source]

Get keyword object for the potential.

get_section() → Section[source]

Convert model to a GTH-formatted section object for input files.

get_str() → str[source]

Convert model to a GTH-formatted string.

hprj_ppnl: dict[int, dict[int, dict[int, float]]] | None = None[source]

n_elecs: dict[int, int] | None = None[source]

nexp_ppl: int | None = None[source]

nprj: int | None = None[source]

nprj_ppnl: dict[int, int] | None = None[source]

r_loc: float | None = None[source]

radii: dict[int, float] | None = None[source]

class Keyword(name: str, *values, description: str | None = None, units: str | None = None, verbose: bool | None = True, repeats: bool | None = False)[source]

Bases: MSONable

Class representing a keyword argument in CP2K. Within CP2K Sections, which activate features of the CP2K code, the keywords are arguments that control the functionality of that feature. For example, the section “FORCE_EVAL” activates the evaluation of forces/energies, but within “FORCE_EVAL” the keyword “METHOD” controls whether or not this will be done with, say, “Quickstep” (DFT) or “EIP” (empirical interatomic potential).

Initializes a keyword. These Keywords and the value passed to them are sometimes as simple as KEYWORD VALUE, but can also be more elaborate such as KEYWORD [UNITS] VALUE1 VALUE2, which is why this class exists: to handle many values and control easy printing to an input file.

Parameters:

• name – The name of this keyword. Must match an acceptable keyword from CP2K

• values – All non-keyword arguments after ‘name’ are interpreted as the values to set for this keyword. i.e: KEYWORD ARG1 ARG2 would provide two values to the keyword.

• description – The description for this keyword. This can make readability of input files easier for some. Default=None.

• units – The units for this keyword. If not specified, CP2K default units will be used. Consult manual for default units. Default=None.

• verbose – Whether the description should be printed with the string of this keyword

• repeats – Whether or not this keyword may be repeated. Default=False.

as_dict()[source]

Get a dictionary representation of the Keyword.

classmethod from_dict(dct: dict) → Self[source]

Initialize from dictionary.

classmethod from_str(s)[source]

Initialize from a string.

Keywords must be labeled with strings. If the post-processor finds that the keywords is a number, then None is return (used by the file reader).

Returns:

Keyword or None

get_str() → str[source]

String representation of Keyword.

verbosity(v)[source]

Change the printing of this keyword’s description.

class KeywordList(keywords: Sequence[Keyword])[source]

Bases: MSONable

Some keywords can be repeated, which makes accessing them via the normal dictionary methods a little unnatural. This class deals with this by defining a collection of same-named keywords that are accessed by one name.

Initializes a keyword list given a sequence of keywords.

Parameters:

keywords – A list of keywords. Must all have the same name (case-insensitive)

append(item)[source]

Append the keyword list.

extend(lst: Sequence[Keyword]) → None[source]

Extend the keyword list.

get_str(indent: int = 0) → str[source]

String representation of Keyword.

verbosity(verbosity)[source]

Silence all keywords in keyword list.

class Kind(specie: str, alias: str | None = None, magnetization: float = 0.0, basis_set: GaussianTypeOrbitalBasisSet | str | None = 'GTH_BASIS', potential: GthPotential | str | None = 'GTH_POTENTIALS', ghost: bool = False, aux_basis: GaussianTypeOrbitalBasisSet | str | None = None, keywords: dict | None = None, subsections: dict | None = None, **kwargs)[source]

Bases: Section

Specifies the information for the different atom types being simulated.

Initialize a KIND section.

Parameters:

• specie – Object representing the atom.

• alias – Alias for the atom, can be used for specifying modifications to certain atoms but not all, e.g. Mg_1 and Mg_2 to force difference oxidation states on the two atoms.

• magnetization – From the CP2K Manual: The magnetization used in the atomic initial guess. Adds magnetization/2 spin-alpha electrons and removes magnetization/2 spin-beta electrons.

• basis_set – Basis set for this atom, accessible from the basis set file specified

• potential – Pseudopotential for this atom, accessible from the potential file

• ghost – Turn this into ghost atom (disable the potential)

• aux_basis – Auxiliary basis to use with ADMM

• keywords – additional keywords

• subsections – additional subsections

• kwargs – Additional kwargs to pass to Section()

class KpointSet(npoints: int, kpoints: Iterable, units: str = 'B_VECTOR')[source]

Bases: Section

Specifies a kpoint line to be calculated between special points.

Parameters:

• npoints (int) – Number of kpoints along the line.

• kpoints – A dictionary of {label: kpoint} kpoints defining the path

• units (str) –

  Units for the kpoint coordinates. Options: “B_VECTOR” (reciprocal coordinates)

  ”CART_ANGSTROM” (units of 2*Pi/Angstrom) “CART_BOHR” (units of 2*Pi/Bohr).

class Kpoints(kpts: Sequence | Sequence[Sequence[int]], weights: Sequence | None = None, eps_geo: float = 1e-06, full_grid: bool = False, parallel_group_size: int = -1, scheme: str = 'MONKHORST-PACK', symmetry: bool = False, units: str = 'B_VECTOR', verbose: bool = False, wavefunctions: str = 'COMPLEX')[source]

Bases: Section

Description of the k-points to use for the calculation.

Parameters:

• kpts (list, tuple) – a 2D array for the kpoints of the form [(1,1,1),]. If len(kpts) == 1. Then it is taken as subdivisions for automatic kpoint scheme. If it has more entries, it is taken as manual entries for kpoints.

• weights (list, tuple) – a weight for each kpoint. Default is to weigh each by 1

• eps_geo (float) – tolerance for symmetry. Default=1e-6

• full_grid (bool) – use full (not reduced) kpoint grid. Default=False.

• parallel_group_size (int) – from cp2k manual: Number of processors to be used for a single kpoint. This number must divide the total number of processes. The number of groups must divide the total number of kpoints. Value=-1 (smallest possible number of processes per group, satisfying the constraints). Value=0 (all processes). Value=n (exactly n processes). Default=-1.

• scheme (str) – kpoint generation scheme. Default=’Monkhorst-Pack’

• symmetry (bool) – Use symmetry to reduce the number of kpoints. Default=False.

• units (str) – Units for the kpoint coordinates (reciprocal coordinates or cartesian). Default=’B_VECTOR’ (reciprocal)

• verbose (bool) – verbose output for kpoints. Default=False

• wavefunctions (str) – Whether to use complex or real valued wavefunctions (if available). Default=’complex’.

classmethod from_kpoints(kpoints: Kpoints, structure=None)[source]

Initialize the section from a Kpoints object (pymatgen.io.vasp.inputs). CP2K does not have an automatic gamma-point constructor, so this is generally used to get the number of divisions from a kpoint static constructor and then build a Monkhorst-Pack grid, which is sufficient for gamma-recommended systems so long as the grid is fine enough.

Parameters:

• kpoints – A pymatgen kpoints object.

• structure – Pymatgen structure object. Required for automatically performing symmetry analysis and reducing the kpoint grid.

• reduce – whether or not to reduce the grid using symmetry. CP2K itself cannot do this automatically without spglib present at execution time.

class LDOS(index: int = 1, alias: str | None = None, keywords: dict | None = None, subsections: dict | None = None, **kwargs)[source]

Bases: Section

Controls printing of the LDOS (List-Density of states). i.e. projects onto specific atoms.

Initialize the LDOS section.

Parameters:

• index – Index of the atom to project onto

• alias – section alias

• keywords – additional keywords

• subsections – additional subsections

class MO_Cubes(write_cube: bool = False, nhomo: int = 1, nlumo: int = 1, keywords: dict | None = None, subsections: dict | None = None, **kwargs)[source]

Bases: Section

Controls printing of the molecular orbital eigenvalues.

Initialize the MO_CUBES section.

class Mgrid(cutoff: float = 1200, rel_cutoff: float = 80, ngrids: int = 5, progression_factor: int = 3, keywords: dict | None = None, subsections: dict | None = None, **kwargs)[source]

Bases: Section

Controls the multigrid for numerical integration.

Initialize the MGRID section.

Parameters:

• cutoff – Cutoff energy (in Rydbergs for historical reasons) defining how find of Gaussians will be used

• rel_cutoff – The relative cutoff energy, which defines how to map the Gaussians onto the multigrid. If the value is too low then, even if you have a high cutoff with sharp Gaussians, they will be mapped to the course part of the multigrid

• ngrids – number of grids to use

• progression_factor – divisor that decides how to map Gaussians the multigrid after the highest mapping is decided by rel_cutoff

• keywords – additional keywords

• subsections – additional subsections

class OrbitalTransformation(minimizer: str = 'CG', preconditioner: str = 'FULL_ALL', algorithm: str = 'STRICT', rotation: bool = False, occupation_preconditioner: bool = False, energy_gap: float = -1, linesearch: str = '2PNT', keywords: dict | None = None, subsections: dict | None = None, **kwargs)[source]

Bases: Section

Turns on the Orbital Transformation scheme for diagonalizing the Hamiltonian. Often faster and with guaranteed convergence compared to normal diagonalization, but requires the system to have a band gap.

NOTE: OT has poor convergence for metallic systems and cannot use SCF mixing or smearing. Therefore, you should not use it for metals or systems with ‘small’ band gaps. In that case, use normal diagonalization

Initialize the OT section.

Parameters:

• minimizer – The minimizer to use with the OT method. Default is conjugate gradient method, which is more robust, but more well-behaved systems should use DIIS, which can be as much as 50% faster.

• preconditioner – Preconditioner to use for OT, FULL_ALL tends to be most robust, but is not always most efficient. For difficult systems, FULL_SINGLE_INVERSE can be more robust, and is reasonably efficient with large systems. For huge, but well behaved, systems, where construction of the preconditioner can take a very long time, FULL_KINETIC can be a good choice.

• algorithm – What algorithm to use for OT. ‘Strict’: Taylor or diagonalization based algorithm. IRAC: Orbital Transformation based Iterative Refinement of the Approximate Congruence transformation (OT/IR).

• rotation – Introduce additional variables to allow subspace rotations (i.e fractional occupations)

• occupation_preconditioner – include the fractional occupation in the preconditioning

• energy_gap – Guess for the band gap. For FULL_ALL, should be smaller than the actual band gap, so simply using 0.01 is a robust value. Choosing a larger value will help if you start with a bad initial guess though. For FULL_SINGLE_INVERSE, energy_gap is treated as a lower bound. Values lower than 0.05 in this case can lead to stability issues.

• linesearch (str) – From the manual: 1D line search algorithm to be used with the OT minimizer, in increasing order of robustness and cost. MINIMIZER CG combined with LINESEARCH GOLD should always find an electronic minimum. Whereas the 2PNT minimizer is almost always OK, 3PNT might be needed for systems in which successive OT CG steps do not decrease the total energy.

• keywords – additional keywords

• subsections – additional subsections

class PBE(parameterization: str = 'ORIG', scale_c: float = 1, scale_x: float = 1, keywords: dict | None = None, subsections: dict | None = None)[source]

Bases: Section

Info about the PBE functional.

Parameters:

• parameterization (str) – ORIG: original PBE PBESOL: PBE for solids/surfaces REVPBE: revised PBE

• scale_c (float) – scales the correlation part of the functional.

• scale_x (float) – scales the exchange part of the functional.

• keywords – additional keywords

• subsections – additional subsections.

class PDOS(nlumo: int = -1, keywords: dict | None = None, subsections: dict | None = None, **kwargs)[source]

Bases: Section

Controls printing of projected density of states onto the different atom KINDS (elemental decomposed DOS).

Initialize the PDOS section.

Parameters:

• nlumo – how many unoccupied orbitals to include (-1==ALL)

• keywords – additional keywords

• subsections – additional subsections

class PotentialFile(objects: Sequence | None = None)[source]

Bases: DataFile

Data file for potentials only.

classmethod from_str(string)[source]

Initialize from a string representation.

class PotentialInfo(electrons: int | None = None, potential_type: str | None = None, nlcc: bool | None = None, xc: str | None = None)[source]

Bases: MSONable

Metadata for this potential.

electrons[source]

Total number of electrons

Type:

int | None

potential_type[source]

Potential type (e.g. GTH)

Type:

str | None

nlcc[source]

Nonlinear core corrected potential

Type:

bool | None

xc[source]

Exchange correlation functional used for creating this potential

Type:

str | None

electrons: int | None = None[source]

classmethod from_str(string)[source]

Get a cp2k formatted string representation.

nlcc: bool | None = None[source]

potential_type: str | None = None[source]

softmatch(other)[source]

Soft matching to see if two potentials match.

Will only match those attributes which are defined for this basis info object (one way checking)

xc: str | None = None[source]

class QS(method: str = 'GPW', eps_default: float = 1e-10, eps_pgf_orb: float | None = None, extrapolation: str = 'ASPC', keywords: dict | None = None, subsections: dict | None = None, **kwargs)[source]

Bases: Section

Controls the quickstep settings (DFT driver).

Initialize the QS Section.

Parameters:

• method ("GPW" | "GAPW") – What DFT methodology to use. GPW (Gaussian Plane Waves) for DFT with pseudopotentials or GAPW (Gaussian Augmented Plane Waves) for all electron calculations.

• eps_default (float) – The default level of convergence accuracy. NOTE: This is a global value for all the numerical value of all EPS_* values in QS module. It is not the same as EPS_SCF, which sets convergence accuracy of the SCF cycle alone.

• eps_pgf_orb – Precision for the overlap matrix. Default is to use sqrt(eps_default)

• extrapolation ("PS" | "ASPC") – Method use for extrapolation. If using gamma-point-only calculation, then one should either PS or ASPC (ASPC especially for MD runs). See the manual for other options.

• keywords – Additional keywords to add

• subsections – Subsections to initialize with.

class Scf(max_scf: int = 50, eps_scf: float = 1e-06, scf_guess: str = 'RESTART', keywords: dict | None = None, subsections: dict | None = None, **kwargs)[source]

Bases: Section

Controls the self consistent field loop.

Initialize the Scf section.

Parameters:

• max_scf (int) – Maximum number of SCF loops before terminating. Defaults to 50.

• eps_scf (float) – Convergence criteria for SCF loop. Defaults to 1e-6.

• scf_guess –

  Initial guess for SCF loop. “ATOMIC”: Generate an atomic density using the atomic code “CORE”: Diagonalize the core Hamiltonian for an initial guess. “HISTORY_RESTART”: Extrapolated from previous RESTART files. “MOPAC”: Use same guess as MOPAC for semi-empirical methods or a simple

  diagonal density matrix for other methods.

  ”NONE”: Skip initial guess (only for NON-SCC DFTB). “RANDOM”: Use random wavefunction coefficients. “RESTART”: Use the RESTART file as an initial guess (and ATOMIC if not present). “SPARSE”: Generate a sparse wavefunction using the atomic code (for OT based

  methods).

• keywords – Additional keywords

• subsections – Additional subsections

class Section(name: str, subsections: dict | None = None, repeats: bool = False, description: str | None = None, keywords: dict | None = None, section_parameters: list | tuple | None = None, location: str | None = None, verbose: bool | None = True, alias: str | None = None, **kwargs)[source]

Bases: MSONable

Basic input representation of input to Cp2k. Activates functionality inside of the Cp2k executable.

Basic object representing a CP2K Section. Sections activate different parts of the calculation. For example, FORCE_EVAL section will activate CP2K’s ability to calculate forces.

Parameters:

• name – The name of the section (must match name in CP2K)

• subsections – A dictionary of subsections that are nested in this section. Format is {‘NAME’: Section(args, **kwargs). The name you chose for ‘NAME’ to index that subsection does not *have to be the same as the section’s true name, but we recommend matching them. You can specify a blank dictionary if there are no subsections, or if you want to insert the subsections later.

• repeats – Whether or not this section can be repeated. Most sections cannot. Default=False.

• description – Description of this section for easier readability

• keywords – the keywords to be set for this section. Each element should be a Keyword object. This can be more cumbersome than simply using kwargs for building a class in a script, but is more convenient for the class instantiations of CP2K sections (see below).

• section_parameters – the section parameters for this section. Section parameters are specialized keywords that modify the behavior of the section overall. Most sections do not have section parameters, but some do. Unlike normal Keywords, these are specified as strings and not as Keyword objects.

• location – the path to the section in the form ‘SECTION/SUBSECTION1/SUBSECTION3’, example for QS module: ‘FORCE_EVAL/DFT/QS’. This location is used to automatically determine if a subsection requires a supersection to be activated.

• verbose – Controls how much is printed to Cp2k input files (Also see Keyword). If True, then a description of the section will be printed with it as a comment (if description is set). Default=True.

• alias – An alias for this class to use in place of the name.

• keyword (kwargs are interpreted as) –

• as (value pairs and added to the keywords array) –

• objects (Keyword) –

add(other)[source]

Add another keyword to the current section.

by_path(path: str)[source]

Access a sub-section using a path. Used by the file parser.

Parameters:

path (str) – Path to section of form ‘SUBSECTION1/SUBSECTION2/SUBSECTION_OF_INTEREST’

check(path: str)[source]

Check if section exists within the current using a path. Can be useful for cross-checking whether or not required dependencies have been satisfied, which CP2K does not enforce.

Parameters:

path (str) – Path to section of form ‘SUBSECTION1/SUBSECTION2/SUBSECTION_OF_INTEREST’

get(d, default=None)[source]

Similar to get for dictionaries. This will attempt to retrieve the section or keyword matching d. Will not raise an error if d does not exist.

Parameters:

• d – the key to retrieve, if present

• default – what to return if d is not found

get_keyword(d, default=None)[source]

Get function, only for subsections.

Parameters:

• d – Name of keyword to get

• default – return if d is not found in keyword list

get_section(d, default=None)[source]

Get function, only for subsections.

Parameters:

• d – Name of section to get

• default – return if d is not found in subsections

get_str() → str[source]

Get string representation of Section.

inc(dct: dict)[source]

Mongo style dict modification. Include.

insert(d)[source]

Insert a new section as a subsection of the current one.

safeset(dct: dict)[source]

Alias for update with strict (no insertions). Used by custodian.

set(dct: dict)[source]

Alias for update. Used by custodian.

setitem(key, value, strict=False)[source]

Helper function for setting items. Kept separate from the double-underscore function so that “strict” option can be made possible.

strict will only set values for items that already have a key entry (no insertion).

silence()[source]

Recursively delete all print sections so that only defaults are printed out.

unset(dct: dict)[source]

Dict based deletion. Used by custodian.

update(dct: dict, strict=False)[source]

Update the Section according to a dictionary argument. This is most useful for providing user-override settings to default parameters. As you pass a dictionary the class variables like “description”, “location”, or “repeats” are not included. Therefore, it is recommended that this be used to modify existing Section objects to a user’s needs, but not used for the creation of new Section child-classes.

Parameters:

• dct (dict) –

  A dictionary containing the update information. Should use nested dictionaries to specify the full path of the update. If a section or keyword does not exist, it will be created, but only with the values that are provided in “d”, not using default values from a Section object. Example: {

  ’SUBSECTION1’: {

  ‘SUBSEC2’: {‘NEW_KEYWORD’: ‘NEW_VAL’}, ‘NEW_SUBSEC’: {‘NEW_KWD’: ‘NEW_VAL’} }

  }

• strict (bool) – If true, only update existing sections and keywords. If false, allow new sections and keywords. Default: False

verbosity(verbosity: bool) → None[source]

Change the section verbosity recursively by turning on/off the printing of descriptions. Turning off descriptions may reduce the appealing documentation of input files, but also helps de-clutter them.

class SectionList(sections: Sequence[Section])[source]

Bases: MSONable

Section list.

Initializes a SectionList object using a sequence of sections.

Parameters:

sections – A list of keywords. Must all have the same name (case-insensitive)

append(item) → None[source]

Append the section list.

extend(lst: list) → None[source]

Extend the section list.

get(d, index=-1)[source]

Get for section list. If index is specified, return the section at that index. Otherwise, return a get on the last section.

get_str() → str[source]

Return string representation of section list.

verbosity(verbosity) → None[source]

Silence all sections in section list.

class Smear(elec_temp: float = 300, method: str = 'FERMI_DIRAC', fixed_magnetic_moment: float = -100.0, keywords: dict | None = None, subsections: dict | None = None, **kwargs)[source]

Bases: Section

Control electron smearing.

Basic object representing a CP2K Section. Sections activate different parts of the calculation. For example, FORCE_EVAL section will activate CP2K’s ability to calculate forces.

Parameters:

• name – The name of the section (must match name in CP2K)

• subsections – A dictionary of subsections that are nested in this section. Format is {‘NAME’: Section(args, **kwargs). The name you chose for ‘NAME’ to index that subsection does not *have to be the same as the section’s true name, but we recommend matching them. You can specify a blank dictionary if there are no subsections, or if you want to insert the subsections later.

• repeats – Whether or not this section can be repeated. Most sections cannot. Default=False.

• description – Description of this section for easier readability

• keywords – the keywords to be set for this section. Each element should be a Keyword object. This can be more cumbersome than simply using kwargs for building a class in a script, but is more convenient for the class instantiations of CP2K sections (see below).

• section_parameters – the section parameters for this section. Section parameters are specialized keywords that modify the behavior of the section overall. Most sections do not have section parameters, but some do. Unlike normal Keywords, these are specified as strings and not as Keyword objects.

• location – the path to the section in the form ‘SECTION/SUBSECTION1/SUBSECTION3’, example for QS module: ‘FORCE_EVAL/DFT/QS’. This location is used to automatically determine if a subsection requires a supersection to be activated.

• verbose – Controls how much is printed to Cp2k input files (Also see Keyword). If True, then a description of the section will be printed with it as a comment (if description is set). Default=True.

• alias – An alias for this class to use in place of the name.

• keyword (kwargs are interpreted as) –

• as (value pairs and added to the keywords array) –

• objects (Keyword) –

class Subsys(keywords: dict | None = None, subsections: dict | None = None, **kwargs)[source]

Bases: Section

Controls the definition of the system to be simulated.

Initialize the subsys section.

class V_Hartree_Cube(keywords: dict | None = None, subsections: dict | None = None, **kwargs)[source]

Bases: Section

Controls printing of the hartree potential as a cube file.

Basic object representing a CP2K Section. Sections activate different parts of the calculation. For example, FORCE_EVAL section will activate CP2K’s ability to calculate forces.

Parameters:

• name – The name of the section (must match name in CP2K)

• subsections – A dictionary of subsections that are nested in this section. Format is {‘NAME’: Section(args, **kwargs). The name you chose for ‘NAME’ to index that subsection does not *have to be the same as the section’s true name, but we recommend matching them. You can specify a blank dictionary if there are no subsections, or if you want to insert the subsections later.

• repeats – Whether or not this section can be repeated. Most sections cannot. Default=False.

• description – Description of this section for easier readability

• keywords – the keywords to be set for this section. Each element should be a Keyword object. This can be more cumbersome than simply using kwargs for building a class in a script, but is more convenient for the class instantiations of CP2K sections (see below).

• section_parameters – the section parameters for this section. Section parameters are specialized keywords that modify the behavior of the section overall. Most sections do not have section parameters, but some do. Unlike normal Keywords, these are specified as strings and not as Keyword objects.

• location – the path to the section in the form ‘SECTION/SUBSECTION1/SUBSECTION3’, example for QS module: ‘FORCE_EVAL/DFT/QS’. This location is used to automatically determine if a subsection requires a supersection to be activated.

• verbose – Controls how much is printed to Cp2k input files (Also see Keyword). If True, then a description of the section will be printed with it as a comment (if description is set). Default=True.

• alias – An alias for this class to use in place of the name.

• keyword (kwargs are interpreted as) –

• as (value pairs and added to the keywords array) –

• objects (Keyword) –

class Xc_Functional(functionals: Iterable | None = None, keywords: dict | None = None, subsections: dict | None = None, **kwargs)[source]

Bases: Section

Defines the XC functional(s) to use.

Basic object representing a CP2K Section. Sections activate different parts of the calculation. For example, FORCE_EVAL section will activate CP2K’s ability to calculate forces.

Parameters:

• name – The name of the section (must match name in CP2K)

• subsections – A dictionary of subsections that are nested in this section. Format is {‘NAME’: Section(args, **kwargs). The name you chose for ‘NAME’ to index that subsection does not *have to be the same as the section’s true name, but we recommend matching them. You can specify a blank dictionary if there are no subsections, or if you want to insert the subsections later.

• repeats – Whether or not this section can be repeated. Most sections cannot. Default=False.

• description – Description of this section for easier readability

• keywords – the keywords to be set for this section. Each element should be a Keyword object. This can be more cumbersome than simply using kwargs for building a class in a script, but is more convenient for the class instantiations of CP2K sections (see below).

• section_parameters – the section parameters for this section. Section parameters are specialized keywords that modify the behavior of the section overall. Most sections do not have section parameters, but some do. Unlike normal Keywords, these are specified as strings and not as Keyword objects.

• location – the path to the section in the form ‘SECTION/SUBSECTION1/SUBSECTION3’, example for QS module: ‘FORCE_EVAL/DFT/QS’. This location is used to automatically determine if a subsection requires a supersection to be activated.

• verbose – Controls how much is printed to Cp2k input files (Also see Keyword). If True, then a description of the section will be printed with it as a comment (if description is set). Default=True.

• alias – An alias for this class to use in place of the name.

• keyword (kwargs are interpreted as) –

• as (value pairs and added to the keywords array) –

• objects (Keyword) –

pymatgen.io.cp2k.outputs module

This module defines the Cp2k output parser along with a few other functions for parsing cp2k-related outputs.

class Cp2kOutput(filename, verbose=False, auto_load=False)[source]

Bases: object

Class for parsing output file from CP2K. The CP2K output file is very flexible in the way that it is returned. This class will automatically parse parameters that should always be present, but other parsing features may be called depending on the run type.

Initialize the Cp2kOutput object.

Parameters:

• filename – (str) Name of the CP2K output file to parse

• verbose – (bool) Whether or not to parse with verbosity (will parse lots of data that may not be useful)

• auto_load (bool) – Whether or not to automatically load basic info like energies and structures.

as_dict()[source]

Return dictionary representation of the output.

property band_structure: BandStructure[source]

Returns band structure object if it has been parsed.

property calculation_type[source]

Returns the calculation type (what io.vasp.outputs calls run_type).

property charge: float[source]

Get charge from the input file.

property complete_dos: CompleteDos[source]

Returns complete dos object if it has been parsed.

property completed[source]

Did the calculation complete.

convergence()[source]

Check whether or not the SCF and geometry optimization cycles converged.

property cp2k_version[source]

The cp2k version used in the calculation.

property is_hubbard: bool[source]

Returns True if hubbard +U correction was used.

property is_metal: bool[source]

Was a band gap found? i.e. is it a metal.

property is_molecule: bool[source]

True if the cp2k output was generated for a molecule (i.e. no periodicity in the cell).

property multiplicity: int[source]

Get the spin multiplicity from input file.

property num_warnings[source]

How many warnings showed up during the run.

parse_atomic_kind_info()[source]

Parse info on what atomic kinds are present and what basis/pseudopotential is describing each of them.

parse_bandstructure(bandstructure_filename=None) → None[source]

Parse a CP2K bandstructure file.

Parameters:

• bandstructure_filename – Filename containing bandstructure info. If

• provided (not) –

• by (then the pmg name of "BAND.bs" will be assumed) –

• parser. (the filename) –

parse_cell_params()[source]

Parse the lattice parameters (initial) from the output file.

parse_chi_tensor(chi_filename=None)[source]

Parse the magnetic susceptibility tensor.

parse_cp2k_params()[source]

Parse the CP2K general parameters from CP2K output file into a dictionary.

parse_dft_params()[source]

Parse the DFT parameters (as well as functional, HF, vdW params).

parse_dos(dos_file=None, pdos_files=None, ldos_files=None)[source]

Parse the dos files produced by cp2k calculation. CP2K produces different files based on the input file rather than assimilating them all into one file.

One file type is the overall DOS file, which is used for k-point calculations. For non-kpoint calculation, the overall DOS is generally not calculated, but the element-projected pDOS is. Separate files are created for each spin channel and each atom kind. If requested, cp2k can also do site/local projected dos (ldos). Each site requested will have a separate file for each spin channel (if spin polarized calculation is performed).

If possible, this function will assimilate the ldos files into a CompleteDos object. Either provide a list of PDOS file paths, or use glob to find the .pdos_ALPHA extension in the calculation directory.

Parameters:

• dos_file (str) – Name of the dos file, otherwise will be inferred

• pdos_files (list) – list of pdos file paths, otherwise they will be inferred

• ldos_files (list) – list of ldos file paths, otherwise they will be inferred

parse_energies()[source]

Get the total energy from a CP2K calculation. Presently, the energy reported in the trajectory (pos.xyz) file takes precedence over the energy reported in the main output file. This is because the trajectory file keeps track of energies in between restarts, while the main output file may or may not depending on whether a particular machine overwrites or appends it.

parse_files()[source]

Identify files present in the directory with the cp2k output file. Looks for trajectories, dos, and cubes.

parse_forces()[source]

Get the forces from the forces file, or from the main output file.

parse_global_params()[source]

Parse the GLOBAL section parameters from CP2K output file into a dictionary.

parse_gtensor(gtensor_filename=None)[source]

Parse a file containing g tensor.

parse_hirshfeld()[source]

Parse the hirshfeld population analysis for each step.

parse_homo_lumo()[source]

Find the HOMO - LUMO gap in [eV]. Returns the last value. For gaps/eigenvalues decomposed by spin up/spin down channel and over many ionic steps, see parse_mo_eigenvalues().

parse_hyperfine(hyperfine_filename=None)[source]

Parse a file containing hyperfine coupling tensors for each atomic site.

parse_initial_structure()[source]

Parse the initial structure from the main cp2k output file.

parse_input()[source]

Load in the input set from the input file (if it can be found).

parse_ionic_steps()[source]

Parse the ionic step info. If already parsed, this will just assimilate.

parse_mo_eigenvalues()[source]

Parse the MO eigenvalues from the cp2k output file. Will get the eigenvalues (and band gap) at each ionic step (if more than one exist).

Everything is decomposed by spin channel. If calculation was performed without spin polarization, then only Spin.up will be present, which represents the average of up and down.

parse_mulliken()[source]

Parse the mulliken population analysis info for each step

parse_nmr_shift()[source]

Parse NMR calculation.

parse_opt_steps()[source]

Parse the geometry optimization information.

parse_overlap_condition()[source]

Retrieve the overlap condition number

parse_plus_u_params()[source]

Parse the DFT+U params.

parse_qs_params()[source]

Parse the DFT parameters (as well as functional, HF, vdW params).

parse_raman()[source]

Parse raman calculation.

parse_scf_opt()[source]

Parse the SCF cycles (not usually important).

parse_scf_params()[source]

Retrieve the most import SCF parameters: the max number of scf cycles (max_scf), the convergence cutoff for scf (eps_scf),.

parse_stresses()[source]

Get the stresses from stress file, or from the main output file.

parse_structures(trajectory_file=None, lattice_file=None)[source]

Parses the structures from a cp2k calculation. Static calculations simply use the initial structure. For calculations with ionic motion, the function will look for the appropriate trajectory and lattice files based on naming convention. If no file is given, and no file is found, it is assumed that the lattice/structure remained constant, and the initial lattice/structure is used. Cp2k does not output the trajectory in the main output file by default, so non static calculations have to reference the trajectory file.

parse_tddfpt()[source]

Parse TDDFPT calculation.

parse_timing()[source]

Parse the timing info (how long did the run take).

parse_total_numbers()[source]

Parse total numbers (not usually important).

property project_name: str[source]

What project name was used for this calculation.

ran_successfully()[source]

Sanity checks that the program ran successfully. Looks at the bottom of the CP2K output file for the “PROGRAM ENDED” line, which is printed when successfully ran. Also grabs the number of warnings issued.

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

This function originally comes from pymatgen.io.vasp.outputs Outcar class.

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_table_pattern(header_pattern, row_pattern, footer_pattern, postprocess=<class 'str'>, attribute_name=None, last_one_only=True, strip=None)[source]

This function originally comes from pymatgen.io.vasp.outputs Outcar class.

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.

• strip (list) – Whether or not to strip contents out of the file before reading for a table pattern. This is mainly used by parse_scf_opt(), to strip HFX info out of the SCF loop start or DFT+U warnings out of the SCF loop iterations.

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 the capturing group is defined without name in row_pattern, or a dict in case that named capturing groups are defined by row_pattern.

property run_type[source]

What type of run (Energy, MD, etc.) was performed.

property spin_polarized: bool[source]

Was the calculation spin polarized.