pymatgen.entries package

Submodules

pymatgen.entries.compatibility module

This module implements Compatibility corrections for mixing runs of different functionals.

class AnionCorrection(*args, **kwargs)[source]

Bases: AnionCorrection

Correct anion energies to obtain the right formation energies. Note that this depends on calculations being run within the same input set.

Used by legacy MaterialsProjectCompatibility and MITCompatibility.

Parameters:

• config_file – Path to the selected compatibility.yaml config file.

• correct_peroxide – Specify whether peroxide/superoxide/ozonide corrections are to be applied or not.

_abc_impl = <_abc._abc_data object>[source]

class AqueousCorrection(*args, **kwargs)[source]

Bases: AqueousCorrection

This class implements aqueous phase compound corrections for elements and H2O.

Used only by MITAqueousCompatibility.

Parameters:

• config_file – Path to the selected compatibility.yaml config file.

• error_file – Path to the selected compatibilityErrors.yaml config file.

_abc_impl = <_abc._abc_data object>[source]

class Compatibility[source]

Bases: MSONable

Abstract Compatibility class, not intended for direct use. Compatibility classes are used to correct the energies of an entry or a set of entries. All Compatibility classes must implement get_adjustments() method.

_abc_impl = <_abc._abc_data object>[source]

static explain(entry)[source]

Prints an explanation of the energy adjustments applied by the Compatibility class. Inspired by the “explain” methods in many database methodologies.

Parameters:

entry – A ComputedEntry.

abstract get_adjustments(entry: ComputedEntry | ComputedStructureEntry) → list[pymatgen.entries.computed_entries.EnergyAdjustment][source]

Get the energy adjustments for a ComputedEntry.

This method must generate a list of EnergyAdjustment objects of the appropriate type (constant, composition-based, or temperature-based) to be applied to the ComputedEntry, and must raise a CompatibilityError if the entry is not compatible.

Parameters:

entry – A ComputedEntry object.

Returns:

A list of EnergyAdjustment to be applied to the

Entry.

Return type:

list[EnergyAdjustment]

Raises:

CompatibilityError if the entry is not compatible –

process_entries(entries: AnyComputedEntry | list[AnyComputedEntry], clean: bool = True, verbose: bool = False, inplace: bool = True, on_error: Literal['ignore', 'warn', 'raise'] = 'ignore') → list[AnyComputedEntry][source]

Process a sequence of entries with the chosen Compatibility scheme.

Warning: This method changes entries in place! All changes can be undone and original entries restored by setting entry.energy_adjustments = [].

Parameters:

• entries (AnyComputedEntry | list[AnyComputedEntry]) – A sequence of Computed(Structure)Entry objects.

• clean (bool) – Whether to remove any previously-applied energy adjustments. If True, all EnergyAdjustment are removed prior to processing the Entry. Defaults to True.

• verbose (bool) – Whether to display progress bar for processing multiple entries. Defaults to False.

• inplace (bool) – Whether to adjust input entries in place. Defaults to True.

• on_error ('ignore' | 'warn' | 'raise') – What to do when get_adjustments(entry) raises CompatibilityError. Defaults to ‘ignore’.

Returns:

Adjusted entries. Entries in the original list incompatible with

chosen correction scheme are excluded from the returned list.

Return type:

list[AnyComputedEntry]

process_entry(entry: ComputedEntry, **kwargs) → ComputedEntry | None[source]

Process a single entry with the chosen Corrections. Note that this method will change the data of the original entry.

Parameters:

• entry – A ComputedEntry object.

• **kwargs – Will be passed to process_entries().

Returns:

An adjusted entry if entry is compatible, else None.

exception CompatibilityError[source]

Bases: Exception

Exception class for Compatibility. Raised by attempting correction on incompatible calculation.

class Correction[source]

Bases: object

A Correction class is a pre-defined scheme for correction a computed entry based on the type and chemistry of the structure and the calculation parameters. All Correction classes must implement a correct_entry method.

_abc_impl = <_abc._abc_data object>[source]

correct_entry(entry)[source]

Corrects a single entry.

Parameters:

entry – A ComputedEntry object.

Returns:

An processed entry.

Raises:

CompatibilityError if entry is not compatible. –

abstract get_correction(entry: ComputedEntry | ComputedStructureEntry) → EnergyAdjustment[source]

Returns correction and uncertainty for a single entry.

Parameters:

entry – A ComputedEntry object.

Returns:

The energy correction to be applied and the uncertainty of the correction.

Raises:

CompatibilityError if entry is not compatible. –

class CorrectionsList(corrections: Sequence[Correction])[source]

Bases: Compatibility

The CorrectionsList class combines a list of corrections to be applied to an entry or a set of entries. Note that some of the Corrections have interdependencies. For example, PotcarCorrection must always be used before any other compatibility. Also, AnionCorrection(“MP”) must be used with PotcarCorrection(“MP”) (similarly with “MIT”). Typically, you should use the specific MaterialsProjectCompatibility and MITCompatibility subclasses instead.

Parameters:

corrections (list[Correction]) – Correction objects to apply.

_abc_impl = <_abc._abc_data object>[source]

explain(entry)[source]

Prints an explanation of the corrections that are being applied for a given compatibility scheme. Inspired by the “explain” methods in many database methodologies.

Parameters:

entry – A ComputedEntry.

get_adjustments(entry: ComputedEntry | ComputedStructureEntry) → list[pymatgen.entries.computed_entries.EnergyAdjustment][source]

Get the list of energy adjustments to be applied to an entry.

get_corrections_dict(entry: ComputedEntry | ComputedStructureEntry) → tuple[dict[str, float], dict[str, float]][source]

Returns the correction values and uncertainties applied to a particular entry.

Parameters:

entry – A ComputedEntry object.

Returns:

Map from correction names to values

(1st) and uncertainties (2nd).

Return type:

tuple[dict[str, float], dict[str, float]]

get_explanation_dict(entry)[source]

Provides an explanation dict of the corrections that are being applied for a given compatibility scheme. Inspired by the “explain” methods in many database methodologies.

Parameters:

entry – A ComputedEntry.

Returns:

(dict) of the form {“Compatibility”: “string”, “Uncorrected_energy”: float, “Corrected_energy”: float, “correction_uncertainty:” float, “Corrections”: [{“Name of Correction”: { “Value”: float, “Explanation”: “string”, “Uncertainty”: float}]}

class GasCorrection(*args, **kwargs)[source]

Bases: GasCorrection

Correct gas energies to obtain the right formation energies. Note that this depends on calculations being run within the same input set. Used by legacy MaterialsProjectCompatibility and MITCompatibility.

Parameters:

config_file – Path to the selected compatibility.yaml config file.

_abc_impl = <_abc._abc_data object>[source]

class MITAqueousCompatibility(compat_type: Literal['GGA', 'Advanced'] = 'Advanced', correct_peroxide: bool = True, check_potcar_hash: bool = False)[source]

Bases: CorrectionsList

This class implements the GGA/GGA+U mixing scheme, which allows mixing of entries. Note that this should only be used for VASP calculations using the MIT parameters (see pymatgen.io.vasp.sets MITVaspInputSet). Using this compatibility scheme on runs with different parameters is not valid.

Parameters:

• compat_type – Two options, GGA or Advanced. GGA means all GGA+U entries are excluded. Advanced means mixing scheme is implemented to make entries compatible with each other, but entries which are supposed to be done in GGA+U will have the equivalent GGA entries excluded. For example, Fe oxides should have a U value under the Advanced scheme. A GGA Fe oxide run will therefore be excluded under the scheme.

• correct_peroxide – Specify whether peroxide/superoxide/ozonide corrections are to be applied or not.

• check_potcar_hash (bool) – Use potcar hash to verify potcars are correct.

_abc_impl = <_abc._abc_data object>[source]

class MITCompatibility(compat_type: Literal['GGA', 'Advanced'] = 'Advanced', correct_peroxide: bool = True, check_potcar_hash: bool = False)[source]

Bases: CorrectionsList

This class implements the GGA/GGA+U mixing scheme, which allows mixing of entries. Note that this should only be used for VASP calculations using the MIT parameters (see pymatgen.io.vasp.sets MITVaspInputSet). Using this compatibility scheme on runs with different parameters is not valid.

Parameters:

• compat_type – Two options, GGA or Advanced. GGA means all GGA+U entries are excluded. Advanced means mixing scheme is implemented to make entries compatible with each other, but entries which are supposed to be done in GGA+U will have the equivalent GGA entries excluded. For example, Fe oxides should have a U value under the Advanced scheme. A GGA Fe oxide run will therefore be excluded under the scheme.

• correct_peroxide – Specify whether peroxide/superoxide/ozonide corrections are to be applied or not.

• check_potcar_hash (bool) – Use potcar hash to verify potcars are correct.

_abc_impl = <_abc._abc_data object>[source]

class MaterialsProject2020Compatibility(*args, **kwargs)[source]

Bases: MaterialsProject2020Compatibility

This class implements the Materials Project 2020 energy correction scheme, which incorporates uncertainty quantification and allows for mixing of GGA and GGA+U entries (see References).

Note that this scheme should only be applied to VASP calculations that use the Materials Project input set parameters (see pymatgen.io.vasp.sets.MPRelaxSet). Using this compatibility scheme on calculations with different parameters is not valid.

Note: While the correction scheme is largely composition-based, the energy corrections applied to ComputedEntry and ComputedStructureEntry can differ for O and S-containing structures if entry.data[‘oxidation_states’] is not populated or explicitly set. This occurs because pymatgen will use atomic distances to classify O and S anions as superoxide/peroxide/oxide and sulfide/polysulfide, resp. when oxidation states are not provided. If you want the most accurate corrections possible, supply pre-defined oxidation states to entry.data or pass ComputedStructureEntry.

Parameters:

• compat_type –

  Two options, GGA or Advanced. GGA means all GGA+U entries are excluded. Advanced means the GGA/GGA+U mixing scheme of Jain et al. (see References) is implemented. In this case, entries which are supposed to be calculated in GGA+U (i.e., transition metal oxides and fluorides) will have the corresponding GGA entries excluded. For example, Fe oxides should have a U value under the Advanced scheme. An Fe oxide run in GGA will therefore be excluded.

  To use the “Advanced” type, Entry.parameters must contain a “hubbards” key which is a dict of all non-zero Hubbard U values used in the calculation. For example, if you ran a Fe2O3 calculation with Materials Project parameters, this would look like entry.parameters[“hubbards”] = {“Fe”: 5.3}. If the “hubbards” key is missing, a GGA run is assumed. Entries obtained from the MaterialsProject database will automatically have these fields populated. Default: “Advanced”

• correct_peroxide – Specify whether peroxide/superoxide/ozonide corrections are to be applied or not. If false, all oxygen-containing compounds are assigned the ‘oxide’ correction. Default: True

• check_potcar (bool) – Check that the POTCARs used in the calculation are consistent with the Materials Project parameters. False bypasses this check altogether. Default: True Can also be disabled globally by running pmg config –add PMG_POTCAR_CHECKS false.

• check_potcar_hash (bool) – Use potcar hash to verify POTCAR settings are consistent with MPRelaxSet. If False, only the POTCAR symbols will be used. Default: False

• config_file (Path) – Path to the selected compatibility.yaml config file. If None, defaults to MP2020Compatibility.yaml distributed with pymatgen.

References

Wang, A., Kingsbury, R., McDermott, M., Horton, M., Jain. A., Ong, S.P.,

Dwaraknath, S., Persson, K. A framework for quantifying uncertainty in DFT energy corrections. Scientific Reports 11: 15496, 2021. https://doi.org/10.1038/s41598-021-94550-5

Jain, A. et al. Formation enthalpies by mixing GGA and GGA + U calculations.

Phys. Rev. B - Condens. Matter Mater. Phys. 84, 1-10 (2011).

_abc_impl = <_abc._abc_data object>[source]

class MaterialsProjectAqueousCompatibility(*args, **kwargs)[source]

Bases: MaterialsProjectAqueousCompatibility

This class implements the Aqueous energy referencing scheme for constructing Pourbaix diagrams from DFT energies, as described in Persson et al.

This scheme applies various energy adjustments to convert DFT energies into Gibbs free energies of formation at 298 K and to guarantee that the experimental formation free energy of H2O is reproduced. Briefly, the steps are:

1. Beginning with the DFT energy of O2, adjust the energy of H2 so that the experimental reaction energy of -2.458 eV/H2O is reproduced.

2. Add entropy to the DFT energy of any compounds that are liquid or gaseous at room temperature

3. Adjust the DFT energies of solid hydrate compounds (compounds that contain water, e.g. FeO.nH2O) such that the energies of the embedded H2O molecules are equal to the experimental free energy

The above energy adjustments are computed dynamically based on the input Entries.

References

K.A. Persson, B. Waldwick, P. Lazic, G. Ceder, Prediction of solid-aqueous equilibria: Scheme to combine first-principles calculations of solids with experimental aqueous states, Phys. Rev. B - Condens. Matter Mater. Phys. 85 (2012) 1-12. doi:10.1103/PhysRevB.85.235438.

Initialize the MaterialsProjectAqueousCompatibility class.

Note that this class requires as inputs the ground-state DFT energies of O2 and H2O, plus the value of any energy adjustments applied to an H2O molecule. If these parameters are not provided in __init__, they can be automatically populated by including ComputedEntry for the ground state of O2 and H2O in a list of entries passed to process_entries. process_entries will fail if one or the other is not provided.

Parameters:

• solid_compat – Compatibility scheme used to pre-process solid DFT energies prior to applying aqueous energy adjustments. May be passed as a class (e.g. MaterialsProject2020Compatibility) or an instance (e.g., MaterialsProject2020Compatibility()). If None, solid DFT energies are used as-is. Default: MaterialsProject2020Compatibility

• o2_energy – The ground-state DFT energy of oxygen gas, including any adjustments or corrections, in eV/atom. If not set, this value will be determined from any O2 entries passed to process_entries. Default: None

• h2o_energy – The ground-state DFT energy of water, including any adjustments or corrections, in eV/atom. If not set, this value will be determined from any H2O entries passed to process_entries. Default: None

• h2o_adjustments – Total energy adjustments applied to one water molecule, in eV/atom. If not set, this value will be determined from any H2O entries passed to process_entries. Default: None

_abc_impl = <_abc._abc_data object>[source]

class MaterialsProjectCompatibility(compat_type: str = 'Advanced', correct_peroxide: bool = True, check_potcar_hash: bool = False)[source]

Bases: CorrectionsList

This class implements the GGA/GGA+U mixing scheme, which allows mixing of entries. Note that this should only be used for VASP calculations using the MaterialsProject parameters (see pymatgen.io.vasp.sets.MPVaspInputSet). Using this compatibility scheme on runs with different parameters is not valid.

Parameters:

• compat_type – Two options, GGA or Advanced. GGA means all GGA+U entries are excluded. Advanced means mixing scheme is implemented to make entries compatible with each other, but entries which are supposed to be done in GGA+U will have the equivalent GGA entries excluded. For example, Fe oxides should have a U value under the Advanced scheme. A GGA Fe oxide run will therefore be excluded under the scheme.

• correct_peroxide – Specify whether peroxide/superoxide/ozonide corrections are to be applied or not.

• check_potcar_hash (bool) – Use potcar hash to verify potcars are correct.

• silence_deprecation (bool) – Silence deprecation warning. Defaults to False.

_abc_impl = <_abc._abc_data object>[source]

class PotcarCorrection(input_set: type[pymatgen.io.vasp.sets.VaspInputSet], check_potcar: bool = True, check_hash: bool = False)[source]

Bases: Correction

Checks that POTCARs are valid within a pre-defined input set. This ensures that calculations performed using different InputSets are not compared against each other.

Entry.parameters must contain a “potcar_symbols” key that is a list of all POTCARs used in the run. Again, using the example of an Fe2O3 run using Materials Project parameters, this would look like entry.parameters[“potcar_symbols”] = [‘PAW_PBE Fe_pv 06Sep2000’, ‘PAW_PBE O 08Apr2002’].

Parameters:

• input_set (InputSet) – object used to generate the runs (used to check for correct potcar symbols).

• check_potcar (bool) – If False, bypass the POTCAR check altogether. Defaults to True. Can also be disabled globally by running pmg config –add PMG_POTCAR_CHECKS false.

• check_hash (bool) – If True, uses the potcar hash to check for valid potcars. If false, uses the potcar symbol (less reliable). Defaults to False.

Raises:

ValueError – if check_potcar=True and entry does not contain “potcar_symbols” key.

_abc_impl = <_abc._abc_data object>[source]

get_correction(entry: ComputedEntry | ComputedStructureEntry) → ufloat[source]

Parameters:

entry (AnyComputedEntry) – ComputedEntry or ComputedStructureEntry.

Raises:

• ValueError – If entry does not contain “potcar_symbols” key.

• CompatibilityError – If entry has wrong potcar hash/symbols.

Returns:

0.0 +/- 0.0 (from uncertainties package)

Return type:

ufloat

class UCorrection(*args, **kwargs)[source]

Bases: UCorrection

This class implements the GGA/GGA+U mixing scheme, which allows mixing of entries. Entry.parameters must contain a “hubbards” key which is a dict of all non-zero Hubbard U values used in the calculation. For example, if you ran a Fe2O3 calculation with Materials Project parameters, this would look like entry.parameters[“hubbards”] = {“Fe”: 5.3} If the “hubbards” key is missing, a GGA run is assumed.

It should be noted that ComputedEntries assimilated using the pymatgen.apps.borg package and obtained via the MaterialsProject REST interface using the pymatgen.matproj.rest package will automatically have these fields populated.

Parameters:

• config_file – Path to the selected compatibility.yaml config file.

• input_set – InputSet object (to check for the +U settings)

• compat_type – Two options, GGA or Advanced. GGA means all GGA+U entries are excluded. Advanced means mixing scheme is implemented to make entries compatible with each other, but entries which are supposed to be done in GGA+U will have the equivalent GGA entries excluded. For example, Fe oxides should have a U value under the Advanced scheme. A GGA Fe oxide run will therefore be excluded under the scheme.

• error_file – Path to the selected compatibilityErrors.yaml config file.

_abc_impl = <_abc._abc_data object>[source]

pymatgen.entries.computed_entries module

This module implements equivalents of the basic ComputedEntry objects, which is the basic entity that can be used to perform many analyses. ComputedEntries contain calculated information, typically from VASP or other electronic structure codes. For example, ComputedEntries can be used as inputs for phase diagram analysis.

class CompositionEnergyAdjustment(adj_per_atom, n_atoms, uncertainty_per_atom=nan, name='', cls=None, description='Composition-based energy adjustment')[source]

Bases: EnergyAdjustment

An energy adjustment applied to a ComputedEntry based on the atomic composition. Used in various DFT energy correction schemes.

Parameters:

• adj_per_atom – float, energy adjustment to apply per atom, in eV/atom

• n_atoms – float or int, number of atoms.

• uncertainty_per_atom – float, uncertainty in energy adjustment to apply per atom, in eV/atom. (Default: np.nan)

• name – str, human-readable name of the energy adjustment. (Default: “”)

• cls – dict, Serialized Compatibility class used to generate the energy adjustment. (Default: None)

• description – str, human-readable explanation of the energy adjustment.

property explain[source]

Return an explanation of how the energy adjustment is calculated.

normalize(factor)[source]

Normalize energy adjustment (in place), dividing value/uncertainty by a factor. :param factor: factor to divide by.

property uncertainty[source]

Return the value of the energy adjustment in eV.

property value[source]

Return the value of the energy adjustment in eV.

class ComputedEntry(composition: Composition | str | dict[str, float], energy: float, correction: float = 0.0, energy_adjustments: list | None = None, parameters: dict | None = None, data: dict | None = None, entry_id: object | None = None)[source]

Bases: Entry

Lightweight Entry object for computed data. Contains facilities for applying corrections to the energy attribute and for storing calculation parameters.

Initializes a ComputedEntry.

Parameters:

• composition (Composition) – Composition of the entry. For flexibility, this can take the form of all the typical input taken by a Composition, including a {symbol: amt} dict, a string formula, and others.

• energy (float) – Energy of the entry. Usually the final calculated energy from VASP or other electronic structure codes.

• correction (float) – Manually set an energy correction, will ignore energy_adjustments if specified.

• energy_adjustments – An optional list of EnergyAdjustment to be applied to the energy. This is used to modify the energy for certain analyses. Defaults to None.

• parameters – An optional dict of parameters associated with the entry. Defaults to None.

• data – An optional dict of any additional data associated with the entry. Defaults to None.

• entry_id – An optional id to uniquely identify the entry.

_abc_impl = <_abc._abc_data object>[source]

as_dict() → dict[source]

Returns:

MSONable dict.

copy() → ComputedEntry[source]

Returns a copy of the ComputedEntry.

property correction: float[source]

Returns: float: the total energy correction / adjustment applied to the entry,

in eV.

property correction_per_atom: float[source]

Returns: float: the total energy correction / adjustment applied to the entry,

normalized by atoms (units of eV/atom).

property correction_uncertainty: float[source]

Returns: float: the uncertainty of the energy adjustments applied to the entry, in eV.

property correction_uncertainty_per_atom: float[source]

Returns: float: the uncertainty of the energy adjustments applied to the entry,

normalized by atoms (units of eV/atom).

property energy: float[source]

the corrected energy of the entry.

Type:

return

classmethod from_dict(d) → ComputedEntry[source]

Parameters:

d – Dict representation.

Returns:

ComputedEntry

normalize(mode: Literal['formula_unit', 'atom'] = 'formula_unit') → ComputedEntry[source]

Normalize the entry’s composition and energy.

Parameters:

mode ("formula_unit" | "atom") – “formula_unit” (the default) normalizes to composition.reduced_formula. “atom” normalizes such that the composition amounts sum to 1.

property uncorrected_energy: float[source]

Returns: float: the uncorrected energy of the entry.

property uncorrected_energy_per_atom: float[source]

Returns: float: the uncorrected energy of the entry, normalized by atoms

(units of eV/atom).

class ComputedStructureEntry(structure: Structure, energy: float, correction: float = 0.0, composition: Composition | str | dict[str, float] | None = None, energy_adjustments: list | None = None, parameters: dict | None = None, data: dict | None = None, entry_id: object | None = None)[source]

Bases: ComputedEntry

A heavier version of ComputedEntry which contains a structure as well. The structure is needed for some analyses.

Initializes a ComputedStructureEntry.

Parameters:

• structure (Structure) – The actual structure of an entry.

• energy (float) – Energy of the entry. Usually the final calculated energy from VASP or other electronic structure codes.

• correction (float, optional) – A correction to the energy. This is mutually exclusive with energy_adjustments, i.e. pass either or neither but not both. Defaults to 0.

• composition (Composition) – Composition of the entry. For flexibility, this can take the form of all the typical input taken by a Composition, including a {symbol: amt} dict, a string formula, and others.

• energy_adjustments – An optional list of EnergyAdjustment to be applied to the energy. This is used to modify the energy for certain analyses. Defaults to None.

• parameters – An optional dict of parameters associated with the entry. Defaults to None.

• data – An optional dict of any additional data associated with the entry. Defaults to None.

• entry_id – An optional id to uniquely identify the entry.

_abc_impl = <_abc._abc_data object>[source]

as_dict() → dict[source]

Returns:

MSONable dict.

copy() → ComputedStructureEntry[source]

Returns a copy of the ComputedStructureEntry.

classmethod from_dict(d) → ComputedStructureEntry[source]

Parameters:

d – Dict representation.

Returns:

ComputedStructureEntry

normalize(mode: Literal['formula_unit', 'atom'] = 'formula_unit') → ComputedStructureEntry[source]

Normalize the entry’s composition and energy. The structure remains unchanged.

Parameters:

mode ("formula_unit" | "atom") – “formula_unit” (the default) normalizes to composition.reduced_formula. “atom” normalizes such that the composition amounts sum to 1.

property structure: Structure[source]

the structure of the entry.

Type:

return

class ConstantEnergyAdjustment(value, uncertainty=nan, name='Constant energy adjustment', cls=None, description='Constant energy adjustment')[source]

Bases: EnergyAdjustment

A constant energy adjustment applied to a ComputedEntry. Useful in energy referencing schemes such as the Aqueous energy referencing scheme.

Parameters:

• value – float, value of the energy adjustment in eV

• uncertainty – float, uncertainty of the energy adjustment in eV. (Default: np.nan)

• name – str, human-readable name of the energy adjustment. (Default: Constant energy adjustment)

• cls – dict, Serialized Compatibility class used to generate the energy adjustment. (Default: None)

• description – str, human-readable explanation of the energy adjustment.

property explain[source]

Return an explanation of how the energy adjustment is calculated.

normalize(factor)[source]

Normalize energy adjustment (in place), dividing value/uncertainty by a factor. :param factor: factor to divide by.

class EnergyAdjustment(value, uncertainty=nan, name='Manual adjustment', cls=None, description='')[source]

Bases: MSONable

Lightweight class to contain information about an energy adjustment or energy correction.

Parameters:

• value (float) – value of the energy adjustment in eV

• uncertainty (float) – uncertainty of the energy adjustment in eV. Default: np.nan

• name (str) – human-readable name of the energy adjustment. (Default: Manual adjustment)

• cls (dict) – Serialized Compatibility class used to generate the energy adjustment. Defaults to {}.

• description (str) – human-readable explanation of the energy adjustment.

abstract property explain[source]

Return an explanation of how the energy adjustment is calculated.

abstract normalize(factor)[source]

Scale the value of the current energy adjustment by factor in-place.

This method is utilized in ComputedEntry.normalize() to scale the energies to a formula unit basis (e.g. E_Fe6O9 = 3 x E_Fe2O3).

property uncertainty[source]

Return the uncertainty in the value of the energy adjustment in eV.

property value[source]

Return the value of the energy correction in eV.

class GibbsComputedStructureEntry(structure: Structure, formation_enthalpy_per_atom: float, temp: float = 300, gibbs_model: Literal['SISSO'] = 'SISSO', composition: Composition | None = None, correction: float = 0.0, energy_adjustments: list | None = None, parameters: dict | None = None, data: dict | None = None, entry_id: object | None = None)[source]

Bases: ComputedStructureEntry

An extension to ComputedStructureEntry which includes the estimated Gibbs free energy of formation via a machine-learned model.

Parameters:

• structure (Structure) – The pymatgen Structure object of an entry.

• formation_enthalpy_per_atom (float) – Formation enthalpy of the entry;

• be (must) – calculated using phase diagram construction (eV)

• temp (float) – Temperature in Kelvin. If temperature is not selected from one of [300, 400, 500, … 2000 K], then free energies will be interpolated. Defaults to 300 K.

• gibbs_model ('SISSO') – Model for Gibbs Free energy. “SISSO”, the descriptor created by Bartel et al. (2018) – see reference in documentation, is currently the only supported option.

• composition (Composition) – The composition of the entry. Defaults to None.

• correction (float) – A correction to be applied to the energy. Defaults to 0.

• energy_adjustments (list) – A list of energy adjustments to be applied to the energy. Defaults to None.

• parameters (dict) – An optional dict of parameters associated with the entry. Defaults to None.

• data (dict) – An optional dict of any additional data associated with the entry. Defaults to None.

• entry_id – An optional id to uniquely identify the entry.

_abc_impl = <_abc._abc_data object>[source]

static _g_delta_sisso(vol_per_atom, reduced_mass, temp) → float[source]

G^delta as predicted by SISSO-learned descriptor from Eq. (4) in Bartel et al. (2018).

Parameters:

• vol_per_atom (float) – volume per atom [Å^3/atom]

• reduced_mass (float) – as calculated with pair-wise sum formula [amu]

• temp (float) – Temperature [K]

Returns:

G^delta [eV/atom]

Return type:

float

static _reduced_mass(structure) → float[source]

Reduced mass as calculated via Eq. 6 in Bartel et al. (2018).

Parameters:

structure (Structure) – The pymatgen Structure object of the entry.

Returns:

reduced mass (amu)

Return type:

float

_sum_g_i() → float[source]

Sum of the stoichiometrically weighted chemical potentials of the elements at specified temperature, as acquired from “g_els.json”.

Returns:

sum of weighted chemical potentials [eV]

Return type:

float

as_dict() → dict[source]

Returns:

MSONable dict.

classmethod from_dict(d) → GibbsComputedStructureEntry[source]

Parameters:

d – Dict representation.

Returns:

GibbsComputedStructureEntry

classmethod from_entries(entries, temp=300, gibbs_model='SISSO') → list[pymatgen.entries.computed_entries.GibbsComputedStructureEntry][source]

Constructor method for initializing GibbsComputedStructureEntry objects from T = 0 K ComputedStructureEntry objects, as acquired from a thermochemical database e.g. The Materials Project.

Parameters:

• entries ([ComputedStructureEntry]) – List of ComputedStructureEntry objects, as downloaded from The Materials Project API.

• temp (int) – Temperature [K] for estimating Gibbs free energy of formation.

• gibbs_model (str) – Gibbs model to use; currently the only option is “SISSO”.

Returns:

list of new entries which replace the orig.

entries with inclusion of Gibbs free energy of formation at the specified temperature.

Return type:

[GibbsComputedStructureEntry]

classmethod from_pd(pd, temp=300, gibbs_model='SISSO') → list[pymatgen.entries.computed_entries.GibbsComputedStructureEntry][source]

Constructor method for initializing a list of GibbsComputedStructureEntry objects from an existing T = 0 K phase diagram composed of ComputedStructureEntry objects, as acquired from a thermochemical database; (e.g.. The Materials Project).

Parameters:

• pd (PhaseDiagram) – T = 0 K phase diagram as created in pymatgen. Must contain ComputedStructureEntry objects.

• temp (int) – Temperature [K] for estimating Gibbs free energy of formation.

• gibbs_model (str) – Gibbs model to use; currently the only option is “SISSO”.

Returns:

list of new entries which replace the orig.

entries with inclusion of Gibbs free energy of formation at the specified temperature.

Return type:

[GibbsComputedStructureEntry]

gf_sisso() → float[source]

Gibbs Free Energy of formation as calculated by SISSO descriptor from Bartel et al. (2018). Units: eV (not normalized).

WARNING: This descriptor only applies to solids. The implementation here attempts to detect and use downloaded NIST-JANAF data for common experimental gases (e.g. CO2) where possible. Note that experimental data is only for Gibbs Free Energy of formation, so expt. entries will register as having a formation enthalpy of 0.

Reference: Bartel, C. J., Millican, S. L., Deml, A. M., Rumptz, J. R., Tumas, W., Weimer, A. W., … Holder, A. M. (2018). Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry. Nature Communications, 9(1), 4168. https://doi.org/10.1038/s41467-018-06682-4

Returns:

the difference between formation enthalpy (T=0 K, Materials Project) and the predicted Gibbs free energy of formation (eV)

Return type:

float

class ManualEnergyAdjustment(value)[source]

Bases: ConstantEnergyAdjustment

A manual energy adjustment applied to a ComputedEntry.

Parameters:

value – float, value of the energy adjustment in eV.

class TemperatureEnergyAdjustment(adj_per_deg, temp, n_atoms, uncertainty_per_deg=nan, name='', cls=None, description='Temperature-based energy adjustment')[source]

Bases: EnergyAdjustment

An energy adjustment applied to a ComputedEntry based on the temperature. Used, for example, to add entropy to DFT energies.

Parameters:

• adj_per_deg – float, energy adjustment to apply per degree K, in eV/atom

• temp – float, temperature in Kelvin

• n_atoms – float or int, number of atoms

• uncertainty_per_deg – float, uncertainty in energy adjustment to apply per degree K, in eV/atom. (Default: np.nan)

• name – str, human-readable name of the energy adjustment. (Default: “”)

• cls – dict, Serialized Compatibility class used to generate the energy adjustment. (Default: None)

• description – str, human-readable explanation of the energy adjustment.

property explain[source]

Return an explanation of how the energy adjustment is calculated.

normalize(factor)[source]

Normalize energy adjustment (in place), dividing value/uncertainty by a factor. :param factor: factor to divide by.

property uncertainty[source]

Return the value of the energy adjustment in eV.

property value[source]

Return the value of the energy correction in eV.

pymatgen.entries.correction_calculator module

This module calculates corrections for the species listed below, fitted to the experimental and computed entries given to the CorrectionCalculator constructor.

class CorrectionCalculator(species: list[str] | None = None, max_error: float = 0.1, allow_unstable: float | bool = 0.1, exclude_polyanions: list[str] | None = None)[source]

Bases: object

A CorrectionCalculator contains experimental and computed entries which it uses to compute corrections.

It graphs residual errors after applying the computed corrections and creates the MPCompatibility.yaml file the Correction classes use.

species[source]

list of species that corrections are being calculated for

exp_compounds[source]

list of dictionaries which each contain a compound’s formula and experimental data

calc_compounds[source]

dictionary of ComputedEntry objects

corrections[source]

list of corrections in same order as species list

corrections_std_error[source]

list of the variances of the corrections in same order as species list

corrections_dict[source]

dictionary of format {‘species’: (value, uncertainty)} for easier correction lookup

Initializes a CorrectionCalculator.

Parameters:

• species – list of species to calculate corrections for

• max_error – maximum tolerable relative uncertainty in experimental energy. Compounds with relative uncertainty greater than this value will be excluded from the fit

• allow_unstable – whether unstable entries are to be included in the fit. If True, all compounds will be included regardless of their energy above hull. If False or a float, compounds with energy above hull greater than the given value (defaults to 0.1 eV/atom) will be excluded

• exclude_polyanions – a list of polyanions that contain additional sources of error that may negatively influence the quality of the fitted corrections. Compounds with these polyanions will be excluded from the fit

compute_corrections(exp_entries: list, calc_entries: dict) → dict[source]

Computes the corrections and fills in correction, corrections_std_error, and corrections_dict.

Parameters:

• exp_entries – list of dictionary objects with the following keys/values: {“formula”: chemical formula, “exp energy”: formation energy in eV/formula unit, “uncertainty”: uncertainty in formation energy}

• calc_entries – dictionary of computed entries, of the form {chemical formula: ComputedEntry}

Raises:

ValueError – calc_compounds is missing an entry

compute_from_files(exp_gz: str, comp_gz: str)[source]

Parameters:

• exp_gz – name of .json.gz file that contains experimental data data in .json.gz file should be a list of dictionary objects with the following keys/values: {“formula”: chemical formula, “exp energy”: formation energy in eV/formula unit, “uncertainty”: uncertainty in formation energy}

• comp_gz – name of .json.gz file that contains computed entries data in .json.gz file should be a dictionary of {chemical formula: ComputedEntry}.

graph_residual_error() → Figure[source]

Graphs the residual errors for all compounds after applying computed corrections.

graph_residual_error_per_species(specie: str) → Figure[source]

Graphs the residual errors for each compound that contains specie after applying computed corrections.

Parameters:

specie – the specie/group that residual errors are being plotted for

Raises:

ValueError – the specie is not a valid specie that this class fits corrections for

make_yaml(name: str = 'MP2020', dir: str | None = None) → None[source]

Creates the _name_Compatibility.yaml that stores corrections as well as _name_CompatibilityUncertainties.yaml for correction uncertainties.

Parameters:

• name – str, alternate name for the created .yaml file. Default: “MP2020”

• dir – str, directory in which to save the file. Pass None (default) to save the file in the current working directory.

pymatgen.entries.entry_tools module

This module implements functions to perform various useful operations on entries, such as grouping entries by structure.

class EntrySet(entries: Iterable[PDEntry | ComputedEntry | ComputedStructureEntry])[source]

Bases: MutableSet, MSONable

A convenient container for manipulating entries. Allows for generating subsets, dumping into files, etc.

Parameters:

entries – All the entries.

_abc_impl = <_abc._abc_data object>[source]

add(element)[source]

Add an entry.

Parameters:

element – Entry

as_dict() → dict[Literal['entries'], list[Entry]][source]

Returns MSONable dict.

property chemsys: set[source]

Returns: set representing the chemical system, e.g., {“Li”, “Fe”, “P”, “O”}.

discard(element)[source]

Discard an entry.

Parameters:

element – Entry

classmethod from_csv(filename: str)[source]

Imports PDEntries from a csv.

Parameters:

filename – Filename to import from.

Returns:

List of Elements, List of PDEntries

get_subset_in_chemsys(chemsys: list[str])[source]

Returns an EntrySet containing only the set of entries belonging to a particular chemical system (in this definition, it includes all sub systems). For example, if the entries are from the Li-Fe-P-O system, and chemsys=[“Li”, “O”], only the Li, O, and Li-O entries are returned.

Parameters:

chemsys – Chemical system specified as list of elements. E.g., [“Li”, “O”]

Returns:

EntrySet

property ground_states: set[source]

A set containing only the entries that are ground states, i.e., the lowest energy per atom entry at each composition.

is_ground_state(entry) → bool[source]

Boolean indicating whether a given Entry is a ground state.

remove_non_ground_states()[source]

Removes all non-ground state entries, i.e., only keep the lowest energy per atom entry at each composition.

to_csv(filename: str, latexify_names: bool = False) → None[source]

Exports PDEntries to a csv.

Parameters:

• filename – Filename to write to.

• entries – PDEntries to export.

• latexify_names – Format entry names to be LaTex compatible, e.g., Li_{2}O

_get_host(structure, species_to_remove)[source]

_perform_grouping(args)[source]

group_entries_by_composition(entries, sort_by_e_per_atom=True)[source]

Given a sequence of Entry-like objects, group them by composition and

optionally sort by energy above hull.

Parameters:

• entries (List) – Sequence of Entry-like objects.

• sort_by_e_per_atom (bool) – Whether to sort the grouped entries by energy per atom (lowest energy first). Default True.

Returns:

Sequence of sequence of entries by composition. e.g, [[ entry1, entry2], [entry3, entry4, entry5]]

group_entries_by_structure(entries, species_to_remove=None, ltol=0.2, stol=0.4, angle_tol=5, primitive_cell=True, scale=True, comparator=None, ncpus=None)[source]

Given a sequence of ComputedStructureEntries, use structure fitter to group them by structural similarity.

Parameters:

• entries – Sequence of ComputedStructureEntries.

• species_to_remove – Sometimes you want to compare a host framework (e.g., in Li-ion battery analysis). This allows you to specify species to remove before structural comparison.

• ltol (float) – Fractional length tolerance. Default is 0.2.

• stol (float) – Site tolerance in Angstrom. Default is 0.4 Angstrom.

• angle_tol (float) – Angle tolerance in degrees. Default is 5 degrees.

• primitive_cell (bool) – If true: input structures will be reduced to primitive cells prior to matching. Defaults to True.

• scale – Input structures are scaled to equivalent volume if true; For exact matching, set to False.

• comparator – A comparator object implementing an equals method that declares equivalency of sites. Default is SpeciesComparator, which implies rigid species mapping.

• ncpus – Number of cpus to use. Use of multiple cpus can greatly improve fitting speed. Default of None means serial processing.

Returns:

Sequence of sequence of entries by structural similarity. e.g, [[ entry1, entry2], [entry3, entry4, entry5]]

pymatgen.entries.exp_entries module

This module defines Entry classes for containing experimental data.

class ExpEntry(composition, thermodata, temperature=298)[source]

Bases: PDEntry, MSONable

An lightweight ExpEntry object containing experimental data for a composition for many purposes. Extends a PDEntry so that it can be used for phase diagram generation and reaction calculation.

Current version works only with solid phases and at 298K. Further extensions for temperature dependence are planned.

Parameters:

• composition – Composition of the entry. For flexibility, this can take the form of all the typical input taken by a Composition, including a {symbol: amt} dict, a string formula, and others.

• thermodata – A sequence of ThermoData associated with the entry.

• temperature – A temperature for the entry in Kelvin. Defaults to 298K.

_abc_impl = <_abc._abc_data object>[source]

as_dict()[source]

Returns:

MSONable dict

classmethod from_dict(d)[source]

Parameters:

d – Dict representation.

Returns:

ExpEntry

pymatgen.entries.mixing_scheme module

This module implements Compatibility corrections for mixing runs of different functionals.

class MaterialsProjectDFTMixingScheme(structure_matcher: StructureMatcher | None = None, run_type_1: str = 'GGA(+U)', run_type_2: str = 'R2SCAN', compat_1: Compatibility | None = <pymatgen.entries.compatibility.cached_class.<locals>._decorated object>, compat_2: Compatibility | None = None, fuzzy_matching: bool = True, check_potcar: bool = True)[source]

Bases: Compatibility

This class implements the Materials Project mixing scheme, which allows mixing of energies from different DFT functionals. Note that this should only be used for VASP calculations using the MaterialsProject parameters (e.g. MPRelaxSet or MPScanRelaxSet). Using this compatibility scheme on runs with different parameters may lead to unexpected results.

This is the scheme used by the Materials Project to generate Phase Diagrams containing a mixture of GGA(+U) and R2SCAN calculations. However in principle it can be used to mix energies from any two functionals.

Instantiate the mixing scheme. The init method creates a generator class that contains relevant settings (e.g., StructureMatcher instance, Compatibility settings for each functional) for processing groups of entries.

Parameters:

• structure_matcher (StructureMatcher) – StructureMatcher object used to determine whether calculations from different functionals describe the same material.

• run_type_1 –

  The first DFT run_type. Typically this is the majority or run type or the “base case” onto which the other calculations are referenced. Valid choices are any run_type recognized by Vasprun.run_type, such as “LDA”, “GGA”, “GGA+U”, “PBEsol”, “SCAN”, or “R2SCAN”. The class will ignore any entries that have a run_type different than run_type_1 or run_type_2.

  The list of run_type_1 entries provided to process_entries MUST form a complete Phase Diagram in order for the mixing scheme to work. If this condition is not satisfied, processing the entries will fail.

  Note that the special string “GGA(+U)” (default) will treat both GGA and GGA+U calculations as a single type. This option exists because GGA/GGA+U mixing is already handled by MaterialsProject2020Compatibility.

• run_type_2 – The second DFT run_type. Typically this is the run_type that is ‘preferred’ but has fewer calculations. If run_type_1 and run_type_2 calculations exist for all materials, run_type_2 energies will be used (hence the ‘preferred’ status). The class will ignore any entries that have a run_type different than run_type_1 or run_type_2.

• compat_1 – Compatibility class used to pre-process entries of run_type_1. Defaults to MaterialsProjectCompatibility2020.

• compat_2 – Compatibility class used to pre-process entries of run_type_2. Defaults to None.

• fuzzy_matching – Whether to use less strict structure matching logic for diatomic elements O2, N2, F2, H2, and Cl2 as well as I and Br. Outputs of DFT relaxations using different functionals frequently fail to structure match for these elements even though they come from the same original material. Fuzzy structure matching considers the materials equivalent if the formula, number of sites, and space group are all identical. If there are multiple materials of run_type_2 that satisfy these criteria, the one with lowest energy is considered to match.

• check_potcar – Whether to ensure the POTCARs used for the run_type_1 and run_type_2 calculations are the same. This is useful for ensuring that the mixing scheme is not used on calculations that used different POTCARs, which can lead to unphysical results. Defaults to True. Has no effect if neither compat_1 nor compat_2 have a check_potcar attribute. Can also be disabled globally by running pmg config –add PMG_POTCAR_CHECKS false.

_abc_impl = <_abc._abc_data object>[source]

_filter_and_sort_entries(entries, verbose=True)[source]

Given a single list of entries, separate them by run_type and return two lists, one containing only entries of each run_type.

_populate_df_row(struct_group, comp, sg, n, pd_type_1, pd_type_2, all_entries)[source]

Helper function to populate a row of the mixing state DataFrame, given a list of matched structures.

static display_entries(entries)[source]

Generate a pretty printout of key properties of a list of ComputedEntry.

get_adjustments(entry, mixing_state_data: pd.DataFrame | None = None)[source]

Returns the corrections applied to a particular entry. Note that get_adjustments is not intended to be called directly in the R2SCAN mixing scheme. Call process_entries instead, and it will pass the required arguments to get_adjustments.

Parameters:

• entry – A ComputedEntry object. The entry must be a member of the list of entries used to create mixing_state_data.

• mixing_state_data – A DataFrame containing information about which Entries correspond to the same materials, which are stable on the phase diagrams of the respective run_types, etc. Can be generated from a list of entries using MaterialsProjectDFTMixingScheme.get_mixing_state_data. This argument is included to facilitate use of the mixing scheme in high-throughput databases where an alternative to get_mixing_state_data is desirable for performance reasons. In general, it should always be left at the default value (None) to avoid inconsistencies between the mixing state data and the properties of the ComputedStructureEntry.

Returns:

Energy adjustments to be applied to entry.

Return type:

[EnergyAdjustment]

Raises:

CompatibilityError if the DFT mixing scheme cannot be applied to the entry. –

get_mixing_state_data(entries: list[pymatgen.entries.computed_entries.ComputedStructureEntry])[source]

Generate internal state data to be passed to get_adjustments.

Parameters:

entries – The list of ComputedStructureEntry to process. It is assumed that the entries have already been filtered using _filter_and_sort_entries() to remove any irrelevant run types, apply compat_1 and compat_2, and confirm that all have unique entry_id.

Returns:

A pandas DataFrame that contains information associating structures from

different functionals with specific materials and establishing how many run_type_1 ground states have been computed with run_type_2. The DataFrame contains one row for each distinct material (Structure), with the following columns:

formula: str the reduced_formula spacegroup: int the spacegroup num_sites: int the number of sites in the Structure entry_id_1: the entry_id of the run_type_1 entry entry_id_2: the entry_id of the run_type_2 entry run_type_1: Optional[str] the run_type_1 value run_type_2: Optional[str] the run_type_2 value energy_1: float or nan the ground state energy in run_type_1 in eV/atom energy_2: float or nan the ground state energy in run_type_2 in eV/atom is_stable_1: bool whether this material is stable on the run_type_1 PhaseDiagram hull_energy_1: float or nan the energy of the run_type_1 hull at this composition in eV/atom hull_energy_2: float or nan the energy of the run_type_1 hull at this composition in eV/atom

None: Returns None if the supplied ComputedStructureEntry are insufficient for applying

the mixing scheme.

Return type:

DataFrame

process_entries(entries: AnyComputedEntry | list[AnyComputedEntry], clean: bool = True, verbose: bool = True, inplace: bool = True, mixing_state_data=None) → list[AnyComputedEntry][source]

Process a sequence of entries with the DFT mixing scheme. Note that this method will change the data of the original entries.

Parameters:

• entries –

  ComputedEntry or [ComputedEntry]. Pass all entries as a single list, even if they are computed with different functionals or require different preprocessing. This list will automatically be filtered based on run_type_1 and run_type_2, and processed according to compat_1 and compat_2.

  Note that under typical use, when mixing_state_data=None, the entries MUST be ComputedStructureEntry. They will be matched using structure_matcher.

• clean (bool) – Whether to remove any previously-applied energy adjustments. If True, all EnergyAdjustment are removed prior to processing the Entry. Default is True.

• verbose (bool) – Whether to print verbose error messages about the mixing scheme. Default is True.

• inplace (bool) – Whether to adjust input entries in place. Default is True.

• mixing_state_data – A DataFrame containing information about which Entries correspond to the same materials, which are stable on the phase diagrams of the respective run_types, etc. If None (default), it will be generated from the list of entries using MaterialsProjectDFTMixingScheme.get_mixing_state_data. This argument is included to facilitate use of the mixing scheme in high-throughput databases where an alternative to get_mixing_state_data is desirable for performance reasons. In general, it should always be left at the default value (None) to avoid inconsistencies between the mixing state data and the properties of the ComputedStructureEntry in entries.

Returns:

Adjusted entries. Entries in the original list incompatible with

chosen correction scheme are excluded from the returned list.

Return type:

list[AnyComputedEntry]