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])

Bases: MutableSet, MSONable

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

Parameters:

entries – All the entries.

add(element)

Add an entry.

Parameters:

element – Entry

as_dict() → dict[Literal['entries'], list[pymatgen.entries.Entry]]

Returns MSONable dict.

property chemsys: set

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

discard(element)

Discard an entry.

Parameters:

element – Entry

classmethod from_csv(filename: str)

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])

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

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

Boolean indicating whether a given Entry is a ground state

remove_non_ground_states()

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

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

group_entries_by_composition(entries, sort_by_e_per_atom=True)

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)

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]]