"""
Helper functions for parsing defect supercell calculations.
"""
import contextlib
import itertools
import os
import re
import warnings
from copy import deepcopy
from functools import lru_cache, partialmethod
from typing import cast
from xml.etree.ElementTree import Element as XML_Element
import numpy as np
from monty.io import reverse_readfile
from monty.serialization import loadfn
from pymatgen.analysis.defects.core import DefectType
from pymatgen.analysis.structure_matcher import get_linear_assignment_solution, pbc_shortest_vectors
from pymatgen.core.periodic_table import Element
from pymatgen.core.structure import Composition, Lattice, PeriodicSite, SiteCollection, Structure
from pymatgen.electronic_structure.core import Spin
from pymatgen.entries.computed_entries import ComputedStructureEntry
from pymatgen.io.vasp.inputs import POTCAR_STATS_PATH, UnknownPotcarWarning
from pymatgen.io.vasp.outputs import Locpot, Outcar, Procar, Vasprun, _parse_vasp_array
from pymatgen.util.typing import PathLike, SpeciesLike
from doped.core import DefectEntry, remove_site_oxi_state
@lru_cache(maxsize=1000) # cache POTCAR generation to speed up generation and writing
def _get_potcar_summary_stats() -> dict:
return loadfn(POTCAR_STATS_PATH)
[docs]
def find_archived_fname(fname, raise_error=True):
"""
Find a suitable filename, taking account of possible use of compression
software.
"""
if os.path.exists(fname):
return fname
# Check for archive files
for ext in [".gz", ".xz", ".bz", ".lzma"]:
if os.path.exists(fname + ext):
return fname + ext
if raise_error:
raise FileNotFoundError
return None
# has to be defined as staticmethod to be consistent with usage in pymatgen, alternatively could make
# fake custom class:
[docs]
@staticmethod # type: ignore[misc]
def parse_projected_eigen(
elem: XML_Element, parse_mag: bool = True
) -> tuple[dict[Spin, np.ndarray], np.ndarray | None]:
"""
Parse the projected eigenvalues from a ``Vasprun`` object (used during
initialisation), but excluding the projected magnetization for efficiency.
Note that following SK's PRs to ``pymatgen`` (#4359, #4360), parsing of
projected eigenvalues adds minimal additional cost to Vasprun parsing
(~1-5%), while parsing of projected magnetization can add ~30% cost.
This is a modified version of ``_parse_projected_eigen`` from
``pymatgen.io.vasp.outputs.Vasprun``, which allows skipping of projected
magnetization parsing in order to expedite parsing in ``doped``, as well as
some small adjustments to maximise efficiency.
Args:
elem (Element):
The XML element to parse, with projected eigenvalues/magnetization.
parse_mag (bool):
Whether to parse the projected magnetization. Default is ``True``.
Returns:
Tuple[Dict[Spin, np.ndarray], Optional[np.ndarray]]:
A dictionary of projected eigenvalues for each spin channel
(up/down), and the projected magnetization (if parsed).
"""
root = elem.find("array/set")
proj_eigen = {}
sets = root.findall("set") # type: ignore[union-attr]
for s in sets:
spin = int(re.match(r"spin(\d+)", s.attrib["comment"])[1]) # type: ignore[index]
if spin == 1 or (spin == 2 and len(sets) == 2):
spin_key = Spin.up if spin == 1 else Spin.down
elif parse_mag: # parse projected magnetization
spin_key = spin # {2:"x", 3:"y", 4:"z"}
else:
continue
proj_eigen[spin_key] = np.array(
[[_parse_vasp_array(sss) for sss in ss.findall("set")] for ss in s.findall("set")]
)
if len(proj_eigen) > 2:
# non-collinear magnetism (spin-orbit coupling) enabled, last three "spin channels" are the
# projected magnetization of the orbitals in the x, y, and z Cartesian coordinates:
proj_mag = np.stack([proj_eigen.pop(i) for i in range(2, 5)], axis=-1)
proj_eigen = {Spin.up: proj_eigen[Spin.up]}
else:
proj_mag = None
# here we _could_ round to 3 decimal places (and ensure rounding 0.0005 up to 0.001) to be _mostly_
# consistent with PROCAR values (still not 100% the same as e.g. 0.00047 will be rounded to 0.0005
# in vasprun, but 0.000 in PROCAR), but this is _reducing_ the accuracy so better not to do this,
# and accept that PROCAR results may not be as numerically robust
# proj_eigen = {k: np.round(v+0.00001, 3) for k, v in proj_eigen.items()}
elem.clear()
return proj_eigen, proj_mag
[docs]
def get_vasprun(vasprun_path: PathLike, parse_mag: bool = True, **kwargs):
"""
Read the ``vasprun.xml(.gz)`` file as a ``pymatgen`` ``Vasprun`` object.
"""
vasprun_path = str(vasprun_path) # convert to string if Path object
warnings.filterwarnings(
"ignore", category=UnknownPotcarWarning
) # Ignore unknown POTCAR warnings when loading vasprun.xml
# pymatgen assumes the default PBE with no way of changing this within get_vasprun())
warnings.filterwarnings(
"ignore", message="No POTCAR file with matching TITEL fields"
) # `message` only needs to match start of message
default_kwargs = {"parse_dos": False, "exception_on_bad_xml": False}
default_kwargs.update(kwargs)
Vasprun._parse_projected_eigen = partialmethod(parse_projected_eigen, parse_mag=parse_mag)
try:
with warnings.catch_warnings(record=True) as w:
vasprun = Vasprun(find_archived_fname(vasprun_path), **default_kwargs)
for warning in w:
if "XML is malformed" in str(warning.message):
warnings.warn(
f"vasprun.xml file at {vasprun_path} is corrupted/incomplete. Attempting to "
f"continue parsing but may fail!"
)
else: # show warning, preserving original category:
warnings.warn(warning.message, category=warning.category)
except FileNotFoundError as exc:
raise FileNotFoundError(
f"vasprun.xml not found at {vasprun_path}(.gz/.xz/.bz/.lzma). Needed for parsing calculation "
f"output!"
) from exc
return vasprun
[docs]
def get_locpot(locpot_path: PathLike):
"""
Read the ``LOCPOT(.gz)`` file as a ``pymatgen`` ``Locpot`` object.
"""
locpot_path = str(locpot_path) # convert to string if Path object
try:
locpot = Locpot.from_file(find_archived_fname(locpot_path))
except FileNotFoundError:
raise FileNotFoundError(
f"LOCPOT file not found at {locpot_path}(.gz/.xz/.bz/.lzma). Needed for calculating the "
f"Freysoldt (FNV) image charge correction!"
) from None
return locpot
def _get_outcar_path(outcar_path: PathLike, raise_error=True):
outcar_path = str(outcar_path) # convert to string if Path object
try:
return find_archived_fname(outcar_path)
except FileNotFoundError:
if raise_error:
raise FileNotFoundError(
f"OUTCAR file not found at {outcar_path}(.gz/.xz/.bz/.lzma). Needed for calculating the "
f"Kumagai (eFNV) image charge correction."
) from None
[docs]
def get_outcar(outcar_path: PathLike):
"""
Read the ``OUTCAR(.gz)`` file as a ``pymatgen`` ``Outcar`` object.
"""
outcar_path = _get_outcar_path(outcar_path)
return Outcar(outcar_path)
[docs]
def get_core_potentials_from_outcar(
outcar_path: PathLike, dir_type: str = "", total_energy: list | float | None = None
):
"""
Get the core potentials from the OUTCAR file, which are needed for the
Kumagai-Oba (eFNV) finite-size correction.
This parser skips the full ``pymatgen`` ``Outcar`` initialisation/parsing,
to expedite parsing and make it more robust (doesn't fail if ``OUTCAR`` is
incomplete, as long as it has the core potentials information).
Args:
outcar_path (PathLike):
The path to the OUTCAR file.
dir_type (str):
The type of directory the OUTCAR is in (e.g. ``bulk`` or
``defect``) for informative error messages.
total_energy (Optional[Union[list, float]]):
The already-parsed total energy for the structure. If provided,
will check that the total energy of the ``OUTCAR`` matches this
value / one of these values, and throw a warning if not.
Returns:
np.ndarray:
The core potentials from the last ionic step in the ``OUTCAR``.
"""
# initialise Outcar class without running __init__ method:
outcar = Outcar.__new__(Outcar)
outcar.filename = _get_outcar_path(outcar_path)
core_pots_list = outcar.read_avg_core_poten()
if not core_pots_list:
_raise_incomplete_outcar_error(outcar_path, dir_type=dir_type)
_check_outcar_energy(outcar_path, total_energy=total_energy)
return -1 * np.array(core_pots_list[-1]) # core potentials from last step
def _get_final_energy_from_outcar(outcar_path):
"""
Get the final total energy from an ``OUTCAR`` file, even if the calculation
was not completed.
Templated on the ``OUTCAR`` parsing code from ``pymatgen``, but works even
if the ``OUTCAR`` is incomplete.
"""
e0_pattern = re.compile(r"energy\(sigma->0\)\s*=\s+([\d\-\.]+)")
e0 = None
for line in reverse_readfile(outcar_path):
clean = line.strip()
if e0 is None and (match := e0_pattern.search(clean)):
e0 = float(match[1])
return e0
def _get_core_potentials_from_outcar_obj(
outcar: Outcar, dir_type: str = "", total_energy: list | float | None = None
):
if outcar.electrostatic_potential is None and not outcar.read_avg_core_poten():
_raise_incomplete_outcar_error(outcar, dir_type=dir_type)
_check_outcar_energy(outcar, total_energy=total_energy)
return -1 * np.array(outcar.electrostatic_potential) or -1 * np.array(outcar.read_avg_core_poten()[-1])
def _check_outcar_energy(outcar: Outcar | PathLike, total_energy: list | float | None = None):
if total_energy:
outcar_energy = (
outcar.final_energy if isinstance(outcar, Outcar) else _get_final_energy_from_outcar(outcar)
)
total_energy = total_energy if isinstance(total_energy, list) else [total_energy]
total_energies = set(np.round(total_energy, 3))
formatted_total_energy = "eV, ".join(f"{energy:.3f}" for energy in total_energies) + " eV"
if len(total_energies) == 2: # most cases, final energy and last electronic step energy
formatted_total_energy += "; final energy & last electronic step energy"
if not any(np.isclose(outcar_energy, energy, atol=0.025) for energy in total_energy):
# 0.025 eV tolerance
warnings.warn(
f"The total energies of the provided (bulk) `OUTCAR` ({outcar_energy:.3f} eV), "
f"used to obtain the atomic core potentials for the eFNV correction, and the "
f"`vasprun.xml` ({formatted_total_energy}), used for energies and structures, do not "
f"match. Please make sure the correct file combination is being used!"
)
def _raise_incomplete_outcar_error(outcar: PathLike | Outcar, dir_type: str = ""):
"""
Raise error about supplied ``OUTCAR`` not having atomic core potential
info.
Input outcar is either a path or a ``pymatgen`` ``Outcar`` object
"""
outcar_info = f"`OUTCAR` at {outcar}" if isinstance(outcar, PathLike) else "`OUTCAR` object"
dir_type = f"{dir_type} " if dir_type else ""
raise ValueError(
f"Unable to parse atomic core potentials from {dir_type}{outcar_info}. This can happen if "
f"`ICORELEVEL` was not set to 0 (= default) in the `INCAR`, the calculation was finished "
f"prematurely with a `STOPCAR`, or the calculation crashed. The Kumagai (eFNV) charge correction "
f"cannot be computed without this data!"
)
[docs]
def get_procar(procar_path: PathLike) -> Procar:
"""
Read the ``PROCAR(.gz)`` file as a ``pymatgen`` ``Procar`` object.
Previously, ``pymatgen`` ``Procar`` parsing did not support SOC
calculations, however this was updated in
https://github.com/materialsproject/pymatgen/pull/3890 to use code from
``easyunfold`` (https://smtg-bham.github.io/easyunfold -- a package for
unfolding electronic band structures for symmetry-broken / defect /
dopant systems, with many plotting & analysis tools).
"""
try:
procar_path = find_archived_fname(str(procar_path)) # convert to string if Path object
except FileNotFoundError:
raise FileNotFoundError(f"PROCAR file not found at {procar_path}(.gz/.xz/.bz/.lzma)!") from None
return Procar(procar_path)
def _get_output_files_and_check_if_multiple(
output_file: PathLike = "vasprun.xml", path: PathLike = "."
) -> tuple[PathLike, bool]:
"""
Search for all files with filenames matching ``output_file``, case-
insensitive.
Args:
output_file (PathLike):
The filename to search for (case-insensitive). Should be either
``vasprun.xml``, ``OUTCAR``, ``LOCPOT`` or ``PROCAR``.
path (PathLike):
The path to the directory to search in.
Returns:
Tuple[PathLike, bool]:
The path to the identified file, and a boolean indicating whether
multiple files were found.
"""
if output_file.lower() == "vasprun.xml":
search_patterns = ["vasprun", ".xml"]
else:
search_patterns = [output_file.lower()]
files = os.listdir(path)
output_files = [
filename
for filename in files
if all(i in filename.lower() for i in search_patterns) and not filename.startswith(".")
]
# sort by direct match to {output_file}, direct match to {output_file}.gz, then alphabetically:
if output_files := sorted(
output_files,
key=lambda x: (x == output_file, x == f"{output_file}.gz", x),
reverse=True,
):
output_path = os.path.join(path, output_files[0])
return (output_path, True) if len(output_files) > 1 else (output_path, False)
return (
os.path.join(path, output_file),
False,
) # so `get_X()` will raise an informative FileNotFoundError
[docs]
def get_defect_type_and_composition_diff(
bulk: Structure | Composition, defect: Structure | Composition
) -> tuple[str, dict]:
"""
Get the difference in composition between a bulk structure and a defect
structure.
Args:
bulk (Union[Structure, Composition]):
The bulk structure or composition.
defect (Union[Structure, Composition]):
The defect structure or composition.
Returns:
tuple[str, Dict[str, int]]:
The defect type (``interstitial``, ``vacancy``, ``substitution`` or
``complex``) and the composition difference between the bulk and
defect structures as a dictionary.
"""
bulk_comp = bulk.composition if isinstance(bulk, Structure) else bulk
defect_comp = defect.composition if isinstance(defect, Structure) else defect
bulk_comp_dict = bulk_comp.get_el_amt_dict()
defect_comp_dict = defect_comp.get_el_amt_dict()
composition_diff = {
element: int(defect_amount - bulk_comp_dict.get(element, 0))
for element, defect_amount in defect_comp_dict.items()
if int(defect_amount - bulk_comp_dict.get(element, 0)) != 0
}
if len(composition_diff) == 1 and next(iter(composition_diff.values())) == 1:
defect_type = "interstitial"
elif len(composition_diff) == 1 and next(iter(composition_diff.values())) == -1:
defect_type = "vacancy"
elif len(composition_diff) == 2 and all(i in composition_diff.values() for i in [-1, 1]):
defect_type = "substitution"
else:
defect_type = "complex"
if len(composition_diff) > 5: # likely a mistake, warn user:
warnings.warn(
f"The composition difference between the bulk ({bulk_comp_dict}) and defect "
f"({defect_comp_dict}) structures is quite large, suggesting either a large complex defect "
f"or a mistake in the inputs. Beware!"
)
return defect_type, composition_diff
[docs]
def get_defect_type_and_site_indices(
bulk_supercell: Structure,
defect_supercell: Structure,
site_tol: float | None = None, # TODO: Change to 0.5 and add complex defect handling
abs_tol: bool = False,
use_oxi_states: bool = False,
use_rms: bool = False,
) -> tuple[str, list[int], list[int]]:
"""
Get the defect type, and indices of defect sites in the bulk (vacancies /
substitutions) and defect (interstitials / substitutions) supercells.
Defect sites are determined by matching sites in the bulk and defect
structures (by element and distances), according to ``site_tol``.
Args:
bulk_supercell (Structure):
The bulk supercell structure.
defect_supercell (Structure):
The defect supercell structure.
site_tol (float | None):
The (fractional) tolerance for matching sites between the defect
and bulk structures. If ``abs_tol`` is ``False`` (default), then
the distance threshold for matching is set to the product of
``site_tol`` and the shortest bond length in the bulk structure for
the given species, otherwise the value is used directly (as a
length in Å).
If set to ``None``, the defect is assumed to be a point defect, and
the largest site mismatch is assigned as the defect site.
Default is 0.5 (i.e. half the shortest bond length in the bulk
structure for the given species).
abs_tol (bool):
Whether to use ``site_tol`` as an absolute distance tolerance (in
Å) instead of a fractional tolerance (in terms of the shortest bond
length in the structure). Default is ``False``.
use_oxi_states (bool):
Whether to use the oxidation states of the sites in the bulk and
defect structures when considering matching sites (such that e.g.
``Fe3+`` and ``Fe2+`` would be considered different species).
Default is ``False``.
use_rms (bool):
Site mapping (using linear assignment) -- used to determine defect
sites -- will be that which minimises either the summed RMS
distances (if ``use_rms`` is ``True``) or just simple linear sum of
distances (if ``False``, default) between all paired sites.
Returns:
defect_type (str):
The type of defect as a string (``interstitial``, ``vacancy`` or
``substitution``).
missing_bulk_site_indices (list[int]):
Indices of sites in the bulk structure that do not match any site
in the defect structure (according to ``site_tol`` choice).
additional_defect_site_indices (list[int]):
Indices of sites in the defect structure that do not match any site
in the bulk structure (according to ``site_tol`` choice).
"""
bulk_composition = bulk_supercell.composition
defect_composition = defect_supercell.composition
try:
defect_type, comp_diff = get_defect_type_and_composition_diff(bulk_composition, defect_composition)
except RuntimeError as exc:
raise ValueError(
"Could not identify defect type from number of sites in structure: "
f"{len(bulk_supercell)} in bulk vs. {len(defect_supercell)} in defect?"
) from exc
if site_tol is None and defect_type == "complex":
raise ValueError(
f"Based on the composition difference between defect and bulk structures ({comp_diff}), "
f"the defect is a complex defect, but ``site_tol`` is set to ``None`` which enforces the "
f"assumption of a point defect. Please set ``site_tol`` to allow parsing of complex defect "
f"sites."
)
oxi_state_decorated = [ # if all sites in both structures are not oxi-state decorated / neutral
any(i in site.species_string for i in ["+", "-", "0"])
for site in [*bulk_supercell.sites, *defect_supercell.sites]
]
if len(set(oxi_state_decorated)) > 1 and use_oxi_states: # not consistent, ignore oxi states:
warnings.warn(
"`use_oxi_states` was set to `True`, but not all sites in the bulk and defect structures are "
"oxidation state decorated. Setting `use_oxi_states` to `False`."
)
use_oxi_states = False
elt_symbols = {
str(species) if use_oxi_states else species.symbol
for species in bulk_composition.elements + defect_composition.elements
}
additional_defect_site_indices = []
missing_bulk_site_indices = []
distance_matrix = bulk_supercell.distance_matrix
for elt_symbol in elt_symbols:
bulk_species_coords, bulk_species_indices = get_coords_and_idx_of_species(
bulk_supercell, elt_symbol, use_oxi_states=use_oxi_states
)
defect_species_coords, defect_species_indices = get_coords_and_idx_of_species(
defect_supercell, elt_symbol, use_oxi_states=use_oxi_states
)
if bulk_species_indices.size == 0: # extrinsic species
site_dist_tol = None
else:
species_distances = distance_matrix[bulk_species_indices]
species_min_dist = max(species_distances[np.nonzero(species_distances)].min(), 1)
site_dist_tol = site_tol if site_tol is None or abs_tol else site_tol * species_min_dist
site_mapping = _get_site_mapping_from_coords_and_indices(
bulk_species_coords,
defect_species_coords,
lattice=bulk_supercell.lattice,
s1_indices=bulk_species_indices,
s2_indices=defect_species_indices,
use_rms=use_rms,
)
defect_site_mappings = [
mapping
for mapping in site_mapping
if mapping[0] is None or (site_dist_tol is not None and mapping[0] > site_dist_tol)
]
for mapping in defect_site_mappings:
if mapping[1] is not None: # missing bulk site
missing_bulk_site_indices.append(mapping[1])
if mapping[2] is not None: # additional defect site (may be from same matched tuple if dist
additional_defect_site_indices.append(mapping[2]) # greater than site_dist_tol)
return defect_type, missing_bulk_site_indices, additional_defect_site_indices
[docs]
def get_coords_and_idx_of_species(
structure_or_sites: SiteCollection,
species_name: str,
frac_coords: bool = True,
use_oxi_states: bool = False,
) -> tuple[np.ndarray, np.ndarray]:
"""
Get arrays of the coordinates and indices of the given species in the
structure/list of sites.
"""
from doped.utils.efficiency import _parse_site_species_str
coords = []
idx = []
for i, site in enumerate(structure_or_sites):
if _parse_site_species_str(site, wout_charge=not use_oxi_states) == species_name:
coords.append(site.frac_coords if frac_coords else site.coords)
idx.append(i)
return np.array(coords), np.array(idx)
[docs]
def get_matching_site(
site: PeriodicSite | np.ndarray[float], structure: Structure, anonymous: bool = False, tol: float = 0.5
) -> PeriodicSite:
"""
Get the (closest) matching ``PeriodicSite`` in ``structure`` for the input
``site``, which can be a ``PeriodicSite`` or fractional coordinates.
If the closest matching site in ``structure`` is > ``tol`` Å (0.5 Å by
default) away from the input ``site`` coordinates, an error is raised.
Automatically accounts for possible differences in assigned oxidation
states, site property dicts etc.
Args:
site (PeriodicSite | np.ndarray[float]):
The site for which to find the closest matching site in
``structure``, either as a ``PeriodicSite`` or fractional
coordinates array. If fractional coordinates, then ``anonymous``
is set to ``True``.
structure (Structure):
The structure in which to search for matching sites to ``site``.
anonymous (bool):
Whether to use anonymous matching, allowing different
species/elements to match each other (i.e. just matching based on
coordinates). Default is ``False`` if ``site`` is a
``PeriodicSite``, and ``True`` if ``site`` is fractional
coordinates.
tol (float):
A distance tolerance (in Å), where an error will be thrown if the
closest matching site is > ``tol`` Å away from the input ``site``.
Default is 0.5 Å.
Returns:
PeriodicSite:
The closest matching site in ``structure`` to the input ``site``.
"""
if (
isinstance(site, PeriodicSite) and not anonymous
): # try directly match first if site in structure:
if site in structure:
return site
site_w_no_ox_state = deepcopy(site)
remove_site_oxi_state(site_w_no_ox_state)
site_w_no_ox_state.properties = {}
bulk_sites_w_no_ox_state = structure.copy().sites
for bulk_site in bulk_sites_w_no_ox_state:
remove_site_oxi_state(bulk_site)
bulk_site.properties = {}
if site_w_no_ox_state in bulk_sites_w_no_ox_state:
return structure.sites[bulk_sites_w_no_ox_state.index(site_w_no_ox_state)]
site_frac_coords = (
site.frac_coords if hasattr(site, "frac_coords") else np.array(site, dtype=float)
) # ensure site is in fractional coords
# else get closest site in structure, raising error if not within tol Å:
if isinstance(site, PeriodicSite) and not anonymous: # reduce to only matching species
candidate_frac_coords, candidate_indices = get_coords_and_idx_of_species(
structure, site.specie.symbol
)
else:
candidate_frac_coords = structure.frac_coords
candidate_indices = np.arange(len(structure))
closest_site_idx = candidate_indices[
np.argmin(structure.lattice.get_all_distances(site_frac_coords, candidate_frac_coords).ravel())
]
closest_site = structure.sites[closest_site_idx]
closest_site_dist = closest_site.distance_and_image_from_frac_coords(site_frac_coords)[0]
if closest_site_dist > tol:
raise ValueError(
f"Closest site to input defect site ({site}) in bulk supercell is {closest_site} "
f"with distance {closest_site_dist:.2f} Å (greater than {tol} Å and suggesting a likely "
f"mismatch in sites/structures here!)."
)
if (
not anonymous
and isinstance(site, PeriodicSite)
and site.specie.symbol != closest_site.specie.symbol
):
raise ValueError(
f"Closest site to input defect site ({site}) in bulk supercell is {closest_site} "
f"with distance {closest_site_dist:.2f} Å which is a different element! Set `anonymous=True` "
f"to allow matching of different elements/species if this is desired."
)
return closest_site
def _create_unrelaxed_defect_structure(
bulk_supercell: Structure,
defect_supercell: Structure,
bulk_site_idx: int | None = None,
defect_site_idx: int | None = None,
defect_coords: bool = False,
) -> Structure:
"""
Create the unrelaxed defect structure, which corresponds to the bulk
supercell with the unrelaxed defect site.
The unrelaxed defect site corresponds to the vacancy/substitution site in
the pristine (bulk) supercell for vacancies/substitutions, and the `final`
relaxed interstitial site for interstitials (as the assignment of their
initial site is ambiguous).
Args:
bulk_supercell (Structure):
The bulk structure.
defect_supercell (Structure):
The defect structure.
bulk_site_idx (int):
The index of the site in the bulk structure that corresponds to the
defect site in the defect structure.
defect_site_idx (int):
The index of the defect site to use in the unreleaxed defect
structure. Just for consistency with the relaxed defect structure.
defect_coords (bool):
Whether to use the fractional coordinates of the defect site in the
defect structure, or the bulk structure. Irrelevant for vacancies.
Parent functions in ``doped`` use ``True`` for interstitials, and
``False`` for substitutions (i.e. use bulk site coords).
Returns:
Structure:
The unrelaxed defect structure.
"""
unrelaxed_defect_structure = bulk_supercell.copy() # create unrelaxed defect structure
if bulk_site_idx is not None:
unrelaxed_defect_structure.remove_sites([bulk_site_idx])
if defect_site_idx is not None:
defect_site_in_defect = defect_supercell[defect_site_idx]
if not defect_coords and bulk_site_idx is not None:
defect_coords = bulk_supercell[bulk_site_idx].frac_coords
else:
defect_coords = defect_site_in_defect.frac_coords
unrelaxed_defect_structure.insert(defect_site_idx, defect_site_in_defect.species, defect_coords)
return unrelaxed_defect_structure
[docs]
def get_wigner_seitz_radius(lattice: Structure | Lattice) -> float:
"""
Calculates the Wigner-Seitz radius of the structure, which corresponds to
the maximum radius of a sphere fitting inside the cell.
Templated on the ``calc_max_sphere_radius`` function from ``pydefect``,
but rewritten to avoid calling ``vise`` which causes hanging on Windows.
(https://github.com/SMTG-Bham/doped/issues/147).
Args:
lattice (Union[Structure,Lattice]):
The lattice of the structure (either a ``pymatgen`` ``Structure``
or ``Lattice`` object).
Returns:
float:
The Wigner-Seitz radius of the structure.
"""
lattice_matrix = lattice.matrix if isinstance(lattice, Lattice) else lattice.lattice.matrix
distances = np.zeros(3, dtype=float) # copied over from pydefect v0.9.4; avoid vise issues
for i in range(3):
a_i_a_j = np.cross(lattice_matrix[i - 2], lattice_matrix[i - 1])
a_k = lattice_matrix[i]
distances[i] = abs(np.dot(a_i_a_j, a_k)) / np.linalg.norm(a_i_a_j)
return max(distances) / 2.0
[docs]
def check_atom_mapping_far_from_defect(
bulk_supercell: Structure,
defect_supercell: Structure,
defect_coords: np.ndarray[float],
coords_are_cartesian: bool = False,
displacement_tol: float = 0.5,
warning: bool | str = "verbose",
) -> bool:
"""
Check the displacement of atoms far from the determined defect site, and
warn the user if they are large (often indicates a mismatch between the
bulk and defect supercell definitions).
The threshold for identifying 'large' displacements is if the mean
displacement of any species is greater than ``displacement_tol`` Ångströms
for sites of that species outside the Wigner-Seitz radius of the defect in
the defect supercell. The Wigner-Seitz radius corresponds to the radius of
the largest sphere which can fit in the cell.
Args:
bulk_supercell (Structure):
The bulk structure.
defect_supercell (Structure):
The defect structure.
defect_coords (np.ndarray[float]):
The coordinates of the defect site.
coords_are_cartesian (bool):
Whether the defect coordinates are in Cartesian or fractional
coordinates. Default is ``False`` (fractional).
displacement_tol (float):
The tolerance for the displacement of atoms far from the defect
site, in Ångströms. Default is 0.5 Å.
warning (bool, str):
Whether to throw a warning if a mismatch is detected. If
``warning = "verbose"`` (default), the individual atomic
displacements are included in the warning message.
Returns:
bool:
Returns ``False`` if a mismatch is detected, else ``True``.
"""
far_from_defect_disps: dict[str, list[float]] = {site.specie.symbol: [] for site in bulk_supercell}
wigner_seitz_radius = get_wigner_seitz_radius(bulk_supercell.lattice)
defect_frac_coords = (
defect_coords
if not coords_are_cartesian
else bulk_supercell.lattice.get_fractional_coords(defect_coords)
)
bulk_sites_outside_or_at_ws_radius = [ # vectorised for fast computation
bulk_supercell[i]
for i in np.where(
bulk_supercell.lattice.get_all_distances(
bulk_supercell.frac_coords, defect_frac_coords
).ravel()
> np.max((wigner_seitz_radius - 1, 1))
)[0]
]
defect_sites_outside_wigner_radius = [ # vectorised for fast computation
defect_supercell[i]
for i in np.where(
defect_supercell.lattice.get_all_distances(
defect_supercell.frac_coords, defect_frac_coords
).ravel()
> wigner_seitz_radius
)[0]
]
for species in bulk_supercell.composition.elements: # divide and vectorise calc for efficiency
bulk_species_outside_near_ws_coords = get_coords_and_idx_of_species(
bulk_sites_outside_or_at_ws_radius, species.name
)[0]
defect_species_outside_ws_coords = get_coords_and_idx_of_species(
defect_sites_outside_wigner_radius, species.name
)[0]
if (
min(
len(bulk_species_outside_near_ws_coords),
len(defect_species_outside_ws_coords),
)
== 0
):
continue # if no sites of this species outside the WS radius, skip
displacement_dists = np.min(
bulk_supercell.lattice.get_all_distances(
defect_species_outside_ws_coords, bulk_species_outside_near_ws_coords
),
axis=1,
)
far_from_defect_disps[species.name].extend(np.round(displacement_dists, 2))
if far_from_defect_large_disps := {
specie: list
for specie, list in far_from_defect_disps.items()
if list and np.mean(list) > displacement_tol
}:
message = (
f"Detected atoms far from the defect site (>{wigner_seitz_radius:.2f} Å) with major "
f"displacements (>{displacement_tol} Å) in the defect supercell. This likely indicates a "
f"mismatch between the bulk and defect supercell definitions (-> see troubleshooting docs) or "
f"an unconverged supercell size, both of which could cause errors in parsing. The mean "
f"displacement of the following species, at sites far from the determined defect position, "
f"is >{displacement_tol} Å: {list(far_from_defect_large_disps.keys())}"
)
if warning == "verbose":
message += f", with displacements (Å): {far_from_defect_large_disps}"
if warning:
warnings.warn(message)
return False
return True
def _get_site_mapping_from_coords_and_indices(
s1_coords: np.ndarray[float],
s2_coords: np.ndarray[float],
s1_indices: np.ndarray[int] | None = None,
s2_indices: np.ndarray[int] | None = None,
lattice: Lattice | None = None,
use_rms: bool = False,
) -> list[tuple[float | None, int | None, int | None]]:
"""
Get the site mapping between two sets of coordinates and indices, based on
the shortest distances between sites.
The coordinates are treated as fractional coordinates if ``lattice`` is
provided, otherwise they are treated as Cartesian coordinates.
Args:
s1_coords (np.ndarray[float]):
The fractional coordinates of the first set of sites.
s2_coords (np.ndarray[float]):
The fractional coordinates of the second set of sites.
s1_indices (np.ndarray[int] | None):
The indices of the first set of sites. If ``None``, the indices are
assumed to be the range of the number of sites in ``s1_coords``.
s2_indices (np.ndarray[int] | None):
The indices of the second set of sites. If ``None``, the indices are
assumed to be the range of the number of sites in ``s2_coords``.
lattice (Lattice | None):
The lattice of the structures. If ``None``, the identity matrix is
used (corresponding to the assumption that the input coordinates
are Cartesian).
use_rms (bool):
The returned site mapping (using linear assignment) will be that
which minimises either the summed RMS distances (if ``use_rms`` is
``True``) or just simple linear sum of distances (if ``False``,
default) between all paired sites.
Returns:
list:
A list of lists containing the distance, index from ``s1_indices``
and index from ``s2_indices`` for each matched site.
"""
lattice = lattice or Lattice(np.eye(3))
if s1_indices is None:
s1_indices = np.arange(len(s1_coords))
if s2_indices is None:
s2_indices = np.arange(len(s2_coords))
s1_coords = np.array(s1_coords)
s2_coords = np.array(s2_coords)
for empty_coords, indices, tuple_idx in [(s1_coords, s2_indices, 2), (s2_coords, s1_indices, 1)]:
if empty_coords.size == 0: # handly case of empty input coords
if indices is None:
return [(None, None, None)]
return [(None, None if tuple_idx == 2 else i, None if tuple_idx == 1 else i) for i in indices]
s1_is_subset = len(s1_coords) < len(s2_coords)
subset_fcoords, subset_indices = (s1_coords, s1_indices) if s1_is_subset else (s2_coords, s2_indices)
superset_fcoords, superset_indices = (
(s2_coords, s2_indices) if s1_is_subset else (s1_coords, s1_indices)
)
_vecs, d_2 = pbc_shortest_vectors(lattice, subset_fcoords, superset_fcoords, return_d2=True)
dists = np.sqrt(d_2)
site_matches, _ = get_linear_assignment_solution(d_2 if use_rms else dists)
site_mapping = [ # site_matches -> matching superset indices, of len(subset)
(dists[i, j], subset_indices[i], superset_indices[j]) for i, j in enumerate(site_matches)
]
for missing_index in set(range(len(superset_fcoords))) - set(site_matches):
site_mapping.append((None, None, superset_indices[missing_index])) # unmatched sites
if not s1_is_subset: # swap tuple order, to match (dist, s1_index, s2_index)
site_mapping = [(dist, index2, index1) for dist, index1, index2 in site_mapping]
return site_mapping
[docs]
def get_site_mappings(
struct1: Structure,
struct2: Structure,
species: SpeciesLike | None = None,
allow_duplicates: bool = False,
threshold: float = 2.0,
anonymous: bool = False,
ignored_species: list[str] | None = None,
use_rms: bool = False,
) -> list[tuple[float | None, int | None, int | None]]:
"""
Get the site mappings between two structures (from ``struct1`` to
``struct2``), based on the shortest distances between sites.
The two structures may have different species orderings.
NOTE: This assumes that both structures have the same lattice definitions
(i.e. that they match, and aren't rigidly translated/rotated with respect
to each other), which is mostly the case unless we have a mismatching
defect/bulk supercell (in which case the
``check_atom_mapping_far_from_defect`` warning should be thrown anyway
during parsing).
Args:
struct1 (Structure):
The input structure.
struct2 (Structure):
The template structure.
species (str):
If provided, only sites of this species will be considered when
matching sites. Default is ``None`` (all species).
allow_duplicates (bool):
If ``True``, allow multiple sites in ``struct1`` to be matched to
the same site in ``struct2``. Default is ``False``.
threshold (float):
If the distance between a pair of matched sites is larger than
this, then a warning will be thrown. Default is 2.0 Å.
anonymous (bool):
If ``True``, the species of the sites will not be considered when
matching sites. Default is ``False`` (only matching species can be
matched together).
ignored_species (list[str]):
A list of species to ignore when matching sites. Default is no
species ignored.
use_rms (bool):
The returned site mapping (using linear assignment -- only
applicable when ``allow_duplicates`` is ``False``) will be that
which minimises either the summed RMS distances (if ``use_rms`` is
``True``) or just simple linear sum of distances (if ``False``,
default) between all paired sites.
Returns:
list:
A list of lists containing the distance, index in ``struct1`` and
index in ``struct2`` for each matched site.
"""
# Generate a site matching table between the input and the template
min_dist_with_index = []
s1_species_symbols = (
[
species.symbol
for species in struct1.composition.elements
if species.symbol not in (ignored_species or [])
]
if not anonymous
else [None]
)
for s1_species_symbol in s1_species_symbols:
if species is not None and s1_species_symbol != species and not anonymous:
continue
if anonymous:
s1_fcoords, s1_indices = struct1.frac_coords, np.arange(len(struct1))
s2_fcoords, s2_indices = struct2.frac_coords, np.arange(len(struct2))
else:
s1_fcoords, s1_indices = get_coords_and_idx_of_species(struct1, s1_species_symbol)
s2_fcoords, s2_indices = get_coords_and_idx_of_species(struct2, s1_species_symbol)
if not allow_duplicates:
min_dist_with_index.extend(
_get_site_mapping_from_coords_and_indices(
s1_fcoords,
s2_fcoords,
lattice=struct1.lattice,
s1_indices=s1_indices,
s2_indices=s2_indices,
use_rms=use_rms,
)
)
continue
dmat = struct1.lattice.get_all_distances(s1_fcoords, s2_fcoords)
for i, index in enumerate(s1_indices):
dists = dmat[i]
min_dist_idx = dists.argmin()
current_dist = dists[min_dist_idx]
s2_index = s2_indices[min_dist_idx]
min_dist_with_index.append((current_dist, index, s2_index))
if current_dist > threshold:
warnings.warn(
f"Large site displacement {current_dist:.2f} Å detected when matching atomic sites: "
f"{struct1[index]} -> {struct2[s2_index]}."
)
if not min_dist_with_index:
raise RuntimeError(
f"No matching sites for species {species} found between the two structures!\n"
f"Struct1 composition: {struct1.composition}, Struct2 composition: {struct2.composition}"
)
return min_dist_with_index
[docs]
def reorder_s2_like_s1(s1_structure: Structure, s2_structure: Structure, threshold=5.0) -> Structure:
"""
Reorder the atoms of a (relaxed) structure, ``s2_structure``, to match the
ordering of the atoms in ``s1_structure``.
s1/s2 structures may have a different species orderings.
NOTE: This assumes that both structures have the same lattice definitions
(i.e. that they match, and aren't rigidly translated/rotated with respect
to each other), which is mostly the case unless we have a mismatching
defect/bulk supercell (in which case the
``check_atom_mapping_far_from_defect`` warning should be thrown anyway
during parsing).
Args:
s1_structure (Structure):
The template structure.
s2_structure (Structure):
The structure to reorder, to match ``s1_structure``.
threshold (float):
If the distance between a pair of matched sites is larger than
this value in Å, then a warning will be thrown. Default is 5.0 Å.
Returns:
Structure:
``s2_structure`` reordered to match ``s1_structure``.
"""
# This function was previously used to ensure correct site matching when pulling site potentials for
# the eFNV Kumagai correction, though no longer used for this purpose. If threshold is set to a low
# value, it will raise a warning if there is a large site displacement detected.
if len(s2_structure) != len(s1_structure):
raise ValueError("Structure reordering not possible, structures have different number of sites.")
# Obtain site mapping between the initial_relax_structure and the unrelaxed structure
mapping = get_site_mappings(s1_structure, s2_structure, threshold=threshold)
mapping = sorted(mapping, key=lambda x: cast(int, x[1])) # sort by s1 index (to match s1 ordering)
# Reorder s2_structure so that it matches the ordering of s1_structure
reordered_sites = [s2_structure[mapping_tuple[-1]] for mapping_tuple in mapping]
# Avoid warning about selective_dynamics properties (can happen if user explicitly set "T T T" (or
# otherwise) for the bulk):
with warnings.catch_warnings():
warnings.filterwarnings("ignore", message="Not all sites have property")
return Structure.from_sites(reordered_sites)
def _compare_potcar_symbols(
bulk_potcar_symbols,
defect_potcar_symbols,
bulk_name="bulk",
defect_name="defect",
warn=True,
only_matching_elements=False,
):
"""
Check all POTCAR symbols in the bulk are the same in the defect
calculation.
Returns True if the symbols match, otherwise returns a list of the symbols
for the bulk and defect calculations.
"""
if only_matching_elements:
defect_elements = [symbol["titel"].split()[1].split("_")[0] for symbol in defect_potcar_symbols]
symbols_to_check = [
symbol
for symbol in bulk_potcar_symbols
if symbol["titel"].split()[1].split("_")[0] in defect_elements
]
else:
symbols_to_check = bulk_potcar_symbols
bulk_mismatch_list = []
defect_mismatch_list = []
for symbol in symbols_to_check:
if symbol["titel"] not in [symbol["titel"] for symbol in defect_potcar_symbols]:
if warn:
warnings.warn(
f"The POTCAR symbols for your {defect_name} and {bulk_name} calculations do not "
f"match, which is likely to cause severe errors in the parsed results. Found the "
f"following symbol in the {bulk_name} calculation:"
f"\n{symbol['titel']}\n"
f"but not in the {defect_name} calculation:"
f"\n{[symbol['titel'] for symbol in defect_potcar_symbols]}\n"
f"The same POTCAR settings should be used for all calculations for accurate results!"
)
if not only_matching_elements:
return [defect_potcar_symbols, bulk_potcar_symbols]
bulk_mismatch_list.append(symbol)
defect_mismatch_list.append(
next(
def_symbol
for def_symbol in defect_potcar_symbols
if def_symbol["titel"].split()[1].split("_")[0]
== symbol["titel"].split()[1].split("_")[0]
)
)
if bulk_mismatch_list:
return [defect_mismatch_list, bulk_mismatch_list]
return True
def _compare_kpoints(
bulk_actual_kpoints,
defect_actual_kpoints,
bulk_kpoints=None,
defect_kpoints=None,
bulk_name="bulk",
defect_name="defect",
warn=True,
):
"""
Check bulk and defect KPOINTS are the same, using the
``Vasprun.actual_kpoints`` lists (i.e. the VASP IBZKPTs essentially).
Returns ``True`` if the KPOINTS match, otherwise returns a list of the
KPOINTS for the bulk and defect calculations.
"""
# sort kpoints, in case same KPOINTS just different ordering:
sorted_bulk_kpoints = sorted(np.array(bulk_actual_kpoints), key=tuple)
sorted_defect_kpoints = sorted(np.array(defect_actual_kpoints), key=tuple)
actual_kpoints_eq = len(sorted_bulk_kpoints) == len(sorted_defect_kpoints) and np.allclose(
sorted_bulk_kpoints, sorted_defect_kpoints
)
# if different symmetry settings used (e.g. for bulk), actual_kpoints can differ but are the same
# input kpoints, which we assume is fine:
kpoints_eq = (
(
bulk_kpoints.kpts == defect_kpoints.kpts
and np.allclose(bulk_kpoints.kpts_shift, defect_kpoints.kpts_shift)
)
if bulk_kpoints and defect_kpoints
else False
)
if not (actual_kpoints_eq or kpoints_eq):
if warn:
formatted_defect_kpts = [[float(kpt) for kpt in kpoints] for kpoints in sorted_defect_kpoints]
formatted_bulk_kpts = [[float(kpt) for kpt in kpoints] for kpoints in sorted_bulk_kpoints]
warnings.warn( # list form is more readable
f"The KPOINTS for your {defect_name} and {bulk_name} calculations do not match, which is "
f"likely to cause errors in the parsed results. Found the following KPOINTS in the "
f"{defect_name} calculation:"
f"\n{formatted_defect_kpts}\n"
f"and in the {bulk_name} calculation:"
f"\n{formatted_bulk_kpts}\n"
f"In general, the same KPOINTS settings should be used for all final calculations for "
f"accurate results!"
)
return [
[list(kpoints) for kpoints in sorted_defect_kpoints],
[list(kpoints) for kpoints in sorted_bulk_kpoints],
]
return True
def _compare_incar_tags(
bulk_incar_dict: dict[str, str | int | float],
defect_incar_dict: dict[str, str | int | float],
fatal_incar_mismatch_tags: dict[str, str | int | float] | None = None,
ignore_tags: set[str] | None = None,
bulk_name: str = "bulk",
defect_name: str = "defect",
warn: bool = True,
):
"""
Check bulk and defect INCAR tags (that can affect energies) are the same.
Returns True if no mismatching tags are found, otherwise returns a list of
the mismatching tags.
"""
if fatal_incar_mismatch_tags is None:
fatal_incar_mismatch_tags = { # dict of tags that can affect energies and their defaults in VASP
"AEXX": 0.25, # default 0.25
"ENCUT": 0,
"LREAL": False, # default False
"HFSCREEN": 0, # default 0 (None)
"GGA": "PE", # default PE
"LHFCALC": False, # default False
"ADDGRID": False, # default False
"ISIF": 2,
"LASPH": False, # default False
"PREC": "Normal", # default Normal
"PRECFOCK": "Normal", # default Normal
"LDAU": False, # default False
"NKRED": 1, # default 1
"LSORBIT": False, # default False
}
if ignore_tags is not None:
fatal_incar_mismatch_tags = {
key: val for key, val in fatal_incar_mismatch_tags.items() if key not in ignore_tags
}
def _compare_incar_vals(val1, val2):
if isinstance(val1, str):
return val1.split()[0].lower() == str(val2).split()[0].lower()
if isinstance(val1, int | float) and isinstance(val2, int | float):
return np.isclose(val1, val2, rtol=1e-3)
return val1 == val2
mismatch_list = []
for key, val in bulk_incar_dict.items():
if key in fatal_incar_mismatch_tags:
defect_val = defect_incar_dict.get(key, fatal_incar_mismatch_tags[key])
if not _compare_incar_vals(val, defect_val):
mismatch_list.append((key, defect_val, val))
# get any missing keys:
defect_incar_keys_not_in_bulk = set(defect_incar_dict.keys()) - set(bulk_incar_dict.keys())
for key in defect_incar_keys_not_in_bulk:
if key in fatal_incar_mismatch_tags and not _compare_incar_vals(
defect_incar_dict[key], fatal_incar_mismatch_tags[key]
):
mismatch_list.append((key, defect_incar_dict[key], fatal_incar_mismatch_tags[key]))
if mismatch_list:
if warn:
warnings.warn(
f"There are mismatching INCAR tags for your {defect_name} and {bulk_name} calculations "
f"which are likely to cause errors in the parsed results (energies). Found the following "
f"differences:\n"
f"(in the format: (INCAR tag, value in {defect_name} calculation, value in {bulk_name} "
f"calculation)):"
f"\n{mismatch_list}\n"
f"In general, the same INCAR settings should be used in all final calculations for these "
f"tags which can affect energies!"
)
return mismatch_list
return True
def _format_mismatching_incar_warning(mismatching_INCAR_warnings: list[tuple[str, set]]) -> str:
"""
Convenience function to generate a formatted warning string listing
mismatching INCAR tags and their values in a clean output.
Used in ``doped.analysis`` and ``doped.chemical_potentials`` when checking
calculation compatibilities.
Args:
mismatching_INCAR_warnings (list[tuple[str, set]]):
A list of tuples containing the INCAR tag and the set of
mismatching values for that tag.
Returns:
str:
A formatted string listing the mismatching INCAR tags and their
values.
"""
# group by the mismatching tags, so we can print them together:
mismatching_tags_name_list_dict = {
tuple(sorted(mismatching_set)): sorted(
[
name
for name, other_mismatching_set in mismatching_INCAR_warnings
if other_mismatching_set == mismatching_set
]
) # sort for consistency
for mismatching_set in [mismatching for name, mismatching in mismatching_INCAR_warnings]
}
return "\n".join(
[
f"{entry_list}:\n{list(mismatching)}"
for mismatching, entry_list in mismatching_tags_name_list_dict.items()
]
)
[docs]
def get_magnetization_from_vasprun(vasprun: Vasprun) -> int | float | np.ndarray[float]:
"""
Determine the total magnetization from a ``Vasprun`` object.
For spin-polarised calculations, this is the difference between the number
of spin-up vs spin-down electrons. For non-spin-polarised calculations,
there is no magnetization. For non-collinear (NCL) magnetization (e.g.
spin-orbit coupling (SOC) calculations), the magnetization becomes a vector
(spinor), in which case we take the vector norm as the total magnetization.
VASP does not write the total magnetization to ``vasprun.xml`` file (but
does to the ``OUTCAR`` file), and so here we have to reverse-engineer it
from the eigenvalues (for normal spin-polarised calculations) or the
projected magnetization & eigenvalues (for NCL calculations). For NCL
calculations, we sum the projected orbital magnetizations for all occupied
states, weighted by the `k`-point weights and normalised by the total
orbital projections for each band and `k`-point. This gives the best
estimate of the total magnetization from the projected magnetization array,
but due to incomplete orbital projections and orbital-dependent non-uniform
scaling factors (i.e. completeness of orbital projects for `s` vs `p` vs
`d` orbitals etc.), there can be inaccuracies up to ~30% in the estimated
total magnetization for tricky cases.
Args:
vasprun (Vasprun):
The ``Vasprun`` object from which to extract the total
magnetization.
Returns:
int or float or np.ndarray[float]:
The total magnetization of the system.
"""
# in theory should be able to use vasprun.idos (integrated dos), but this doesn't show
# spin-polarisation / account for NELECT changes from neutral apparently
eigenvalues_and_occs = vasprun.eigenvalues
kweights = np.array(vasprun.actual_kpoints_weights)
# first check if it's a spin-polarised calculation:
if len(eigenvalues_and_occs) == 1 or not vasprun.is_spin:
# non-spin-polarised or NCL calculation:
if not vasprun.parameters.get("LNONCOLLINEAR", False):
return 0 # non-spin polarised calculation
if getattr(vasprun, "projected_magnetization", None) is None:
raise RuntimeError(
"Cannot determine magnetization from non-collinear Vasprun calculation, as this requires "
"the `Vasprun.projected_magnetization` attribute, which is parsed with "
"`Vasprun(parse_projected_eigen=True)` (default in `doped`)."
)
# else NCL calculation:
# need to scale by the summed orbital projections for each band (which should be 1):
# vasprun.projected_eigenvalues[Spin.up].shape -> (nkpoints, nbands, natoms, norbitals)
summed_orbital_projections = vasprun.projected_eigenvalues[Spin.up].sum(axis=(-2, -1))
summed_orbital_projections = np.where(
summed_orbital_projections == 0, 1, summed_orbital_projections
) # avoid division by zero, by setting any zero values to 1
normalisation_factors = 1 / summed_orbital_projections
# vasprun.projected_magnetization.shape -> (nkpoints, nbands, natoms, norbitals, 3 -- x/y/z)
# sum the projected magnetization over atoms and orbitals, then multiply by per-band/kpoint
# normalisation factors:
normalised_proj_mag_per_kpoint_band_direction = (
vasprun.projected_magnetization.sum(axis=(-3, -2)) * normalisation_factors[..., None]
) # [..., None] adds new axis, which allows broadcasting (i.e.
# (nkpoints, nbands, 3) * (nkpoints, nbands, 1) -- adding the "(...,1 )" dimension)
# then multiply by occupancies, sum over bands, multiply by k-point weights, sum over k-points:
return (
(
normalised_proj_mag_per_kpoint_band_direction
* eigenvalues_and_occs[Spin.up][:, :, 1][..., None]
).sum(axis=1)
* kweights[..., None]
).sum(axis=0)
# product of the sum of occupations over all bands, times the k-point weights:
n_spin_up = np.sum(eigenvalues_and_occs[Spin.up][:, :, 1].sum(axis=1) * kweights)
n_spin_down = np.sum(eigenvalues_and_occs[Spin.down][:, :, 1].sum(axis=1) * kweights)
return n_spin_up - n_spin_down
[docs]
def get_nelect_from_vasprun(vasprun: Vasprun) -> int | float:
"""
Determine the number of electrons (``NELECT``) from a ``Vasprun`` object.
Args:
vasprun (Vasprun):
The ``Vasprun`` object from which to extract ``NELECT``.
Returns:
int or float: The number of electrons in the system.
"""
# can also obtain this (NELECT), charge and magnetization from Outcar objects, worth keeping in mind
# but not needed atm
# in theory should be able to use vasprun.idos (integrated dos), but this doesn't show
# spin-polarisation / account for NELECT changes from neutral apparently
eigenvalues_and_occs = vasprun.eigenvalues
kweights = vasprun.actual_kpoints_weights
# product of the sum of occupations over all bands, times the k-point weights:
nelect = np.sum(eigenvalues_and_occs[Spin.up][:, :, 1].sum(axis=1) * kweights)
if len(eigenvalues_and_occs) > 1:
nelect += np.sum(eigenvalues_and_occs[Spin.down][:, :, 1].sum(axis=1) * kweights)
elif not vasprun.parameters.get("LNONCOLLINEAR", False):
nelect *= 2 # non-spin-polarised or SOC calc
return round(nelect, 2)
[docs]
def get_neutral_nelect_from_vasprun(vasprun: Vasprun, skip_potcar_init: bool = False) -> int:
"""
Determine the number of electrons (``NELECT``) from a ``Vasprun`` object,
corresponding to a neutral charge state for the structure.
Args:
vasprun (Vasprun):
The ``Vasprun`` object from which to extract ``NELECT``.
skip_potcar_init (bool):
Whether to skip the initialisation of the ``POTCAR`` statistics
(i.e. the auto-charge determination) and instead try to reverse
engineer ``NELECT`` using the ``DefectDictSet``.
Returns:
int:
The number of electrons in the system for a neutral charge state.
"""
nelect = None
if not skip_potcar_init:
with contextlib.suppress(Exception): # try determine charge without POTCARs first:
grouped_symbols = [list(group) for key, group in itertools.groupby(vasprun.atomic_symbols)]
potcar_summary_stats = _get_potcar_summary_stats()
for trial_functional in ["PBE_64", "PBE_54", "PBE_52", "PBE", potcar_summary_stats.keys()]:
if all(
potcar_summary_stats[trial_functional].get(
vasprun.potcar_spec[i]["titel"].replace(" ", ""), False
)
for i in range(len(grouped_symbols))
):
break
nelect = sum( # this is always the NELECT for the bulk
np.array([len(i) for i in grouped_symbols])
* np.array(
[
potcar_summary_stats[trial_functional][
vasprun.potcar_spec[i]["titel"].replace(" ", "")
][0]["ZVAL"]
for i in range(len(grouped_symbols))
]
)
)
if nelect is not None:
return int(nelect)
# else try reverse engineer NELECT using DefectDictSet
from doped.vasp import DefectDictSet
potcar_symbols = [titel.split()[1] for titel in vasprun.potcar_symbols]
potcar_settings = {symbol.split("_")[0]: symbol for symbol in potcar_symbols}
with warnings.catch_warnings(): # ignore POTCAR warnings if not available
warnings.simplefilter("ignore", UserWarning)
return int(
DefectDictSet(
vasprun.structures[-1],
charge_state=0,
user_potcar_settings=potcar_settings,
).nelect
)
def _get_bulk_supercell(defect_entry: DefectEntry):
if hasattr(defect_entry, "bulk_supercell") and defect_entry.bulk_supercell:
return defect_entry.bulk_supercell
if (
hasattr(defect_entry, "bulk_entry")
and defect_entry.bulk_entry
and hasattr(defect_entry.bulk_entry, "structure")
and defect_entry.bulk_entry.structure
):
return defect_entry.bulk_entry.structure
return None
def _get_defect_supercell(defect_entry: DefectEntry):
if hasattr(defect_entry, "defect_supercell") and defect_entry.defect_supercell:
return defect_entry.defect_supercell
if (
hasattr(defect_entry, "sc_entry")
and defect_entry.sc_entry
and hasattr(defect_entry.sc_entry, "structure")
and defect_entry.sc_entry.structure
):
return defect_entry.sc_entry.structure
return None
def _get_unrelaxed_defect_structure(defect_entry: DefectEntry, **kwargs) -> Structure | None:
if (
hasattr(defect_entry, "calculation_metadata")
and defect_entry.calculation_metadata
and "unrelaxed_defect_structure" in defect_entry.calculation_metadata
):
return defect_entry.calculation_metadata["unrelaxed_defect_structure"]
bulk_supercell = _get_bulk_supercell(defect_entry)
if bulk_supercell is not None: # reparse info:
_update_defect_entry_structure_metadata(defect_entry, **kwargs)
return defect_entry.calculation_metadata.get("unrelaxed_defect_structure")
def _get_defect_supercell_frac_coords(defect_entry: DefectEntry, relaxed=True) -> np.ndarray[float] | None:
sc_defect_frac_coords = defect_entry.sc_defect_frac_coords
site = None
if not relaxed:
site = _get_defect_supercell_site(defect_entry, relaxed=False)
if sc_defect_frac_coords is None and site is None:
site = _get_defect_supercell_site(defect_entry)
if site is not None:
sc_defect_frac_coords = site.frac_coords
return sc_defect_frac_coords
def _get_defect_supercell_site(defect_entry: DefectEntry, relaxed=True, **kwargs) -> PeriodicSite | None:
def _return_defect_supercell_site(defect_entry: DefectEntry, relaxed=True):
if relaxed or defect_entry.defect.defect_type == DefectType.Interstitial:
# always final relaxed site for interstitials (note that "bulk_site" may be guessed initial
# site if it is close enough to the final relaxed site):
if site := getattr(defect_entry, "defect_supercell_site", None):
return site
if defect_entry.sc_defect_frac_coords is not None:
return PeriodicSite(
defect_entry.defect.site.species,
defect_entry.sc_defect_frac_coords,
_get_defect_supercell(defect_entry).lattice,
)
# otherwise we use ``bulk_site``, for relaxed = False (vacancies & substitutions)
if (
hasattr(defect_entry, "calculation_metadata")
and defect_entry.calculation_metadata
and defect_entry.calculation_metadata.get("bulk_site")
):
return defect_entry.calculation_metadata.get("bulk_site")
return None
if defect_supercell_site := _return_defect_supercell_site(defect_entry, relaxed=relaxed):
return defect_supercell_site
# otherwise need to reparse info:
_update_defect_entry_structure_metadata(defect_entry, **kwargs)
return _return_defect_supercell_site(defect_entry, relaxed=relaxed)
def _update_defect_entry_structure_metadata(defect_entry: DefectEntry, overwrite: bool = False, **kwargs):
"""
Helper function to reparse the defect site information for a given
``DefectEntry``, updating the relevant attributes and calculation metadata.
Args:
defect_entry (DefectEntry):
The ``DefectEntry`` object for which to update the defect site
information.
overwrite (bool):
Whether to overwrite existing ``DefectEntry`` attributes with the
newly parsed values. Default is ``False`` (i.e. only update if the
attributes are not already set).
**kwargs:
Keyword arguments to pass to ``get_equiv_frac_coords_in_primitive``
(such as ``symprec``, ``dist_tol_factor``,
``fixed_symprec_and_dist_tol_factor``, ``verbose``) and/or
``Defect`` initialization (such as ``oxi_state``, ``multiplicity``,
``symprec``, ``dist_tol_factor``) in the
``defect_and_info_from_structures`` function.
"""
from doped.analysis import defect_and_info_from_structures
bulk_supercell = _get_bulk_supercell(defect_entry)
defect_supercell = _get_defect_supercell(defect_entry)
(
defect,
defect_site,
defect_structure_metadata,
) = defect_and_info_from_structures(
bulk_supercell,
defect_supercell,
**kwargs, # pass any additional kwargs (e.g. oxidation state, multiplicity, etc.)
)
if not getattr(defect_entry, "calculation_metadata", None):
defect_entry.calculation_metadata = {}
# update any missing calculation_metadata:
for k, v in defect_structure_metadata.items():
if not defect_entry.calculation_metadata.get(k) or overwrite:
defect_entry.calculation_metadata[k] = v
for attr_name, value in {
"defect": defect,
"sc_defect_frac_coords": defect_site.frac_coords, # _relaxed_ defect site
"defect_supercell_site": defect_site,
"defect_supercell": defect_supercell,
"bulk_supercell": bulk_supercell,
}.items():
if getattr(defect_entry, attr_name, None) is None or overwrite:
setattr(defect_entry, attr_name, value)
def _partial_defect_entry_from_structures(
bulk_supercell: Structure, defect_supercell: Structure, **kwargs
) -> DefectEntry:
"""
Helper function to create a partial ``DefectEntry`` from the input bulk and
defect supercells.
Uses ``defect_and_info_from_structures`` to extract the defect structural
information, and creates a corresponding ``DefectEntry`` object (which has
no ``bulk_entry`` and a fake zero-energy ``sc_entry``, and so cannot be
used for energy analyses). Primarily intended for internal usage in
``doped`` parsing/analysis functions.
Args:
bulk_supercell (Structure):
The bulk supercell structure.
defect_supercell (Structure):
The defect supercell structure.
**kwargs:
Keyword arguments to pass to ``get_equiv_frac_coords_in_primitive``
(such as ``symprec``, ``dist_tol_factor``,
``fixed_symprec_and_dist_tol_factor``, ``verbose``) and/or
``Defect`` initialization (such as ``oxi_state``, ``multiplicity``,
``symprec``, ``dist_tol_factor``) in the
``defect_and_info_from_structures`` function.
Returns:
DefectEntry:
A partial ``DefectEntry`` object containing the defect and defect
site information, but no ``bulk_entry`` and a zero-energy
``sc_entry``.
"""
from doped.analysis import defect_and_info_from_structures
(
defect,
defect_site,
defect_structure_metadata,
) = defect_and_info_from_structures(
bulk_supercell,
defect_supercell,
**kwargs, # pass any additional kwargs (e.g. oxidation state, multiplicity, etc.)
)
return DefectEntry(
# pmg attributes:
defect=defect, # this corresponds to _unrelaxed_ defect
charge_state=0,
sc_entry=ComputedStructureEntry(
structure=bulk_supercell,
energy=0.0, # needs to be set, so set to 0.0
),
sc_defect_frac_coords=defect_site.frac_coords, # _relaxed_ defect site
bulk_entry=None,
# doped attributes:
name="Partial Defect Entry",
defect_supercell_site=defect_site, # _relaxed_ defect site
defect_supercell=defect_supercell,
bulk_supercell=bulk_supercell,
calculation_metadata=defect_structure_metadata, # only structural metadata here
)
def _num_electrons_from_charge_state(structure: Structure, charge_state: int = 0) -> int:
"""
Get the total number of electrons (including core electrons! -- so
different to ``NELECT`` in VASP in most cases) for a given structure and
charge state.
Args:
structure (Structure):
The structure for which to get the total number of electrons.
charge_state (int):
The charge state of the system. Default is 0.
Returns:
int:
The total number of electrons in the system, including core
electrons.
"""
total_Z = int(
sum(Element(elt).Z * num for elt, num in structure.composition.get_el_amt_dict().items())
)
return int(total_Z + charge_state)
[docs]
def spin_degeneracy_from_vasprun(vasprun: Vasprun, charge_state: int | None = None) -> int:
"""
Get the spin degeneracy (multiplicity) of a system from a ``VASP`` vasprun
output.
Spin degeneracy is determined by first getting the total magnetization and
thus electron spin (S = N_μB/2 -- where N_μB is the magnetization in Bohr
magnetons (i.e. electronic units, as used in VASP), and using the spin
multiplicity equation: ``g_spin = 2S + 1``. The total magnetization
``N_μB`` is determined using ``get_magnetization_from_vasprun`` (see
docstring for details), and if this fails, then simple spin behaviour is
assumed with singlet (S = 0) behaviour for even-electron systems and
doublet behaviour (S = 1/2) for odd-electron systems.
For non-collinear (NCL) magnetization (e.g. spin-orbit coupling (SOC)
calculations), the magnetization ``N_μB`` becomes a vector (spinor), in
which case we take the vector norm as the total magnetization. This can be
non-integer in these cases (e.g. due to SOC mixing of spin states, as
**_S_** is no longer a good quantum number). As an approximation for these
cases, we round ``N_μB`` to the nearest integer which would be allowed
under collinear magnetism (i.e. even numbers for even-electron systems, odd
numbers for odd-electron systems).
Args:
vasprun (Vasprun):
``pymatgen`` ``Vasprun`` for which to determine spin degeneracy.
charge_state (int):
The charge state of the system, which can be used to determine the
number of electrons. If ``None`` (default), automatically
determines the number of electrons using
``get_nelect_from_vasprun(vasprun)``.
Returns:
int: Spin degeneracy of the system.
"""
if charge_state is None:
num_electrons = get_nelect_from_vasprun(vasprun)
else:
num_electrons = _num_electrons_from_charge_state(vasprun.final_structure, charge_state)
try:
magnetization = get_magnetization_from_vasprun(vasprun)
if isinstance(magnetization, np.ndarray):
# take the vector norm as the total magnetization
magnetization = np.linalg.norm(magnetization)
# round to nearest possible value (even numbers for even-electron systems, odd for odd-electron):
if num_electrons % 2 == 0: # even-electron system, spin degeneracy = 1, 3, 5, ...
magnetization = round(magnetization / 2) * 2 # nearest even number
else:
magnetization = round((magnetization - 1) / 2) * 2 + 1 # nearest odd number
# spin multiplicity = 2S + 1 = 2(mag/2) + 1 = mag + 1 (where mag is in Bohr magnetons
# i.e. number of electrons, as in VASP):
return abs(magnetization) + 1
except (RuntimeError, TypeError): # NCL calculation without parsed projected magnetization:
return _simple_spin_degeneracy_from_num_electrons(int(num_electrons)) # guess from charge
def _simple_spin_degeneracy_from_num_electrons(num_electrons: int = 0) -> int:
"""
Get the spin degeneracy of a system from the total number of electrons,
assuming simple singlet (S=0) behaviour for even-electron systems or
doublet (S=1/2) behaviour for odd-electron systems.
Spin multiplicity is equal to ``2S + 1``, so 1 for singlets (S = 0), 2 for
doublets (S = 1/2), 3 for triplets (S = 1) etc.
Args:
num_electrons (int): The total number of electrons.
Returns:
int:
The spin multiplicity assuming singlet or doublet behaviour.
"""
return int(num_electrons % 2 + 1)
[docs]
def total_charge_from_vasprun(vasprun: Vasprun) -> int | None:
"""
Determine the total charge state of a system from the vasprun, and compare
to the expected charge state if provided.
Note that if the system is charged, then this function relies on access to
``POTCAR`` data, which can be setup with ``pymatgen`` as detailed on the
installation page here:
https://doped.readthedocs.io/en/latest/Installation.html#setup-potcars-and-materials-project-api
Args:
vasprun (Vasprun):
``pymatgen`` ``Vasprun`` object for which to determine the total
charge.
Returns:
int or None:
The total charge state, or ``None`` if it cannot be determined.
"""
if (nelect := vasprun.incar.get("NELECT")) is None:
return 0 # neutral if NELECT not specified
auto_charge = None
with contextlib.suppress(Exception): # otherwise determine neutral NELECT from vasprun & POTCARs:
neutral_nelect = get_neutral_nelect_from_vasprun(vasprun)
auto_charge = -1 * (nelect - neutral_nelect)
if abs(auto_charge) >= 10:
neutral_nelect = get_neutral_nelect_from_vasprun(vasprun, skip_potcar_init=True)
auto_charge = -1 * (nelect - neutral_nelect)
return auto_charge
def _get_bulk_locpot_dict(bulk_path, quiet=False):
bulk_locpot_path, multiple = _get_output_files_and_check_if_multiple("LOCPOT", bulk_path)
if multiple and not quiet:
_multiple_files_warning(
"LOCPOT",
bulk_path,
bulk_locpot_path,
dir_type="bulk",
)
bulk_locpot = get_locpot(bulk_locpot_path)
return {str(k): bulk_locpot.get_average_along_axis(k) for k in [0, 1, 2]}
def _get_bulk_site_potentials(
bulk_path: PathLike, quiet: bool = False, total_energy: list | float | None = None
):
bulk_outcar_path, multiple = _get_output_files_and_check_if_multiple("OUTCAR", bulk_path)
if multiple and not quiet:
_multiple_files_warning(
"OUTCAR",
bulk_path,
bulk_outcar_path,
dir_type="bulk",
)
return get_core_potentials_from_outcar(bulk_outcar_path, dir_type="bulk", total_energy=total_energy)
def _update_defect_entry_charge_corrections(defect_entry, charge_correction_type):
meta = defect_entry.calculation_metadata[f"{charge_correction_type}_meta"]
corr = (
meta[f"{charge_correction_type}_electrostatic"]
+ meta[f"{charge_correction_type}_potential_alignment_correction"]
)
defect_entry.corrections.update({f"{charge_correction_type}_charge_correction": corr})
_vasp_file_parsing_action_dict = {
"vasprun.xml": "parse the calculation energy and metadata.",
"OUTCAR": "parse core levels and compute the Kumagai (eFNV) image charge correction.",
"LOCPOT": "parse the electrostatic potential and compute the Freysoldt (FNV) charge correction.",
}
def _multiple_files_warning(file_type, directory, chosen_filepath, action=None, dir_type="bulk"):
filename = os.path.basename(chosen_filepath)
if action is None:
action = _vasp_file_parsing_action_dict[file_type]
warnings.warn(
f"Multiple `{file_type}` files found in {dir_type} directory: {directory}. Using {filename} to "
f"{action}"
)
[docs]
def get_dimer_bonds(structure: Structure, rtol: float = 1.05) -> dict[str, list[float]]:
"""
Get a dictionary of all homoionic (dimer) bonds in the structure.
This function uses the ``get_homoionic_bonds`` and
``get_dimer_bond_length`` functions from ``shakenbreak`` to identify dimer
bonds in the structure (where any pair of atoms of the same element with
distance < ``rtol * get_dimer_bond_length(elt, elt)`` are considered a
dimer bond), returning a dictionary of the site names and the dimer bond
length.
Args:
structure (Structure): The structure to get the dimer bond lengths for.
rtol (float):
The relative tolerance to use for classifying bonds as dimer bonds,
where distances < ``rtol * get_dimer_bond_length(elt, elt)`` are
considered dimer bonds. Default is 1.05.
Returns:
dict[str, list[float]]:
A dictionary of element names with values being sub-dictionaries of
site names and their homoionic neighbours and distances (in Å)
which are classified as dimer bonds.
(e.g. {'O': {'O(1)': {'O(3)': '1.44 Å'}}})
"""
from shakenbreak.analysis import get_homoionic_bonds
from shakenbreak.distortions import get_dimer_bond_length
dimer_bond_dict = {
str(elt): get_homoionic_bonds(
structure=structure,
elements=str(elt),
radius=rtol * get_dimer_bond_length(elt, elt),
verbose=False,
)
for elt in structure.composition.elements
}
return {k: v for k, v in dimer_bond_dict.items() if v}