diff --git a/.gitignore b/.gitignore index a9944e13f2e..36b743b627a 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,4 @@ +__pycache__/ .DS_Store pymatgen.egg-info dependencies/PyCifRW-3.3/PyCifRW.egg-info diff --git a/pymatgen/analysis/magnetism/analyzer.py b/pymatgen/analysis/magnetism/analyzer.py index 4e60657e88e..8f8cc1f915c 100644 --- a/pymatgen/analysis/magnetism/analyzer.py +++ b/pymatgen/analysis/magnetism/analyzer.py @@ -20,7 +20,10 @@ from pymatgen.transformations.standard_transformations import ( AutoOxiStateDecorationTransformation, ) -from pymatgen.transformations.advanced_transformations import MagOrderingTransformation, MagOrderParameterConstraint +from pymatgen.transformations.advanced_transformations import ( + MagOrderingTransformation, + MagOrderParameterConstraint, +) from pymatgen.alchemy.materials import TransformedStructure from pymatgen.symmetry.groups import SpaceGroup from pymatgen.analysis.bond_valence import BVAnalyzer @@ -83,7 +86,8 @@ def __init__( make_primitive: bool = True, default_magmoms: bool = None, set_net_positive: bool = True, - threshold: float = 0.1, + threshold: float = 0.00, + threshold_nonmag: float = 0.1, ): """ A class which provides a few helpful methods to analyze @@ -128,8 +132,10 @@ def __init__( moments such that the net magnetization is positive. Argument will be ignored if mode "respect_sign" is used. :param threshold (float): number (in Bohr magnetons) below which magmoms - will be rounded to zero, default of 0.1 can probably be increased for many - magnetic systems, depending on your application + will be rounded to zero, + :param threshold_nonmag (float): number (in Bohr magneton) + below which nonmagnetic ions (with no magmom specified + in default_magmoms) will be rounded to zero """ if default_magmoms: @@ -189,7 +195,9 @@ def __init__( "site properties. Any 'None' magmoms have been " "replaced with zero." ) - magmoms = [m if m else 0 for m in structure.site_properties["magmom"]] + magmoms = [ + m if m else 0 for m in structure.site_properties["magmom"] + ] elif has_spin: magmoms = [getattr(sp, "spin", 0) for sp in structure.species] structure.remove_spin() @@ -219,8 +227,17 @@ def __init__( self.total_magmoms = sum(magmoms) self.magnetization = sum(magmoms) / structure.volume - # round magmoms below threshold to zero - magmoms = [m if abs(m) > threshold else 0 for m in magmoms] + # round magmoms on magnetic ions below threshold to zero + # and on non magnetic ions below threshold_nonmag + magmoms = [ + m + if abs(m) > threshold and a.species_string in self.default_magmoms + else m + if abs(m) > threshold_nonmag + and a.species_string not in self.default_magmoms + else 0 + for (m, a) in zip(magmoms, structure.sites) + ] # overwrite existing magmoms with default_magmoms if overwrite_magmom_mode not in ( @@ -280,7 +297,9 @@ def __init__( # round magmoms, used to smooth out computational data magmoms = ( - self._round_magmoms(magmoms, round_magmoms) if round_magmoms else magmoms + self._round_magmoms(magmoms, round_magmoms) + if round_magmoms + else magmoms ) if set_net_positive: @@ -328,7 +347,9 @@ def _round_magmoms(magmoms, round_magmoms_mode: Union[int, float]): extrema = xgrid[argrelextrema(kernel_m, comparator=np.greater)] # round magmoms to these extrema - magmoms = [extrema[(np.abs(extrema - m)).argmin()] for m in magmoms] + magmoms = [ + extrema[(np.abs(extrema - m)).argmin()] for m in magmoms + ] except Exception as e: @@ -364,7 +385,11 @@ def get_structure_with_only_magnetic_atoms(self, make_primitive=True): :return: Structure """ - sites = [site for site in self.structure if abs(site.properties["magmom"]) > 0] + sites = [ + site + for site in self.structure + if abs(site.properties["magmom"]) > 0 + ] structure = Structure.from_sites(sites) @@ -524,7 +549,9 @@ def ordering(self): total_magnetization = abs(sum(magmoms)) - is_potentially_ferromagnetic = np.all(magmoms >= 0) or np.all(magmoms <= 0) + is_potentially_ferromagnetic = np.all(magmoms >= 0) or np.all( + magmoms <= 0 + ) if total_magnetization > 0 and is_potentially_ferromagnetic: return Ordering.FM @@ -611,7 +638,9 @@ def __str__(self): sorted_indices = np.lexsort( (frac_coords[:, 2], frac_coords[:, 1], frac_coords[:, 0]) ) - s = Structure.from_sites([self.structure[idx] for idx in sorted_indices]) + s = Structure.from_sites( + [self.structure[idx] for idx in sorted_indices] + ) # adapted from Structure.__repr__ outs = ["Structure Summary", repr(s.lattice)] @@ -632,15 +661,24 @@ class MagneticStructureEnumerator: and produce a list of plausible magnetic orderings. """ - available_strategies = ("ferromagnetic", "antiferromagnetic", - "ferrimagnetic_by_motif", "ferrimagnetic_by_species", - "antiferromagnetic_by_motif", "nonmagnetic") + available_strategies = ( + "ferromagnetic", + "antiferromagnetic", + "ferrimagnetic_by_motif", + "ferrimagnetic_by_species", + "antiferromagnetic_by_motif", + "nonmagnetic", + ) - def __init__(self, structure, default_magmoms=None, - strategies=("ferromagnetic", "antiferromagnetic"), - automatic=True, - truncate_by_symmetry=True, - transformation_kwargs=None): + def __init__( + self, + structure, + default_magmoms=None, + strategies=("ferromagnetic", "antiferromagnetic"), + automatic=True, + truncate_by_symmetry=True, + transformation_kwargs=None, + ): """ This class will try generated different collinear magnetic orderings for a given input structure. @@ -688,7 +726,10 @@ def __init__(self, structure, default_magmoms=None, self.max_unique_sites = 8 # kwargs to pass to transformation (ultimately to enumlib) - default_transformation_kwargs = {"check_ordered_symmetry": False, "timeout": 5} + default_transformation_kwargs = { + "check_ordered_symmetry": False, + "timeout": 5, + } transformation_kwargs = transformation_kwargs or {} transformation_kwargs.update(default_transformation_kwargs) self.transformation_kwargs = transformation_kwargs @@ -717,7 +758,9 @@ def __init__(self, structure, default_magmoms=None, # this workflow is not appropriate), and has many convenience # methods e.g. magnetic structure matching, etc. self.input_analyzer = CollinearMagneticStructureAnalyzer( - structure, default_magmoms=default_magmoms, overwrite_magmom_mode="none" + structure, + default_magmoms=default_magmoms, + overwrite_magmom_mode="none", ) # this workflow enumerates structures with different combinations @@ -725,13 +768,17 @@ def __init__(self, structure, default_magmoms=None, # if your input structure has vector magnetic moments, this # workflow is not appropriate if not self.input_analyzer.is_collinear: - raise ValueError("Input structure ({}) is non-collinear.".format(formula)) + raise ValueError( + "Input structure ({}) is non-collinear.".format(formula) + ) self.sanitized_structure = self._sanitize_input_structure(structure) # we will first create a set of transformations # and then apply them to our input structure - self.transformations = self._generate_transformations(self.sanitized_structure) + self.transformations = self._generate_transformations( + self.sanitized_structure + ) self._generate_ordered_structures( self.sanitized_structure, self.transformations ) @@ -754,7 +801,9 @@ def _sanitize_input_structure(input_structure): input_structure.remove_spin() # sanitize input structure: first make primitive ... - input_structure = input_structure.get_primitive_structure(use_site_props=False) + input_structure = input_structure.get_primitive_structure( + use_site_props=False + ) # ... and strip out existing magmoms, which can cause conflicts # with later transformations otherwise since sites would end up @@ -800,7 +849,9 @@ def _generate_transformations(self, structure): ) # now we can begin to generate our magnetic orderings - self.logger.info("Generating magnetic orderings for {}".format(formula)) + self.logger.info( + "Generating magnetic orderings for {}".format(formula) + ) mag_species_spin = analyzer.magnetic_species_and_magmoms types_mag_species = sorted( @@ -815,7 +866,9 @@ def _generate_transformations(self, structure): # permutations) increase, 8 is a soft limit, this can be increased # but do so with care if num_unique_sites > self.max_unique_sites: - raise ValueError("Too many magnetic sites to sensibly perform enumeration.") + raise ValueError( + "Too many magnetic sites to sensibly perform enumeration." + ) # maximum cell size to consider: as a rule of thumb, if the primitive cell # contains a large number of magnetic sites, perhaps we only need to enumerate @@ -823,7 +876,9 @@ def _generate_transformations(self, structure): # contains a single magnetic site, we have to create larger supercells if "max_cell_size" not in self.transformation_kwargs: # TODO: change to 8 / num_mag_sites ? - self.transformation_kwargs["max_cell_size"] = max(1, int(4 / num_mag_sites)) + self.transformation_kwargs["max_cell_size"] = max( + 1, int(4 / num_mag_sites) + ) self.logger.info( "Max cell size set to {}".format( self.transformation_kwargs["max_cell_size"] @@ -845,7 +900,8 @@ def _generate_transformations(self, structure): for index in indices: wyckoff[index] = symbol is_magnetic_sites = [ - True if site.specie in types_mag_species else False for site in structure + True if site.specie in types_mag_species else False + for site in structure ] # we're not interested in sites that we don't think are magnetic, # set these symbols to None to filter them out later @@ -887,7 +943,9 @@ def _generate_transformations(self, structure): fm_structure = analyzer.get_ferromagnetic_structure() # store magmom as spin property, to be consistent with output from # other transformations - fm_structure.add_spin_by_site(fm_structure.site_properties["magmom"]) + fm_structure.add_spin_by_site( + fm_structure.site_properties["magmom"] + ) fm_structure.remove_site_property("magmom") # we now have our first magnetic ordering... @@ -903,7 +961,8 @@ def _generate_transformations(self, structure): constraint = MagOrderParameterConstraint( 0.5, - # TODO: update MagOrderParameterConstraint in pymatgen to take types_mag_species directly + # TODO: update MagOrderParameterConstraint in + # pymatgen to take types_mag_species directly species_constraints=list(map(str, types_mag_species)), ) all_constraints["afm"] = [constraint] @@ -913,21 +972,28 @@ def _generate_transformations(self, structure): for sp in types_mag_species: constraints = [ - MagOrderParameterConstraint(0.5, species_constraints=str(sp)) + MagOrderParameterConstraint( + 0.5, species_constraints=str(sp) + ) ] all_constraints["afm_by_{}".format(sp)] = constraints # ...and then we also try ferrimagnetic orderings by motif if a # single magnetic species is present... - if "ferrimagnetic_by_motif" in self.strategies and len(wyckoff_symbols) > 1: + if ( + "ferrimagnetic_by_motif" in self.strategies + and len(wyckoff_symbols) > 1 + ): # these orderings are AFM on one local environment, and FM on the rest for symbol in wyckoff_symbols: constraints = [ MagOrderParameterConstraint( - 0.5, site_constraint_name="wyckoff", site_constraints=symbol + 0.5, + site_constraint_name="wyckoff", + site_constraints=symbol, ), MagOrderParameterConstraint( 1.0, @@ -936,7 +1002,9 @@ def _generate_transformations(self, structure): ), ] - all_constraints["ferri_by_motif_{}".format(symbol)] = constraints + all_constraints[ + "ferri_by_motif_{}".format(symbol) + ] = constraints # and also try ferrimagnetic when there are multiple magnetic species if "ferrimagnetic_by_species" in self.strategies: @@ -948,12 +1016,16 @@ def _generate_transformations(self, structure): for sp in types_mag_species: # attempt via a global order parameter - all_constraints["ferri_by_{}".format(sp)] = num_sp[sp] / total_mag_sites + all_constraints["ferri_by_{}".format(sp)] = ( + num_sp[sp] / total_mag_sites + ) # attempt via afm on sp, fm on remaining species constraints = [ - MagOrderParameterConstraint(0.5, species_constraints=str(sp)), + MagOrderParameterConstraint( + 0.5, species_constraints=str(sp) + ), MagOrderParameterConstraint( 1.0, species_constraints=list( @@ -973,7 +1045,9 @@ def _generate_transformations(self, structure): constraints = [ MagOrderParameterConstraint( - 0.5, site_constraint_name="wyckoff", site_constraints=symbol + 0.5, + site_constraint_name="wyckoff", + site_constraints=symbol, ) ] @@ -993,7 +1067,9 @@ def _generate_transformations(self, structure): return transformations - def _generate_ordered_structures(self, sanitized_input_structure, transformations): + def _generate_ordered_structures( + self, sanitized_input_structure, transformations + ): """ Apply our input structure to our list of transformations and output a list of ordered structures that have been pruned for duplicates and for those @@ -1016,7 +1092,10 @@ def _generate_ordered_structures(self, sanitized_input_structure, transformation # utility function to combine outputs from several transformations def _add_structures( - ordered_structures, ordered_structures_origins, structures_to_add, origin="" + ordered_structures, + ordered_structures_origins, + structures_to_add, + origin="", ): """ Transformations with return_ranked_list can return either @@ -1062,8 +1141,13 @@ def _add_structures( duplicate_checker = CollinearMagneticStructureAnalyzer( ordered_structure, overwrite_magmom_mode="none" ) - for check_idx, check_structure in enumerate(ordered_structures): - if check_idx not in structures_to_remove and check_idx != idx: + for check_idx, check_structure in enumerate( + ordered_structures + ): + if ( + check_idx not in structures_to_remove + and check_idx != idx + ): if duplicate_checker.matches_ordering(check_structure): structures_to_remove.append(check_idx) @@ -1128,7 +1212,9 @@ def _add_structures( ) ) - ordered_structures = [ordered_structures[i] for i, _ in structs_to_keep] + ordered_structures = [ + ordered_structures[i] for i, _ in structs_to_keep + ] ordered_structures_origins = [ ordered_structures_origins[i] for i, _ in structs_to_keep ] @@ -1147,7 +1233,8 @@ def _add_structures( self.input_origin = None if self.input_analyzer.ordering != Ordering.NM: matches = [ - self.input_analyzer.matches_ordering(s) for s in ordered_structures + self.input_analyzer.matches_ordering(s) + for s in ordered_structures ] if not any(matches): ordered_structures.append(self.input_analyzer.structure) @@ -1161,7 +1248,9 @@ def _add_structures( "structures at index {}".format(matches.index(True)) ) self.input_index = matches.index(True) - self.input_origin = ordered_structures_origins[self.input_index] + self.input_origin = ordered_structures_origins[ + self.input_index + ] self.ordered_structures = ordered_structures self.ordered_structure_origins = ordered_structures_origins @@ -1200,7 +1289,9 @@ def magnetic_deformation(structure_A, structure_B): p = np.dot(lattice_a_inv, lattice_b) eta = 0.5 * (np.dot(p.T, p) - np.identity(3)) w, v = np.linalg.eig(eta) - deformation = 100 * (1.0 / 3.0) * np.sqrt(w[0] ** 2 + w[1] ** 2 + w[2] ** 2) + deformation = ( + 100 * (1.0 / 3.0) * np.sqrt(w[0] ** 2 + w[1] ** 2 + w[2] ** 2) + ) MagneticDeformation = namedtuple("MagneticDeformation", "type deformation") diff --git a/pymatgen/analysis/magnetism/heisenberg.py b/pymatgen/analysis/magnetism/heisenberg.py new file mode 100644 index 00000000000..ab9edb52d67 --- /dev/null +++ b/pymatgen/analysis/magnetism/heisenberg.py @@ -0,0 +1,828 @@ +# coding: utf-8 +# Copyright (c) Pymatgen Development Team. +# Distributed under the terms of the MIT License. + +from pymatgen.analysis.magnetism import CollinearMagneticStructureAnalyzer, Ordering +from pymatgen.analysis.graphs import StructureGraph +from pymatgen.analysis.local_env import MinimumDistanceNN +from pymatgen.analysis.structure_matcher import ElementComparator, StructureMatcher +from pymatgen.symmetry.analyzer import SpacegroupAnalyzer + +from monty.serialization import dumpfn + +import numpy as np +import pandas as pd +import copy + +import logging +import sys + +""" +This module implements a simple algorithm for extracting nearest neighbor +exchange parameters by mapping low energy magnetic orderings to a Heisenberg +model. +""" + +__author__ = "ncfrey" +__version__ = "0.1" +__maintainer__ = "Nathan C. Frey" +__email__ = "ncfrey@lbl.gov" +__status__ = "Development" +__date__ = "June 2019" + + +class HeisenbergMapper: + def __init__(self, ordered_structures, energies, cutoff=0.0, tol=0.02): + """Compute exchange parameters from low energy magnetic orderings. + + Exchange parameters are computed by mapping to a classical Heisenberg + model. Strategy is the scheme for generating neighbors. Currently only + MinimumDistanceNN is implemented. + n+1 unique orderings are required to compute n exchange + parameters. + + First run a MagneticOrderingsWF to obtain low energy collinear magnetic + orderings and find the magnetic ground state. Then enumerate magnetic + states with the ground state as the input structure, find the subset + of supercells that map to the ground state, and do static calculations + for these orderings. + + Args: + ordered_structures (list): Structure objects with magmoms. + energies (list): Energies of each relaxed magnetic structure. + cutoff (float): Cutoff in Angstrom for nearest neighbor search. + Defaults to 0 (only NN, no NNN, etc.) + tol (float): Tolerance (in Angstrom) on nearest neighbor distances + being equal. + + Parameters: + strategy (object): Class from pymatgen.analysis.local_env for + constructing graphs. + sgraphs (list): StructureGraph objects. + unique_site_ids (dict): Maps each site to its unique numerical + identifier. + wyckoff_ids (dict): Maps unique numerical identifier to wyckoff + position. + nn_interacations (dict): {i: j} pairs of NN interactions + between unique sites. + dists (dict): NN, NNN, and NNNN interaction distances + ex_mat (DataFrame): Invertible Heisenberg Hamiltonian for each + graph. + ex_params (dict): Exchange parameter values (meV/atom) + + """ + + # Save original copies of inputs + self.ordered_structures_ = ordered_structures + self.energies_ = energies + + # Sanitize inputs and order them by energy / magnetic moments + hs = HeisenbergScreener(ordered_structures, energies) + ordered_structures = hs.screened_structures + energies = hs.screened_energies + + self.ordered_structures = ordered_structures + self.energies = energies + self.cutoff = cutoff + self.tol = tol + + # Get graph representations + self.sgraphs = self._get_graphs(cutoff, ordered_structures) + + # Get unique site ids and wyckoff symbols + self.unique_site_ids, self.wyckoff_ids = self._get_unique_sites( + ordered_structures[0] + ) + + # These attributes are set by internal methods + self.nn_interactions = None + self.dists = None + self.ex_mat = None + self.ex_params = None + + # Check how many commensurate graphs we found + if len(self.sgraphs) < 2: + print("We need at least 2 unique orderings.") + sys.exit(1) + else: # Set attributes + self._get_nn_dict() + self._get_exchange_df() + + @staticmethod + def _get_graphs(cutoff, ordered_structures): + """ + Generate graph representations of magnetic structures with nearest + neighbor bonds. Right now this only works for MinimumDistanceNN. + + Args: + cutoff (float): Cutoff in Angstrom for nearest neighbor search. + ordered_structures (list): Structure objects. + + Returns: + sgraphs (list): StructureGraph objects. + + """ + + # Strategy for finding neighbors + if cutoff: + strategy = MinimumDistanceNN(cutoff=cutoff, get_all_sites=True) + else: + strategy = MinimumDistanceNN() # only NN + + # Generate structure graphs + sgraphs = [ + StructureGraph.with_local_env_strategy(s, strategy=strategy) + for s in ordered_structures + ] + + return sgraphs + + @staticmethod + def _get_unique_sites(structure): + """ + Get dict that maps site indices to unique identifiers. + + Args: + structure (Structure): ground state Structure object. + + Returns: + unique_site_ids (dict): maps tuples of equivalent site indices to a + unique int identifier + wyckoff_ids (dict): maps tuples of equivalent site indices to their + wyckoff symbols + + """ + + # Get a nonmagnetic representation of the supercell geometry + s0 = CollinearMagneticStructureAnalyzer( + structure, make_primitive=False, threshold=0.0 + ).get_nonmagnetic_structure(make_primitive=False) + + # Get unique sites and wyckoff positions + if "wyckoff" in s0.site_properties: + s0.remove_site_property("wyckoff") + + symm_s0 = SpacegroupAnalyzer(s0).get_symmetrized_structure() + wyckoff = ["n/a"] * len(symm_s0) + equivalent_indices = symm_s0.equivalent_indices + wyckoff_symbols = symm_s0.wyckoff_symbols + + # Construct dictionaries that map sites to numerical and wyckoff + # identifiers + unique_site_ids = {} + wyckoff_ids = {} + + i = 0 + for indices, symbol in zip(equivalent_indices, wyckoff_symbols): + unique_site_ids[tuple(indices)] = i + wyckoff_ids[i] = symbol + i += 1 + for index in indices: + wyckoff[index] = symbol + + return unique_site_ids, wyckoff_ids + + def _get_nn_dict(self): + """Get dict of unique nearest neighbor interactions. + + Returns: + None: (sets self.nn_interactions and self.dists instance variables) + + """ + + tol = self.tol # tolerance on NN distances + sgraph = self.sgraphs[0] + unique_site_ids = self.unique_site_ids + + nn_dict = {} + nnn_dict = {} + nnnn_dict = {} + + all_dists = [] + + # Loop over unique sites and get neighbor distances up to NNNN + for k in unique_site_ids: + i = k[0] + i_key = unique_site_ids[k] + connected_sites = sgraph.get_connected_sites(i) + dists = [round(cs[-1], 2) for cs in connected_sites] # i<->j distances + dists = sorted(list(set(dists))) # NN, NNN, NNNN, etc. + + dists = dists[:3] # keep up to NNNN + all_dists += dists + + # Keep only up to NNNN and call dists equal if they are within tol + all_dists = sorted(list(set(all_dists))) + rm_list = [] + for idx, d in enumerate(all_dists[:-1]): + if abs(d - all_dists[idx + 1]) < tol: + rm_list.append(idx + 1) + + all_dists = [d for idx, d in enumerate(all_dists) if idx not in rm_list] + + if len(all_dists) < 3: # pad with zeros + all_dists += [0.0] * (3 - len(all_dists)) + + all_dists = all_dists[:3] + labels = ["nn", "nnn", "nnnn"] + dists = {l: d for (l, d) in zip(labels, all_dists)} + + # Get dictionary keys for interactions + for k in unique_site_ids: + i = k[0] + i_key = unique_site_ids[k] + connected_sites = sgraph.get_connected_sites(i) + + # Loop over sites and determine unique NN, NNN, etc. interactions + for cs in connected_sites: + dist = round(cs[-1], 2) # i_j distance + + j = cs[2] # j index + for key in unique_site_ids.keys(): + if j in key: + j_key = unique_site_ids[key] + if abs(dist - dists["nn"]) <= tol: + nn_dict[i_key] = j_key + elif abs(dist - dists["nnn"]) <= tol: + nnn_dict[i_key] = j_key + elif abs(dist - dists["nnnn"]) <= tol: + nnnn_dict[i_key] = j_key + + nn_interactions = {"nn": nn_dict, "nnn": nnn_dict, "nnnn": nnnn_dict} + + self.dists = dists + self.nn_interactions = nn_interactions + + def _get_exchange_df(self): + """ + Loop over all sites in a graph and count the number and types of + nearest neighbor interactions, computing +-|S_i . S_j| to construct + a Heisenberg Hamiltonian for each graph. + + Returns: + None: (sets self.ex_mat instance variable) + + TODO: + * Deal with large variance in |S| across configs + + """ + + sgraphs = self.sgraphs + tol = self.tol + unique_site_ids = self.unique_site_ids + nn_interactions = self.nn_interactions + dists = self.dists + + # Get |site magmoms| from FM ordering so that S_i and S_j are consistent? + # Large S variations is throwing a loop + # fm_struct = self.get_low_energy_orderings()[0] + + # Total energy and nonmagnetic energy contribution + columns = ["E", "E0"] + + # Get labels of unique NN interactions + for k0, v0 in nn_interactions.items(): + for i, j in v0.items(): # i and j indices + c = str(i) + "-" + str(j) + "-" + str(k0) + c_rev = str(j) + "-" + str(i) + "-" + str(k0) + if c not in columns and c_rev not in columns: + columns.append(c) + + num_sgraphs = len(sgraphs) + + # Keep n interactions (not counting 'E') for n+1 structure graphs + columns = columns[: num_sgraphs + 1] + + num_nn_j = len(columns) - 1 # ignore total energy + j_columns = [name for name in columns if name not in ["E", "E0"]] + ex_mat_empty = pd.DataFrame(columns=columns) + ex_mat = ex_mat_empty.copy() + + if len(j_columns) < 2: + self.ex_mat = ex_mat # Only can be calculated here + else: + sgraphs_copy = copy.deepcopy(sgraphs) + sgraph_index = 0 + + # Loop over all sites in each graph and compute |S_i . S_j| + # for n+1 unique graphs to compute n exchange params + for graph in sgraphs: + sgraph = sgraphs_copy.pop(0) + ex_row = pd.DataFrame( + np.zeros((1, num_nn_j + 1)), index=[sgraph_index], columns=columns + ) + + for i, node in enumerate(sgraph.graph.nodes): + # s_i_sign = np.sign(sgraph.structure.site_properties['magmom'][i]) + s_i = sgraph.structure.site_properties["magmom"][i] + + for k in unique_site_ids.keys(): + if i in k: + i_index = unique_site_ids[k] + + # Get all connections for ith site and compute |S_i . S_j| + connections = sgraph.get_connected_sites(i) + # dists = [round(cs[-1], 2) for cs in connections] # i<->j distances + # dists = sorted(list(set(dists))) # NN, NNN, NNNN, etc. + + for j, connection in enumerate(connections): + j_site = connection[2] + dist = round(connection[-1], 2) # i_j distance + + # s_j_sign = np.sign(sgraph.structure.site_properties['magmom'][j_site]) + s_j = sgraph.structure.site_properties["magmom"][j_site] + + for k in unique_site_ids.keys(): + if j_site in k: + j_index = unique_site_ids[k] + + # Determine order of connection + if abs(dist - dists["nn"]) <= tol: + order = "-nn" + elif abs(dist - dists["nnn"]) <= tol: + order = "-nnn" + elif abs(dist - dists["nnnn"]) <= tol: + order = "-nnnn" + j_ij = str(i_index) + "-" + str(j_index) + order + j_ji = str(j_index) + "-" + str(i_index) + order + + if j_ij in ex_mat.columns: + ex_row.at[sgraph_index, j_ij] -= s_i * s_j + elif j_ji in ex_mat.columns: + ex_row.at[sgraph_index, j_ji] -= s_i * s_j + + # Ignore the row if it is a duplicate to avoid singular matrix + if ex_mat.append(ex_row)[j_columns].equals( + ex_mat.append(ex_row)[j_columns].drop_duplicates(keep="first") + ): + e_index = self.ordered_structures.index(sgraph.structure) + ex_row.at[sgraph_index, "E"] = self.energies[e_index] + sgraph_index += 1 + ex_mat = ex_mat.append(ex_row) + # if sgraph_index == num_nn_j: # check for zero columns + # zeros = [b for b in (ex_mat[j_columns] == 0).all(axis=0)] + # if True in zeros: + # sgraph_index -= 1 # keep looking + + ex_mat[j_columns] = ex_mat[j_columns].div( + 2.0 + ) # 1/2 factor in Heisenberg Hamiltonian + ex_mat[["E0"]] = 1 # Nonmagnetic contribution + + # Check for singularities and delete columns with all zeros + zeros = [b for b in (ex_mat == 0).all(axis=0)] + if True in zeros: + c = ex_mat.columns[zeros.index(True)] + len_zeros = len(c) + ex_mat = ex_mat.drop(columns=[c], axis=1) + # ex_mat = ex_mat.drop(ex_mat.tail(len_zeros).index) + + # Force ex_mat to be square + ex_mat = ex_mat[: ex_mat.shape[1] - 1] + + self.ex_mat = ex_mat + + def get_exchange(self): + """ + Take Heisenberg Hamiltonian and corresponding energy for each row and + solve for the exchange parameters. + + Returns: + ex_params (dict): Exchange parameter values (meV/atom). + + """ + + ex_mat = self.ex_mat + # Solve the matrix equation for J_ij values + E = ex_mat[["E"]] + j_names = [j for j in ex_mat.columns if j not in ["E"]] + + # Only 1 NN interaction + if len(j_names) < 3: + + # Estimate exchange by J ~ E_AFM - E_FM + j_avg = self.estimate_exchange() + ex_params = {"": j_avg} + self.ex_params = ex_params + + return ex_params + + # Solve eigenvalue problem for more than 1 NN interaction + H = ex_mat.loc[:, ex_mat.columns != "E"].values + H_inv = np.linalg.inv(H) + j_ij = np.dot(H_inv, E) + + # Convert J_ij to meV + j_ij[1:] *= 1000 # J_ij in meV + j_ij = j_ij.tolist() + ex_params = {j_name: j[0] for j_name, j in zip(j_names, j_ij)} + + self.ex_params = ex_params + + return ex_params + + def get_low_energy_orderings(self): + """ + Find lowest energy FM and AFM orderings to compute E_AFM - E_FM. + + Returns: + fm_struct (Structure): fm structure with 'magmom' site property + afm_struct (Structure): afm structure with 'magmom' site property + fm_e (float): fm energy + afm_e (float): afm energy + + """ + + fm_struct, afm_struct = None, None + mag_min = np.inf + mag_max = 0.001 + fm_e_min = 0 + afm_e_min = 0 + afm_threshold = 1 # total magnetization < threshold -> AFM, not FiM + + for s, e in zip(self.ordered_structures, self.energies): + + ordering = CollinearMagneticStructureAnalyzer( + s, threshold=0.0, make_primitive=False + ).ordering + magmoms = s.site_properties["magmom"] + + # Try to find matching orderings first + if ordering == Ordering.FM and e < fm_e_min: + fm_struct = s + mag_max = abs(sum(magmoms)) + fm_e = e + fm_e_min = e + + if ordering == Ordering.AFM and e < afm_e_min: + afm_struct = s + afm_e = e + mag_min = abs(sum(magmoms)) + afm_e_min = e + + # Brute force search for closest thing to FM and AFM + if not fm_struct or not afm_struct: + for s, e in zip(self.ordered_structures, self.energies): + magmoms = s.site_properties["magmom"] + + if abs(sum(magmoms)) > mag_max: # FM ground state + fm_struct = s + fm_e = e + mag_max = abs(sum(magmoms)) + + # AFM ground state + if abs(sum(magmoms)) < mag_min: + afm_struct = s + afm_e = e + mag_min = abs(sum(magmoms)) + afm_e_min = e + elif abs(sum(magmoms)) == 0 and mag_min == 0: + if e < afm_e_min: + afm_struct = s + afm_e = e + afm_e_min = e + + # Convert to magnetic structures with 'magmom' site property + fm_struct = CollinearMagneticStructureAnalyzer( + fm_struct, make_primitive=False, threshold=0.0 + ).get_structure_with_only_magnetic_atoms(make_primitive=False) + + afm_struct = CollinearMagneticStructureAnalyzer( + afm_struct, make_primitive=False, threshold=0.0 + ).get_structure_with_only_magnetic_atoms(make_primitive=False) + return fm_struct, afm_struct, fm_e, afm_e + + def estimate_exchange(self, fm_struct=None, afm_struct=None, fm_e=None, afm_e=None): + """ + Estimate for a structure based on low energy FM and AFM orderings. + + Args: + fm_struct (Structure): fm structure with 'magmom' site property + afm_struct (Structure): afm structure with 'magmom' site property + fm_e (float): fm energy + afm_e (float): afm energy + + Returns: + j_avg (float): Average exchange parameter (meV/atom) + + """ + + # Get low energy orderings if not supplied + if any(arg is None for arg in [fm_struct, afm_struct, fm_e, afm_e]): + fm_struct, afm_struct, fm_e, afm_e = self.get_low_energy_orderings() + + n = max(len(fm_struct), len(afm_struct)) # num of magnetic atoms + # fm_e *= n # eV/ion -> eV + # afm_e *= n + + magmoms = fm_struct.site_properties["magmom"] + afm_magmoms = afm_struct.site_properties["magmom"] + + m_avg = np.mean([np.sqrt(m ** 2) for m in magmoms]) + afm_m_avg = np.mean([np.sqrt(m ** 2) for m in afm_magmoms]) + + # If m_avg for FM config is < 1 we won't get sensibile results. + if m_avg < 1: + iamthedanger = """ + Local magnetic moments are small (< 1 muB / atom). The + exchange parameters may be wrong, but and the mean + field critical temperature estimate may be OK. + """ + logging.warning(iamthedanger) + + delta_e = afm_e - fm_e # J > 0 -> FM + j_avg = delta_e / (n * m_avg ** 2) # eV / magnetic ion + j_avg *= 1000 # meV / ion + + return j_avg + + def get_mft_temperature(self, j_avg): + """ + Crude mean field estimate of critical temperature based on for + one sublattice, or solving the coupled equations for a multisublattice + material. + + Args: + j_avg (float): j_avg (float): Average exchange parameter (meV/atom) + + Returns: + mft_t (float): Critical temperature (K) + + """ + + num_sublattices = len(self.unique_site_ids) + k_boltzmann = 0.0861733 # meV/K + + # Only 1 magnetic sublattice + if num_sublattices == 1: + + mft_t = 2 * abs(j_avg) / 3 / k_boltzmann + + else: # multiple magnetic sublattices + omega = np.zeros((num_sublattices, num_sublattices)) + ex_params = self.ex_params + ex_params = {k: v for (k, v) in ex_params.items() if k != "E0"} # ignore E0 + for k in ex_params: + # split into i, j unique site identifiers + sites = [elem for elem in k.split("-")] + sites = [int(num) for num in sites[:2]] # cut 'nn' identifier + i, j = sites[0], sites[1] + omega[i, j] += ex_params[k] + omega[j, i] += ex_params[k] + + omega = omega * 2 / 3 / k_boltzmann + eigenvals, eigenvecs = np.linalg.eig(omega) + mft_t = max(eigenvals) + + if mft_t > 1500: # Not sensible! + stayoutofmyterritory = """ + This mean field estimate is too high! Probably + the true low energy orderings were not given as inputs. + """ + logging.warning(stayoutofmyterritory) + + return mft_t + + def get_interaction_graph(self, filename=None): + """ + Get a StructureGraph with edges and weights that correspond to exchange + interactions and J_ij values, respectively. + + Args: + filename (str): if not None, save interaction graph to filename. + Returns: + igraph (StructureGraph): Exchange interaction graph. + """ + + structure = self.ordered_structures[0] + sgraph = self.sgraphs[0] + unique_site_ids = self.unique_site_ids + dists = self.dists + tol = self.tol + + igraph = StructureGraph.with_empty_graph( + structure, edge_weight_name="exchange_constant", edge_weight_units="meV" + ) + + if "" in self.ex_params: # Only is available + warning_msg = """ + Only is available. The interaction graph will not tell + you much. + """ + warnings.warn(warning_msg) + + # J_ij exchange interaction matrix + for i, node in enumerate(sgraph.graph.nodes): + connections = sgraph.get_connected_sites(i) + for c in connections: + jimage = c[1] # relative integer coordinates of atom j + dx = jimage[0] + dy = jimage[1] + dz = jimage[2] + j = c[2] # index of neighbor + dist = c[-1] # i <-> j distance + + j_exc = self._get_j_exc(i, j, dist) + + igraph.add_edge( + i, j, to_jimage=jimage, weight=j_exc, warn_duplicates=False + ) + + # Save to a json file if desired + if filename: + if filename.endswith(".json"): + dumpfn(igraph, filename) + else: + filename += ".json" + dumpfn(igraph, filename) + + return igraph + + def _get_j_exc(self, i, j, dist): + """ + Convenience method for looking up exchange parameter between two sites. + + Args: + i (int): index of ith site + j (int): index of jth site + dist (float): distance (Angstrom) between sites + (10E-2 precision) + + Returns: + j_exc (float): Exchange parameter in meV + """ + + # Get unique site identifiers + for k in self.unique_site_ids.keys(): + if i in k: + i_index = self.unique_site_ids[k] + if j in k: + j_index = self.unique_site_ids[k] + + order = "" + + # Determine order of interaction + if abs(dist - self.dists["nn"]) <= self.tol: + order = "-nn" + elif abs(dist - self.dists["nnn"]) <= self.tol: + order = "-nnn" + elif abs(dist - self.dists["nnnn"]) <= self.tol: + order = "-nnnn" + + j_ij = str(i_index) + "-" + str(j_index) + order + j_ji = str(j_index) + "-" + str(i_index) + order + + if j_ij in self.ex_params: + j_exc = self.ex_params[j_ij] + elif j_ji in self.ex_params: + j_exc = self.ex_params[j_ji] + else: + j_exc = 0 + + # Check if only averaged NN values are available + if "" in self.ex_params and order == "-nn": + j_exc = self.ex_params[""] + + return j_exc + + +class HeisenbergScreener: + def __init__(self, structures, energies): + """Clean and screen magnetic orderings. + + This class pre-processes magnetic orderings and energies for HeisenbergMapper. It prioritizes low-energy orderings with large and localized magnetic moments. + + Args: + structures (list): Structure objects with magnetic moments. + energies (list): energies of magnetic orderings. + + Attributes: + screened_structures (list): Sorted structures. + screened_energies (list): Sorted energies. + + """ + + # Cleanup + structures, energies = self._do_cleanup(structures, energies) + + n_structures = len(structures) + + # If there are more than 2 structures, we want to perform a + # screening to prioritize well-behaved orderings + if n_structures > 2: + structures, energies = self._do_screen(structures, energies) + + self.screened_structures = structures + self.screened_energies = energies + + @staticmethod + def _do_cleanup(structures, energies): + """Sanitize input structures and energies. + + Takes magnetic structures and performs the following operations + - Erases nonmagnetic ions and gives all ions ['magmom'] site prop + - Checks for duplicate/degenerate orderings + - Sorts by energy + + Args: + structures (list): Structure objects with magmoms. + energies (list): Corresponding energies. + + Returns: + ordered_structures (list): Sanitized structures. + ordered_energies (list): Sorted energies. + + """ + + # Get only magnetic ions & give all structures site_properties['magmom'] + # zero threshold so that magnetic ions with small moments + # are preserved + ordered_structures = [ + CollinearMagneticStructureAnalyzer( + s, make_primitive=False, threshold=0.0 + ).get_structure_with_only_magnetic_atoms(make_primitive=False) + for s in structures + ] + + # Check for duplicate / degenerate states (sometimes different initial + # configs relax to the same state) + remove_list = [] + for i, e in enumerate(energies): + e_tol = 6 # 10^-4 eV tol on equal energies + e = round(e, e_tol) + if i not in remove_list: + for i_check, e_check in enumerate(energies): + e_check = round(e_check, e_tol) + if i != i_check and i_check not in remove_list and e == e_check: + remove_list.append(i_check) + + # Also discard structures with small |magmoms| < 0.1 uB + # xx - get rid of these or just bury them in the list? + # for i, s in enumerate(ordered_structures): + # magmoms = s.site_properties['magmom'] + # if i not in remove_list: + # if any(abs(m) < 0.1 for m in magmoms): + # remove_list.append(i) + + # Remove duplicates + if len(remove_list): + ordered_structures = [ + s for i, s in enumerate(ordered_structures) if i not in remove_list + ] + energies = [e for i, e in enumerate(energies) if i not in remove_list] + + # Sort by energy if not already sorted + ordered_structures = [ + s for _, s in sorted(zip(energies, ordered_structures), reverse=False) + ] + ordered_energies = sorted(energies, reverse=False) + + return ordered_structures, ordered_energies + + @staticmethod + def _do_screen(structures, energies): + """Screen and sort magnetic orderings based on some criteria. + + Prioritize low energy orderings and large, localized magmoms. do_clean should be run first to sanitize inputs. + + Args: + structures (list): At least three structure objects. + energies (list): Energies. + + Returns: + screened_structures (list): Sorted structures. + screened_energies (list): Sorted energies. + + """ + + magmoms = [s.site_properties["magmom"] for s in structures] + mean_mm = [np.mean([abs(m) for m in ms]) for ms in magmoms] + max_mm = [np.max([abs(m) for m in ms]) for ms in magmoms] + min_mm = [np.min([abs(m) for m in ms]) for ms in magmoms] + n_below_1ub = [len([m for m in ms if abs(m) < 1]) for ms in magmoms] + + df = pd.DataFrame( + { + "structure": structures, + "energy": energies, + "magmoms": magmoms, + "n_below_1ub": n_below_1ub, + } + ) + + # keep the ground and first excited state fixed to capture the + # low-energy spectrum + index = list(df.index)[2:] + df_high_energy = df.iloc[2:] + + # Prioritize structures with fewer magmoms < 1 uB + df_high_energy = df_high_energy.sort_values(by="n_below_1ub") + + index = [0, 1] + list(df_high_energy.index) + + # sort + df = df.reindex(index) + screened_structures = list(df["structure"].values) + screened_energies = list(df["energy"].values) + + return screened_structures, screened_energies diff --git a/pymatgen/analysis/magnetism/tests/c6173e8c-0cf6-4af3-b659-f1a901c27910.dmp b/pymatgen/analysis/magnetism/tests/c6173e8c-0cf6-4af3-b659-f1a901c27910.dmp new file mode 100644 index 00000000000..2c34b618870 Binary files /dev/null and b/pymatgen/analysis/magnetism/tests/c6173e8c-0cf6-4af3-b659-f1a901c27910.dmp differ diff --git a/pymatgen/analysis/magnetism/tests/igraph.json b/pymatgen/analysis/magnetism/tests/igraph.json new file mode 100644 index 00000000000..677cad3b99e --- /dev/null +++ b/pymatgen/analysis/magnetism/tests/igraph.json @@ -0,0 +1 @@ +{"@module": "pymatgen.analysis.graphs", "@class": "StructureGraph", "structure": {"@module": "pymatgen.core.structure", "@class": "Structure", "charge": null, "lattice": {"matrix": [[2.898976, -2.899063, 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{"to_jimage": [-2, -1, 0], "id": 5, "key": 9}, {"to_jimage": [0, -1, 1], "id": 5, "key": 10}, {"to_jimage": [0, 1, -1], "id": 5, "key": 11}, {"to_jimage": [1, 1, -1], "id": 5, "key": 12}, {"to_jimage": [-1, -1, 1], "id": 5, "key": 13}, {"to_jimage": [0, 0, 1], "id": 5, "key": 14}, {"to_jimage": [0, 0, -1], "id": 5, "key": 15}, {"to_jimage": [1, 2, 0], "id": 5, "key": 16}, {"to_jimage": [-1, -2, 0], "id": 5, "key": 17}]]}, "@version": "2019.6.5"} \ No newline at end of file diff --git a/pymatgen/analysis/magnetism/tests/test_heisenberg.py b/pymatgen/analysis/magnetism/tests/test_heisenberg.py new file mode 100644 index 00000000000..9fb234979f8 --- /dev/null +++ b/pymatgen/analysis/magnetism/tests/test_heisenberg.py @@ -0,0 +1,94 @@ +# coding: utf-8 +# Copyright (c) Pymatgen Development Team. +# Distributed under the terms of the MIT License. + +import warnings + +import os +import unittest +import pandas as pd + +from monty.serialization import loadfn + +from pymatgen.analysis.magnetism.heisenberg import HeisenbergMapper +from pymatgen import Structure + +test_dir = os.path.join( + os.path.dirname(__file__), + "..", + "..", + "..", + "..", + "test_files", + "magnetic_orderings", +) + + +class HeisenbergMapperTest(unittest.TestCase): + @classmethod + def setUpClass(cls): + cls.df = pd.read_json(os.path.join(test_dir, "mag_orderings_test_cases.json")) + + # Good tests + cls.Mn3Al = pd.read_json(os.path.join(test_dir, "Mn3Al.json")) + + cls.compounds = [cls.Mn3Al] + + cls.hms = [] + for c in cls.compounds: + ordered_structures = list(c["structure"]) + ordered_structures = [Structure.from_dict(d) for d in ordered_structures] + epa = list(c["energy_per_atom"]) + energies = [e * len(s) for (e, s) in zip(epa, ordered_structures)] + + hm = HeisenbergMapper(ordered_structures, energies, cutoff=7.5, tol=0.02) + cls.hms.append(hm) + + def setUp(self): + pass + + def tearDown(self): + warnings.simplefilter("default") + + def test_graphs(self): + for hm in self.hms: + sgraphs = hm.sgraphs + self.assertEqual(len(sgraphs), 7) + + def test_sites(self): + for hm in self.hms: + unique_site_ids = hm.unique_site_ids + self.assertEqual(unique_site_ids[(0, 1)], 0) + + def test_nn_interactions(self): + for hm in self.hms: + num_interacts = len(hm.nn_interactions) + self.assertEqual(num_interacts, 3) + + dists = hm.dists + self.assertEqual(dists["nn"], 2.51) + + def test_exchange_params(self): + for hm in self.hms: + ex_params = hm.get_exchange() + J_nn = round(101.01616118049606, 3) + self.assertEqual(round(ex_params["0-1-nn"], 3), J_nn) + + def test_mean_field(self): + for hm in self.hms: + j_avg = hm.estimate_exchange() + value = round(52.54997149705518, 3) + self.assertEqual(round(j_avg, 3), value) + + mft_t = hm.get_mft_temperature(j_avg) + value = round(1031.1626039484843) + self.assertEqual(round(mft_t), value) + + def test_get_igraph(self): + for hm in self.hms: + igraph = hm.get_interaction_graph("igraph.json") + self.assertEqual(len(igraph), 6) + + +if __name__ == "__main__": + unittest.main() diff --git a/pymatgen/command_line/tests/test_vampire_caller.py b/pymatgen/command_line/tests/test_vampire_caller.py new file mode 100644 index 00000000000..0424fbe4d00 --- /dev/null +++ b/pymatgen/command_line/tests/test_vampire_caller.py @@ -0,0 +1,68 @@ +# coding: utf-8 +# Copyright (c) Pymatgen Development Team. +# Distributed under the terms of the MIT License. + +import unittest +import os +import warnings + +import pandas as pd + +from monty.os.path import which + +import pymatgen.command_line.vampire_caller as vampirecaller +from pymatgen.analysis.magnetism.heisenberg import HeisenbergMapper + +from pymatgen import Structure + +test_dir = os.path.join( + os.path.dirname(__file__), "..", "..", "..", "test_files", "magnetic_orderings" +) + + +@unittest.skipIf(not which("vampire-serial"), "vampire executable not present") +class VampireCallerTest(unittest.TestCase): + @classmethod + def setUpClass(cls): + + print("Testing with: ", which("vampire-serial")) + + cls.Mn3Al = pd.read_json(os.path.join(test_dir, "Mn3Al.json")) + + cls.compounds = [cls.Mn3Al] + + cls.structure_inputs = [] + cls.energy_inputs = [] + for c in cls.compounds: + ordered_structures = list(c["structure"]) + ordered_structures = [Structure.from_dict(d) for d in ordered_structures] + epa = list(c["energy_per_atom"]) + energies = [e * len(s) for (e, s) in zip(epa, ordered_structures)] + + cls.structure_inputs.append(ordered_structures) + cls.energy_inputs.append(energies) + + def setUp(self): + pass + + def tearDown(self): + warnings.simplefilter("default") + + def test_vampire(self): + for structs, energies in zip(self.structure_inputs, self.energy_inputs): + settings = {"start_t": 0, "end_t": 500, "temp_increment": 50} + vc = vampirecaller.VampireCaller( + structs, + energies, + mc_box_size=3.0, + equil_timesteps=1000, + mc_timesteps=2000, + user_input_settings=settings, + ) + + critical_temp = vc.output.critical_temp + self.assertAlmostEqual(400, critical_temp, delta=100) + + +if __name__ == "__main__": + unittest.main() diff --git a/pymatgen/command_line/vampire_caller.py b/pymatgen/command_line/vampire_caller.py new file mode 100644 index 00000000000..c79fab560dc --- /dev/null +++ b/pymatgen/command_line/vampire_caller.py @@ -0,0 +1,427 @@ +# coding: utf-8 +# Copyright (c) Pymatgen Development Team. +# Distributed under the terms of the MIT License. + +import subprocess +import logging +import numpy as np +import pandas as pd +import os + +from monty.dev import requires +from monty.os.path import which + +from pymatgen.analysis.magnetism.heisenberg import HeisenbergMapper +from pymatgen.analysis.magnetism.analyzer import CollinearMagneticStructureAnalyzer + +""" +This module implements an interface to the VAMPIRE code for atomistic +simulations of magnetic materials. + +This module depends on a compiled vampire executable available in the path. +Please download at https://vampire.york.ac.uk/download/ and +follow the instructions to compile the executable. + +If you use this module, please cite the following: + +"Atomistic spin model simulations of magnetic nanomaterials." +R. F. L. Evans, W. J. Fan, P. Chureemart, T. A. Ostler, M. O. A. Ellis +and R. W. Chantrell. J. Phys.: Condens. Matter 26, 103202 (2014) +""" + +__author__ = "ncfrey" +__version__ = "0.1" +__maintainer__ = "Nathan C. Frey" +__email__ = "ncfrey@lbl.gov" +__status__ = "Development" +__date__ = "June 2019" + +VAMPEXE = which("vampire-serial") + + +class VampireCaller: + @requires( + VAMPEXE, + "VampireCaller requires vampire-serial to be in the path." + "Please follow the instructions at https://vampire.york.ac.uk/download/.", + ) + def __init__( + self, + ordered_structures, + energies, + mc_box_size=4.0, + equil_timesteps=2000, + mc_timesteps=4000, + save_inputs=False, + hm=None, + user_input_settings=None, + ): + + """ + Run Vampire on a material with magnetic ordering and exchange parameter information to compute the critical temperature with classical Monte Carlo. + + user_input_settings is a dictionary that can contain: + * start_t (int): Start MC sim at this temp, defaults to 0 K. + * end_t (int): End MC sim at this temp, defaults to 1500 K. + * temp_increment (int): Temp step size, defaults to 25 K. + + Args: + ordered_structures (list): Structure objects with magmoms. + energies (list): Energies of each relaxed magnetic structure. + mc_box_size (float): x=y=z dimensions (nm) of MC simulation box + equil_timesteps (int): number of MC steps for equilibrating + mc_timesteps (int): number of MC steps for averaging + save_inputs (bool): if True, save scratch dir of vampire input files + hm (HeisenbergMapper): object already fit to low energy + magnetic orderings. + user_input_settings (dict): optional commands for VAMPIRE Monte Carlo + + Parameters: + sgraph (StructureGraph): Ground state graph. + unique_site_ids (dict): Maps each site to its unique identifier + nn_interacations (dict): {i: j} pairs of NN interactions + between unique sites. + ex_params (dict): Exchange parameter values (meV/atom) + mft_t (float): Mean field theory estimate of critical T + mat_name (str): Formula unit label for input files + mat_id_dict (dict): Maps sites to material id # for vampire + indexing. + + TODO: + * Create input files in a temp folder that gets cleaned up after run terminates + + """ + + self.mc_box_size = mc_box_size + self.equil_timesteps = equil_timesteps + self.mc_timesteps = mc_timesteps + self.save_inputs = save_inputs + self.user_input_settings = user_input_settings + + # Sort by energy if not already sorted + ordered_structures = [ + s for _, s in sorted(zip(energies, ordered_structures), reverse=False) + ] + + energies = sorted(energies, reverse=False) + + # Get exchange parameters and set instance variables + if not hm: + hm = HeisenbergMapper(ordered_structures, energies, cutoff=7.5, tol=0.02) + + # Instance attributes from HeisenbergMapper + self.hm = hm + self.structure = hm.ordered_structures[0] # ground state + self.sgraph = hm.sgraphs[0] # ground state graph + self.unique_site_ids = hm.unique_site_ids + self.nn_interactions = hm.nn_interactions + self.dists = hm.dists + self.tol = hm.tol + self.ex_params = hm.get_exchange() + + # Full structure name before reducing to only magnetic ions + self.mat_name = str(hm.ordered_structures_[0].composition.reduced_formula) + + # Switch to scratch dir which automatically cleans up vampire inputs files unless user specifies to save them + # with ScratchDir('/scratch', copy_from_current_on_enter=self.save_inputs, copy_to_current_on_exit=self.save_inputs) as temp_dir: + + # os.chdir(temp_dir) + + # Create input files + self._create_mat() + self._create_input() + self._create_ucf() + + # Call Vampire + process = subprocess.Popen( + ["vampire-serial"], stdout=subprocess.PIPE, stderr=subprocess.PIPE + ) + stdout, stderr = process.communicate() + stdout = stdout.decode() + + if stderr: + vanhelsing = stderr.decode() + if len(vanhelsing) > 27: # Suppress blank warning msg + logging.warning(vanhelsing) + + if process.returncode != 0: + raise RuntimeError( + "Vampire exited with return code {}.".format(process.returncode) + ) + + self._stdout = stdout + self._stderr = stderr + + # Process output + nmats = max(self.mat_id_dict.values()) + self.output = VampireOutput("output", nmats) + + def _create_mat(self): + + structure = self.structure + mat_name = self.mat_name + magmoms = structure.site_properties["magmom"] + + # Maps sites to material id for vampire inputs + mat_id_dict = {} + + nmats = 0 + for key in self.unique_site_ids: + spin_up, spin_down = False, False + nmats += 1 # at least 1 mat for each unique site + + # Check which spin sublattices exist for this site id + for site in key: + m = magmoms[site] + if m > 0: + spin_up = True + if m < 0: + spin_down = True + + # Assign material id for each site + for site in key: + m = magmoms[site] + if spin_up and not spin_down: + mat_id_dict[site] = nmats + if spin_down and not spin_up: + mat_id_dict[site] = nmats + if spin_up and spin_down: + + # Check if spin up or down shows up first + m0 = magmoms[key[0]] + if m > 0 and m0 > 0: + mat_id_dict[site] = nmats + if m < 0 and m0 < 0: + mat_id_dict[site] = nmats + if m > 0 and m0 < 0: + mat_id_dict[site] = nmats + 1 + if m < 0 and m0 > 0: + mat_id_dict[site] = nmats + 1 + + # Increment index if two sublattices + if spin_up and spin_down: + nmats += 1 + + mat_file = ["material:num-materials=%d" % (nmats)] + + for key in self.unique_site_ids: + i = self.unique_site_ids[key] # unique site id + + for site in key: + mat_id = mat_id_dict[site] + + # Only positive magmoms allowed + m_magnitude = abs(magmoms[site]) + + if magmoms[site] > 0: + spin = 1 + if magmoms[site] < 0: + spin = -1 + + atom = structure[i].species.reduced_formula + + mat_file += ["material[%d]:material-element=%s" % (mat_id, atom)] + mat_file += [ + "material[%d]:damping-constant=1.0" % (mat_id), + "material[%d]:uniaxial-anisotropy-constant=1.0e-24" + % (mat_id), # xx - do we need this? + "material[%d]:atomic-spin-moment=%.2f !muB" % (mat_id, m_magnitude), + "material[%d]:initial-spin-direction=0,0,%d" % (mat_id, spin), + ] + + mat_file = "\n".join(mat_file) + mat_file_name = mat_name + ".mat" + + self.mat_id_dict = mat_id_dict + + with open(mat_file_name, "w") as f: + f.write(mat_file) + + def _create_input(self): + """Todo: + * How to determine range and increment of simulation? + """ + + structure = self.structure + mcbs = self.mc_box_size + equil_timesteps = self.equil_timesteps + mc_timesteps = self.mc_timesteps + mat_name = self.mat_name + + input_script = ["material:unit-cell-file=%s.ucf" % (mat_name)] + input_script += ["material:file=%s.mat" % (mat_name)] + + # Specify periodic boundary conditions + input_script += [ + "create:periodic-boundaries-x", + "create:periodic-boundaries-y", + "create:periodic-boundaries-z", + ] + + # Unit cell size in Angstrom + abc = structure.lattice.abc + ucx, ucy, ucz = abc[0], abc[1], abc[2] + + input_script += ["dimensions:unit-cell-size-x = %.10f !A" % (ucx)] + input_script += ["dimensions:unit-cell-size-y = %.10f !A" % (ucy)] + input_script += ["dimensions:unit-cell-size-z = %.10f !A" % (ucz)] + + # System size in nm + input_script += [ + "dimensions:system-size-x = %.1f !nm" % (mcbs), + "dimensions:system-size-y = %.1f !nm" % (mcbs), + "dimensions:system-size-z = %.1f !nm" % (mcbs), + ] + + # Critical temperature Monte Carlo calculation + input_script += [ + "sim:integrator = monte-carlo", + "sim:program = curie-temperature", + ] + + # Default Monte Carlo params + input_script += [ + "sim:equilibration-time-steps = %d" % (equil_timesteps), + "sim:loop-time-steps = %d" % (mc_timesteps), + "sim:time-steps-increment = 1", + ] + + # Set temperature range and step size of simulation + if "start_t" in self.user_input_settings: + start_t = self.user_input_settings["start_t"] + else: + start_t = 0 + + if "end_t" in self.user_input_settings: + end_t = self.user_input_settings["end_t"] + else: + end_t = 1500 + + if "temp_increment" in self.user_input_settings: + temp_increment = self.user_input_settings["temp_increment"] + else: + temp_increment = 25 + + input_script += [ + "sim:minimum-temperature = %d" % (start_t), + "sim:maximum-temperature = %d" % (end_t), + "sim:temperature-increment = %d" % (temp_increment), + ] + + # Output to save + input_script += [ + "output:temperature", + "output:mean-magnetisation-length", + "output:material-mean-magnetisation-length", + "output:mean-susceptibility", + ] + + input_script = "\n".join(input_script) + + with open("input", "w") as f: + f.write(input_script) + + def _create_ucf(self): + + structure = self.structure + mat_name = self.mat_name + tol = self.tol + dists = self.dists + + abc = structure.lattice.abc + ucx, ucy, ucz = abc[0], abc[1], abc[2] + + ucf = ["# Unit cell size:"] + ucf += ["%.10f %.10f %.10f" % (ucx, ucy, ucz)] + + ucf += ["# Unit cell lattice vectors:"] + a1 = list(structure.lattice.matrix[0]) + ucf += ["%.10f %.10f %.10f" % (a1[0], a1[1], a1[2])] + a2 = list(structure.lattice.matrix[1]) + ucf += ["%.10f %.10f %.10f" % (a2[0], a2[1], a2[2])] + a3 = list(structure.lattice.matrix[2]) + ucf += ["%.10f %.10f %.10f" % (a3[0], a3[1], a3[2])] + + nmats = max(self.mat_id_dict.values()) + + ucf += ["# Atoms num_materials; id cx cy cz mat cat hcat"] + ucf += ["%d %d" % (len(structure), nmats)] + + # Fractional coordinates of atoms + for site, r in enumerate(structure.frac_coords): + # Back to 0 indexing for some reason... + mat_id = self.mat_id_dict[site] - 1 + ucf += ["%d %.10f %.10f %.10f %d 0 0" % (site, r[0], r[1], r[2], mat_id)] + + # J_ij exchange interaction matrix + sgraph = self.sgraph + ninter = 0 + for i, node in enumerate(sgraph.graph.nodes): + ninter += sgraph.get_coordination_of_site(i) + + ucf += ["# Interactions"] + ucf += ["%d isotropic" % (ninter)] + + iid = 0 # counts number of interaction + for i, node in enumerate(sgraph.graph.nodes): + connections = sgraph.get_connected_sites(i) + for c in connections: + jimage = c[1] # relative integer coordinates of atom j + dx = jimage[0] + dy = jimage[1] + dz = jimage[2] + j = c[2] # index of neighbor + dist = round(c[-1], 2) + + # Look up J_ij between the sites + j_exc = self.hm._get_j_exc(i, j, dist) + + # Convert J_ij from meV to Joules + j_exc *= 1.6021766e-22 + + j_exc = str(j_exc) # otherwise this rounds to 0 + + ucf += ["%d %d %d %d %d %d %s" % (iid, i, j, dx, dy, dz, j_exc)] + iid += 1 + + ucf = "\n".join(ucf) + ucf_file_name = mat_name + ".ucf" + + with open(ucf_file_name, "w") as f: + f.write(ucf) + + +class VampireOutput: + def __init__(self, vamp_stdout, nmats): + """ + This class processes results from a Vampire Monte Carlo simulation + and returns the critical temperature. + + Args: + vamp_stdout (txt file): stdout from running vampire-serial. + + Attributes: + critical_temp (float): Monte Carlo Tc result. + """ + + self.vamp_stdout = vamp_stdout + self.critical_temp = np.nan + self._parse_stdout(vamp_stdout, nmats) + + def _parse_stdout(self, vamp_stdout, nmats): + + names = ( + ["T", "m_total"] + + ["m_" + str(i) for i in range(1, nmats + 1)] + + ["X_x", "X_y", "X_z", "X_m", "nan"] + ) + + # Parsing vampire MC output + df = pd.read_csv(vamp_stdout, sep="\t", skiprows=9, header=None, names=names) + df.drop("nan", axis=1, inplace=True) + df.to_csv("vamp_out.txt") + + # Max of susceptibility <-> critical temp + T_crit = df.iloc[df.X_m.idxmax()]["T"] + + self.critical_temp = T_crit diff --git a/test_files/magnetic_orderings/CoS2.json b/test_files/magnetic_orderings/CoS2.json new file mode 100644 index 00000000000..14228648f52 --- /dev/null +++ b/test_files/magnetic_orderings/CoS2.json @@ -0,0 +1 @@ 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