Source code for pycalphad.model

The model module provides support for using a Database to perform
calculations under specified conditions.
import copy
import warnings
from sympy import exp, log, Abs, Add, And, Float, Mul, Piecewise, Pow, S, sin, StrictGreaterThan, Symbol, zoo, oo, nan
from tinydb import where
import pycalphad.variables as v
from pycalphad.core.errors import DofError
from pycalphad.core.constants import MIN_SITE_FRACTION
from pycalphad.core.utils import unpack_components, get_pure_elements, wrap_symbol
from pycalphad.core.constraints import is_multiphase_constraint
import numpy as np
from collections import OrderedDict

# Maximum number of levels deep we check for symbols that are functions of
# other symbols

[docs]class ReferenceState(): """ Define the phase and any fixed state variables as a reference state for a component. Parameters ---------- Attributes ---------- fixed_statevars : dict Dictionary of {StateVariable: value} that will be fixed, e.g. {v.T: 298.15, v.P: 101325} phase_name : str Name of phase species : Species pycalphad Species variable """ def __init__(self, species, reference_phase, fixed_statevars=None): """ Parameters ---------- species : str or Species Species to define the reference state for. Only pure elements supported. reference_phase : str Name of phase fixed_statevars : None, optional Dictionary of {StateVariable: value} that will be fixed, e.g. {v.T: 298.15, v.P: 101325} If None (the default), an empty dict will be created. """ if isinstance(species, v.Species): self.species = species else: self.species = v.Species(species) self.phase_name = reference_phase self.fixed_statevars = fixed_statevars if fixed_statevars is not None else {} def __repr__(self): if len(self.fixed_statevars.keys()) > 0: s = "ReferenceState('{}', '{}', {})".format(, self.phase_name, self.fixed_statevars) else: s = "ReferenceState('{}', '{}')".format(, self.phase_name) return s
[docs]class Model(object): """ Models use an abstract representation of the function for calculation of values under specified conditions. Parameters ---------- dbe : Database Database containing the relevant parameters. comps : list Names of components to consider in the calculation. phase_name : str Name of phase model to build. parameters : dict or list Optional dictionary of parameters to be substituted in the model. A list of parameters will cause those symbols to remain symbolic. This will overwrite parameters specified in the database Methods ------- None yet. Examples -------- None yet. Notes ----- The two sublattice ionic liquid model has several special cases compared to typical models within the compound energy formalism. A few key differences arise. First, variable site ratios (modulated by vacancy site fractions) are used to charge balance the phase. Second, endmembers for neutral species and interactions among only neutral species should be specified using only one sublattice (dropping the cation sublattice). For understanding the special cases used throughout this class, users are referred to: Sundman, "Modification of the two-sublattice model for liquids", Calphad 15(2) (1991) 109-119 """ # We only use the contributions attribute in build_phase. # Users should not access it later since subclasses can override build_phase # and make self.models inconsistent with contributions. # Note that we include atomic ordering last since it uses self.models # to figure out its contribution. contributions = [('ref', 'reference_energy'), ('idmix', 'ideal_mixing_energy'), ('xsmix', 'excess_mixing_energy'), ('mag', 'magnetic_energy'), ('2st', 'twostate_energy'), ('ein', 'einstein_energy'), ('ord', 'atomic_ordering_energy')] def __init__(self, dbe, comps, phase_name, parameters=None): self._dbe = dbe self._endmember_reference_model = None self.components = set() self.constituents = [] self.phase_name = phase_name.upper() phase = dbe.phases[self.phase_name] self.site_ratios = list(phase.sublattices) active_species = unpack_components(dbe, comps) for idx, sublattice in enumerate(phase.constituents): subl_comps = set(sublattice).intersection(active_species) self.components |= subl_comps # Support for variable site ratios in ionic liquid model if phase.model_hints.get('ionic_liquid_2SL', False): if idx == 0: subl_idx = 1 elif idx == 1: subl_idx = 0 else: raise ValueError('Two-sublattice ionic liquid specified with more than two sublattices') self.site_ratios[subl_idx] = Add(*[v.SiteFraction(self.phase_name, idx, spec) * abs(spec.charge) for spec in subl_comps]) if phase.model_hints.get('ionic_liquid_2SL', False): # Special treatment of "neutral" vacancies in 2SL ionic liquid # These are treated as having variable valence for idx, sublattice in enumerate(phase.constituents): subl_comps = set(sublattice).intersection(active_species) if v.Species('VA') in subl_comps: if idx == 0: subl_idx = 1 elif idx == 1: subl_idx = 0 else: raise ValueError('Two-sublattice ionic liquid specified with more than two sublattices') self.site_ratios[subl_idx] += self.site_ratios[idx] * v.SiteFraction(self.phase_name, idx, v.Species('VA')) self.site_ratios = tuple(self.site_ratios) # Verify that this phase is still possible to build is_pure_VA = set() for sublattice in phase.constituents: sublattice_comps = set(sublattice).intersection(self.components) if len(sublattice_comps) == 0: # None of the components in a sublattice are active # We cannot build a model of this phase raise DofError( '{0}: Sublattice {1} of {2} has no components in {3}' \ .format(self.phase_name, sublattice, phase.constituents, self.components)) is_pure_VA.add(sum(set(map(lambda s : getattr(s, 'number_of_atoms'),sublattice_comps)))) self.constituents.append(sublattice_comps) if sum(is_pure_VA) == 0: #The only possible component in a sublattice is vacancy #We cannot build a model of this phase raise DofError( '{0}: Sublattices of {1} contains only VA (VACUUM) constituents' \ .format(self.phase_name, phase.constituents)) self.components = sorted(self.components) desired_active_pure_elements = [list(x.constituents.keys()) for x in self.components] desired_active_pure_elements = [el.upper() for constituents in desired_active_pure_elements for el in constituents] self.pure_elements = sorted(set(desired_active_pure_elements)) self.nonvacant_elements = [x for x in self.pure_elements if x != 'VA'] # Convert string symbol names to sympy Symbol objects # This makes xreplace work with the symbols dict symbols = {Symbol(s): val for s, val in dbe.symbols.items()} if parameters is not None: self._parameters_arg = parameters if isinstance(parameters, dict): symbols.update([(wrap_symbol(s), val) for s, val in parameters.items()]) else: # Lists of symbols that should remain symbolic for s in parameters: symbols.pop(wrap_symbol(s)) else: self._parameters_arg = None self._symbols = {wrap_symbol(key): value for key, value in symbols.items()} self.models = OrderedDict() self.build_phase(dbe) for name, value in self.models.items(): self.models[name] = self.symbol_replace(value, symbols) self.site_fractions = sorted([x for x in self.variables if isinstance(x, v.SiteFraction)], key=str) self.state_variables = sorted([x for x in self.variables if not isinstance(x, v.SiteFraction)], key=str)
[docs] @staticmethod def symbol_replace(obj, symbols): """ Substitute values of symbols into 'obj'. Parameters ---------- obj : SymPy object symbols : dict mapping sympy.Symbol to SymPy object Returns ------- SymPy object """ try: # Need to do more substitutions to catch symbols that are functions # of other symbols for iteration in range(_MAX_PARAM_NESTING): obj = obj.xreplace(symbols) undefs = [x for x in obj.free_symbols if not isinstance(x, v.StateVariable)] if len(undefs) == 0: break except AttributeError: # Can't use xreplace on a float pass return obj
def __eq__(self, other): if self is other: return True elif type(self) != type(other): return False else: return self.__dict__ == other.__dict__ def __ne__(self, other): return not self.__eq__(other) def __hash__(self): return hash(repr(self))
[docs] def moles(self, species): "Number of moles of species or elements." species = v.Species(species) is_pure_element = (len(species.constituents.keys()) == 1 and list(species.constituents.keys())[0] == result = S.Zero normalization = S.Zero if is_pure_element: element = list(species.constituents.keys())[0] for idx, sublattice in enumerate(self.constituents): active = set(sublattice).intersection(self.components) result += self.site_ratios[idx] * \ sum(int(spec.number_of_atoms > 0) * spec.constituents.get(element, 0) * v.SiteFraction(self.phase_name, idx, spec) for spec in active) normalization += self.site_ratios[idx] * \ sum(spec.number_of_atoms * v.SiteFraction(self.phase_name, idx, spec) for spec in active) else: for idx, sublattice in enumerate(self.constituents): active = set(sublattice).intersection({species}) if len(active) == 0: continue result += self.site_ratios[idx] * sum(v.SiteFraction(self.phase_name, idx, spec) for spec in active) normalization += self.site_ratios[idx] * \ sum(int(spec.number_of_atoms > 0) * v.SiteFraction(self.phase_name, idx, spec) for spec in active) return result / normalization
@property def ast(self): "Return the full abstract syntax tree of the model." return Add(*list(self.models.values())) @property def variables(self): "Return state variables in the model." return sorted([x for x in self.ast.free_symbols if isinstance(x, v.StateVariable)], key=str) @property def degree_of_ordering(self): result = S.Zero site_ratio_normalization = S.Zero # Calculate normalization factor for idx, sublattice in enumerate(self.constituents): active = set(sublattice).intersection(self.components) subl_content = sum(int(spec.number_of_atoms > 0) * v.SiteFraction(self.phase_name, idx, spec) for spec in active) site_ratio_normalization += self.site_ratios[idx] * subl_content site_ratios = [c/site_ratio_normalization for c in self.site_ratios] for comp in self.components: if comp.number_of_atoms == 0: continue comp_result = S.Zero for idx, sublattice in enumerate(self.constituents): active = set(sublattice).intersection(set(self.components)) if comp in active: comp_result += site_ratios[idx] * Abs(v.SiteFraction(self.phase_name, idx, comp) - self.moles(comp)) / self.moles(comp) result += comp_result return result / sum(int(spec.number_of_atoms > 0) for spec in self.components) DOO = degree_of_ordering # Can be defined as a list of pre-computed first derivatives gradient = None # Note: In order-disorder phases, TC will always be the *disordered* value of TC curie_temperature = TC = S.Zero beta = BMAG = S.Zero neel_temperature = NT = S.Zero #pylint: disable=C0103 # These are standard abbreviations from Thermo-Calc for these quantities energy = GM = property(lambda self: self.ast) entropy = SM = property(lambda self: -self.GM.diff(v.T)) enthalpy = HM = property(lambda self: self.GM - v.T*self.GM.diff(v.T)) heat_capacity = CPM = property(lambda self: -v.T*self.GM.diff(v.T, v.T)) #pylint: enable=C0103 mixing_energy = GM_MIX = property(lambda self: self.GM - self.endmember_reference_model.GM) mixing_enthalpy = HM_MIX = property(lambda self: self.GM_MIX - v.T*self.GM_MIX.diff(v.T)) mixing_entropy = SM_MIX = property(lambda self: -self.GM_MIX.diff(v.T)) mixing_heat_capacity = CPM_MIX = property(lambda self: -v.T*self.GM_MIX.diff(v.T, v.T)) @property def endmember_reference_model(self): """ Return a Model containing only energy contributions from endmembers. Returns ------- Model Notes ----- The endmember_reference_model is used for ``_MIX`` properties of Model objects. It is defined such that subtracting it from the model will set the energy of the endmembers to zero. The endmember_reference_model AST can be modified in the same way as any Model. Partitioned models have energetic contributions from the ordered compound energies/interactions and the disordered compound energies/interactions. The endmembers to choose as the reference is ambiguous. If the current model has an ordered energy as part of a partitioned model, then the model energy contributions are set to ``nan``. The endmember reference model is built lazily and stored for later re-use because it needs to copy the Database and instantiate a new Model. """ if self._endmember_reference_model is None: endmember_only_dbe = copy.deepcopy(self._dbe) endmember_only_dbe._parameters.remove(where('constituent_array').test(self._interaction_test)) mod_endmember_only = self.__class__(endmember_only_dbe, self.components, self.phase_name, parameters=self._parameters_arg) # Ideal mixing contributions are always generated, so we need to set the # contribution of the endmember reference model to zero to preserve ideal # mixing in this model. mod_endmember_only.models['idmix'] = 0 if self.models.get('ord', S.Zero) != S.Zero: warnings.warn( f"{self.phase_name} is a partitioned model with an ordering energy " "contribution. The choice of endmembers for the endmember " "reference model used by `_MIX` properties is ambiguous for " "partitioned models. The `Model.set_reference_state` method is a " "better choice for computing mixing energy. See " " " "for an example." ) for k in mod_endmember_only.models.keys(): mod_endmember_only.models[k] = nan self._endmember_reference_model = mod_endmember_only return self._endmember_reference_model
[docs] def get_internal_constraints(self): constraints = [] for idx, sublattice in enumerate(self.constituents): constraints.append(sum(v.SiteFraction(self.phase_name, idx, spec) for spec in sublattice) - 1) return constraints
[docs] def get_multiphase_constraints(self, conds): fixed_chempots = [cond for cond in conds.keys() if isinstance(cond, v.ChemicalPotential)] multiphase_constraints = [] for statevar in sorted(conds.keys(), key=str): if not is_multiphase_constraint(statevar): continue if isinstance(statevar, v.MoleFraction): multiphase_constraints.append(Symbol('NP') * self.moles(statevar.species)) elif statevar == v.N: multiphase_constraints.append(Symbol('NP') * (sum(self.moles(spec) for spec in self.nonvacant_elements))) elif statevar in [v.T, v.P]: return multiphase_constraints.append(S.Zero) else: raise NotImplementedError return multiphase_constraints
[docs] def build_phase(self, dbe): """ Generate the symbolic form of all the contributions to this phase. Parameters ---------- dbe : Database """ contrib_vals = list(OrderedDict(self.__class__.contributions).values()) if 'atomic_ordering_energy' in contrib_vals: if contrib_vals.index('atomic_ordering_energy') != (len(contrib_vals) - 1): # Check for a common mistake in custom models # Users that need to override this behavior should override build_phase raise ValueError('\'atomic_ordering_energy\' must be the final contribution') self.models.clear() for key, value in self.__class__.contributions: self.models[key] = S(getattr(self, value)(dbe))
def _array_validity(self, constituent_array): """ Return True if the constituent_array contains only active species of the current Model instance. """ if len(constituent_array) != len(self.constituents): # Allow an exception for the ionic liquid model, where neutral # species can be specified in the anion sublattice without any # species in the cation sublattice. ionic_liquid_2SL = self._dbe.phases[self.phase_name].model_hints.get('ionic_liquid_2SL', False) if ionic_liquid_2SL and len(constituent_array) == 1: param_sublattice = constituent_array[0] model_anion_sublattice = self.constituents[1] if (set(param_sublattice).issubset(model_anion_sublattice) or (param_sublattice[0] == v.Species('*'))): return True return False for param_sublattice, model_sublattice in zip(constituent_array, self.constituents): if not (set(param_sublattice).issubset(model_sublattice) or (param_sublattice[0] == v.Species('*'))): return False return True def _purity_test(self, constituent_array): """ Return True if the constituent_array is valid and has exactly one species in every sublattice. """ if not self._array_validity(constituent_array): return False return not any(len(sublattice) != 1 for sublattice in constituent_array) def _interaction_test(self, constituent_array): """ Return True if the constituent_array is valid and has more than one species in at least one sublattice. """ if not self._array_validity(constituent_array): return False return any([len(sublattice) > 1 for sublattice in constituent_array]) @property def _site_ratio_normalization(self): """ Calculates the normalization factor based on the number of sites in each sublattice. """ site_ratio_normalization = S.Zero # Calculate normalization factor for idx, sublattice in enumerate(self.constituents): active = set(sublattice).intersection(self.components) subl_content = sum(spec.number_of_atoms * v.SiteFraction(self.phase_name, idx, spec) for spec in active) site_ratio_normalization += self.site_ratios[idx] * subl_content return site_ratio_normalization @staticmethod def _Muggianu_correction_dict(comps): #pylint: disable=C0103 """ Replace y_i -> y_i + (1 - sum(y involved in parameter)) / m, where m is the arity of the interaction parameter. Returns a dict converting the list of Symbols (comps) to this. m is assumed equal to the length of comps. When incorporating binary, ternary or n-ary interaction parameters into systems with more than n components, the sum of site fractions involved in the interaction parameter may no longer be unity. This breaks the symmetry of the parameter. The solution suggested by Muggianu, 1975, is to renormalize the site fractions by replacing them with a term that will sum to unity even in higher-order systems. There are other solutions that involve retaining the asymmetry for physical reasons, but this solution works well for components that are physically similar. This procedure is based on an analysis by Hillert, 1980, published in the Calphad journal. """ arity = len(comps) return_dict = {} correction_term = (S.One - Add(*comps)) / arity for comp in comps: return_dict[comp] = comp + correction_term return return_dict
[docs] def redlich_kister_sum(self, phase, param_search, param_query): """ Construct parameter in Redlich-Kister polynomial basis, using the Muggianu ternary parameter extension. """ rk_terms = [] # search for desired parameters params = param_search(param_query) for param in params: # iterate over every sublattice mixing_term = S.One for subl_index, comps in enumerate(param['constituent_array']): comp_symbols = None # convert strings to symbols if comps[0] == v.Species('*'): # Handle wildcards in constituent array comp_symbols = \ [ v.SiteFraction(, subl_index, comp) for comp in sorted(set(phase.constituents[subl_index])\ .intersection(self.components)) ] mixing_term *= Add(*comp_symbols) else: if ( phase.model_hints.get('ionic_liquid_2SL', False) and # This is an ionic 2SL len(param['constituent_array']) == 1 and # There's only one sublattice all(const.charge == 0 for const in param['constituent_array'][0]) # All constituents are neutral ): # The constituent array is all neutral anion species in what would be the # second sublattice. TDB syntax allows for specifying neutral species with # one sublattice model. Set the sublattice index to 1 for the purpose of # site fractions. subl_index = 1 comp_symbols = \ [ v.SiteFraction(, subl_index, comp) for comp in comps ] if phase.model_hints.get('ionic_liquid_2SL', False): # This is an ionic 2SL # We need to special case sorting for this model, because the constituents # should not be alphabetically sorted. The model should be (C)(A, Va, B) # for cations (C), anions (A), vacancies (Va) and neutrals (B). Thus the # second sublattice should be sorted by species with charge, then by # vacancies, if present, then by neutrals. Hint: in Thermo-Calc, using # `set-start-constitution` for a phase will prompt you to enter site # fractions for species in the order they are sorted internally within # Thermo-Calc. This can be used to verify sorting behavior. # Assume that the constituent array is already in sorted order # alphabetically, so we need to rearrange the species first by charged # species, then VA, then netural species. Since the cation sublattice # should only have charged species by definition, this is equivalent to # a no-op for the first sublattice. charged_symbols = [sitefrac for sitefrac in comp_symbols if sitefrac.species.charge != 0 and sitefrac.species.number_of_atoms > 0] va_symbols = [sitefrac for sitefrac in comp_symbols if sitefrac.species == v.Species('VA')] neutral_symbols = [sitefrac for sitefrac in comp_symbols if sitefrac.species.charge == 0 and sitefrac.species.number_of_atoms > 0] comp_symbols = charged_symbols + va_symbols + neutral_symbols mixing_term *= Mul(*comp_symbols) # is this a higher-order interaction parameter? if len(comps) == 2 and param['parameter_order'] > 0: # interacting sublattice, add the interaction polynomial mixing_term *= Pow(comp_symbols[0] - \ comp_symbols[1], param['parameter_order']) if len(comps) == 3: # 'parameter_order' is an index to a variable when # we are in the ternary interaction parameter case # NOTE: The commercial software packages seem to have # a "feature" where, if only the zeroth # parameter_order term of a ternary parameter is specified, # the other two terms are automatically generated in order # to make the parameter symmetric. # In other words, specifying only this parameter: # PARAMETER G(FCC_A1,AL,CR,NI;0) 298.15 +30300; 6000 N ! # Actually implies: # PARAMETER G(FCC_A1,AL,CR,NI;0) 298.15 +30300; 6000 N ! # PARAMETER G(FCC_A1,AL,CR,NI;1) 298.15 +30300; 6000 N ! # PARAMETER G(FCC_A1,AL,CR,NI;2) 298.15 +30300; 6000 N ! # # If either 1 or 2 is specified, no implicit parameters are # generated. # We need to handle this case. if param['parameter_order'] == 0: # are _any_ of the other parameter_orders specified? ternary_param_query = ( (where('phase_name') == param['phase_name']) & \ (where('parameter_type') == \ param['parameter_type']) & \ (where('constituent_array') == \ param['constituent_array']) ) other_tern_params = param_search(ternary_param_query) if len(other_tern_params) == 1 and \ other_tern_params[0] == param: # only the current parameter is specified # We need to generate the other two parameters. order_one = copy.deepcopy(param) order_one['parameter_order'] = 1 order_two = copy.deepcopy(param) order_two['parameter_order'] = 2 # Add these parameters to our iteration. params.extend((order_one, order_two)) # Include variable indicated by parameter order index # Perform Muggianu adjustment to site fractions mixing_term *= comp_symbols[param['parameter_order']].subs( self._Muggianu_correction_dict(comp_symbols), simultaneous=True) if phase.model_hints.get('ionic_liquid_2SL', False): # Special normalization rules for parameters apply under this model # If there are no anions present in the anion sublattice (only VA and neutral # species), then the energy has an additional Q*y(VA) term anions_present = any([m.species.charge < 0 for m in mixing_term.free_symbols]) if not anions_present: pair_rule = {} # Cation site fractions must always appear with vacancy site fractions va_subls = [(v.Species('VA') in phase.constituents[idx]) for idx in range(len(phase.constituents))] # The last index that contains a vacancy va_subl_idx = (len(phase.constituents) - 1) - va_subls[::-1].index(True) va_present = any((v.Species('VA') in c) for c in param['constituent_array']) if va_present and (max(len(c) for c in param['constituent_array']) == 1): # No need to apply pair rule for VA-containing endmember pass elif va_subl_idx > -1: for sym in mixing_term.free_symbols: if sym.species.charge > 0: pair_rule[sym] = sym * v.SiteFraction(sym.phase_name, va_subl_idx, v.Species('VA')) mixing_term = mixing_term.xreplace(pair_rule) # This parameter is normalized differently due to the variable charge valence of vacancies mixing_term *= self.site_ratios[va_subl_idx] param_val = param['parameter'] if isinstance(param_val, Piecewise): # Eliminate redundant Piecewise and extrapolate beyond temperature limits filtered_args = [i for i in param_val.args if not ((i.cond == S.true) and (i.expr == S.Zero))] if len(filtered_args) == 1: param_val = filtered_args[0].expr rk_terms.append(mixing_term * param_val) return Add(*rk_terms)
[docs] def reference_energy(self, dbe): """ Returns the weighted average of the endmember energies in symbolic form. """ pure_param_query = ( (where('phase_name') == self.phase_name) & \ (where('parameter_order') == 0) & \ (where('parameter_type') == "G") & \ (where('constituent_array').test(self._purity_test)) ) phase = dbe.phases[self.phase_name] param_search = pure_energy_term = self.redlich_kister_sum(phase, param_search, pure_param_query) return pure_energy_term / self._site_ratio_normalization
[docs] def ideal_mixing_energy(self, dbe): #pylint: disable=W0613 """ Returns the ideal mixing energy in symbolic form. """ phase = dbe.phases[self.phase_name] # Normalize site ratios site_ratio_normalization = self._site_ratio_normalization site_ratios = self.site_ratios site_ratios = [c/site_ratio_normalization for c in site_ratios] ideal_mixing_term = S.Zero sitefrac_limit = Float(MIN_SITE_FRACTION/10.) for subl_index, sublattice in enumerate(phase.constituents): active_comps = set(sublattice).intersection(self.components) ratio = site_ratios[subl_index] for comp in active_comps: sitefrac = \ v.SiteFraction(, subl_index, comp) # We lose some precision here, but this makes the limit behave nicely # We're okay until fractions of about 1e-12 (platform-dependent) mixing_term = Piecewise((sitefrac*log(sitefrac), StrictGreaterThan(sitefrac, sitefrac_limit, evaluate=False)), (0, True), evaluate=False) ideal_mixing_term += (mixing_term*ratio) ideal_mixing_term *= (v.R * v.T) return ideal_mixing_term
[docs] def excess_mixing_energy(self, dbe): """ Build the binary, ternary and higher order interaction term Here we use Redlich-Kister polynomial basis by default Here we use the Muggianu ternary extension by default Replace y_i -> y_i + (1 - sum(y involved in parameter)) / m, where m is the arity of the interaction parameter """ phase = dbe.phases[self.phase_name] param_search = param_query = ( (where('phase_name') == self.phase_name) & \ ((where('parameter_type') == 'G') | (where('parameter_type') == 'L')) & \ (where('constituent_array').test(self._interaction_test)) ) excess_term = self.redlich_kister_sum(phase, param_search, param_query) return excess_term / self._site_ratio_normalization
[docs] def magnetic_energy(self, dbe): #pylint: disable=C0103, R0914 """ Return the energy from magnetic ordering in symbolic form. The implemented model is the Inden-Hillert-Jarl formulation. The approach follows from the background of W. Xiong et al, Calphad, 2012. """ phase = dbe.phases[self.phase_name] param_search = self.TC = self.curie_temperature = S.Zero self.BMAG = self.beta = S.Zero if 'ihj_magnetic_structure_factor' not in phase.model_hints: return S.Zero if 'ihj_magnetic_afm_factor' not in phase.model_hints: return S.Zero site_ratio_normalization = self._site_ratio_normalization # define basic variables afm_factor = phase.model_hints['ihj_magnetic_afm_factor'] if afm_factor == 0: # Apply improved magnetic model which does not use AFM / Weiss factor return self.xiong_magnetic_energy(dbe) bm_param_query = ( (where('phase_name') == & \ (where('parameter_type') == 'BMAGN') & \ (where('constituent_array').test(self._array_validity)) ) tc_param_query = ( (where('phase_name') == & \ (where('parameter_type') == 'TC') & \ (where('constituent_array').test(self._array_validity)) ) mean_magnetic_moment = \ self.redlich_kister_sum(phase, param_search, bm_param_query) beta = mean_magnetic_moment / Piecewise( (afm_factor, mean_magnetic_moment <= 0), (1., True), evaluate=False ) self.BMAG = self.beta = beta.subs(self._symbols) curie_temp = \ self.redlich_kister_sum(phase, param_search, tc_param_query) tc = curie_temp / Piecewise( (afm_factor, curie_temp <= 0), (1., True), evaluate=False ) self.TC = self.curie_temperature = tc.subs(self._symbols) # Used to prevent singularity tau_positive_tc = v.T / (curie_temp + 1e-9) tau_negative_tc = v.T / ((curie_temp/afm_factor) + 1e-9) # define model parameters p = phase.model_hints['ihj_magnetic_structure_factor'] A = 518/1125 + (11692/15975)*(1/p - 1) # factor when tau < 1 and tc < 0 sub_tau_neg_tc = 1 - (1/A) * ((79/(140*p))*(tau_negative_tc**(-1)) + (474/497)*(1/p - 1) \ * ((tau_negative_tc**3)/6 + (tau_negative_tc**9)/135 + (tau_negative_tc**15)/600) ) # factor when tau < 1 and tc > 0 sub_tau_pos_tc = 1 - (1/A) * ((79/(140*p))*(tau_positive_tc**(-1)) + (474/497)*(1/p - 1) \ * ((tau_positive_tc**3)/6 + (tau_positive_tc**9)/135 + (tau_positive_tc**15)/600) ) # factor when tau >= 1 and tc > 0 super_tau_pos_tc = -(1/A) * ((tau_positive_tc**-5)/10 + (tau_positive_tc**-15)/315 + (tau_positive_tc**-25)/1500) # factor when tau >= 1 and tc < 0 super_tau_neg_tc = -(1/A) * ((tau_negative_tc**-5)/10 + (tau_negative_tc**-15)/315 + (tau_negative_tc**-25)/1500) # This is an optimization to reduce the complexity of the compile-time expression expr_cond_pairs = [(sub_tau_neg_tc, curie_temp/afm_factor > v.T), (sub_tau_pos_tc, curie_temp > v.T), (super_tau_pos_tc, And(curie_temp < v.T, curie_temp > 0)), (super_tau_neg_tc, And(curie_temp/afm_factor < v.T, curie_temp < 0)), (0, True) ] g_term = Piecewise(*expr_cond_pairs, evaluate=False) return v.R * v.T * log(beta+1) * \ g_term / site_ratio_normalization
[docs] def xiong_magnetic_energy(self, dbe): """ Return the energy from magnetic ordering in symbolic form. The approach follows W. Xiong et al, Calphad, 2012. """ phase = dbe.phases[self.phase_name] param_search = self.TC = self.curie_temperature = S.Zero if 'ihj_magnetic_structure_factor' not in phase.model_hints: return S.Zero if 'ihj_magnetic_afm_factor' not in phase.model_hints: return S.Zero site_ratio_normalization = self._site_ratio_normalization # define basic variables afm_factor = phase.model_hints['ihj_magnetic_afm_factor'] if afm_factor != 0: raise ValueError('Xiong model called with nonzero AFM / Weiss factor') nt_param_query = ( (where('phase_name') == & \ (where('parameter_type') == 'NT') & \ (where('constituent_array').test(self._array_validity)) ) bm_param_query = ( (where('phase_name') == & \ (where('parameter_type') == 'BMAGN') & \ (where('constituent_array').test(self._array_validity)) ) tc_param_query = ( (where('phase_name') == & \ (where('parameter_type') == 'TC') & \ (where('constituent_array').test(self._array_validity)) ) mean_magnetic_moment = \ self.redlich_kister_sum(phase, param_search, bm_param_query) beta = mean_magnetic_moment curie_temp = \ self.redlich_kister_sum(phase, param_search, tc_param_query) neel_temp = \ self.redlich_kister_sum(phase, param_search, nt_param_query) self.TC = self.curie_temperature = curie_temp.subs(self._symbols) self.NT = self.neel_temperature = neel_temp.subs(self._symbols) self.BMAG = self.beta = beta.subs(self._symbols) tau_curie = v.T / curie_temp tau_curie = tau_curie.xreplace({zoo: 1.0e10}) tau_neel = v.T / neel_temp tau_neel = tau_neel.xreplace({zoo: 1.0e10}) # define model parameters p = phase.model_hints['ihj_magnetic_structure_factor'] D = 0.33471979 + 0.49649686*(1/p - 1) sub_tau_curie = 1 - (1/D) * ((0.38438376/p)*(tau_curie**(-1)) + 0.63570895*(1/p - 1) \ * ((tau_curie**3)/6 + (tau_curie**9)/135 + (tau_curie**15)/600) + (tau_curie**21)/1617 ) sub_tau_neel = 1 - (1/D) * ((0.38438376/p)*(tau_neel**(-1)) + 0.63570895*(1/p - 1) \ * ((tau_neel**3)/6 + (tau_neel**9)/135 + (tau_neel**15)/600) + (tau_neel**21)/1617 ) super_tau_curie = -(1/D) * ((tau_curie**-7)/21 + (tau_curie**-21)/630 + (tau_curie**-35)/2975 + (tau_curie**-49)/8232) super_tau_neel = -(1/D) * ((tau_neel**-7)/21 + (tau_neel**-21)/630 + (tau_neel**-35)/2975 + (tau_neel**-49)/8232) expr_cond_pairs_curie = [(0, tau_curie <= 0), (super_tau_curie, tau_curie > 1), (sub_tau_curie, True) ] expr_cond_pairs_neel = [(0, tau_neel <= 0), (super_tau_neel, tau_neel > 1), (sub_tau_neel, True) ] g_term = Piecewise(*expr_cond_pairs_curie, evaluate=False) + Piecewise(*expr_cond_pairs_neel, evaluate=False) return v.R * v.T * log(beta+1) * \ g_term / site_ratio_normalization
[docs] def twostate_energy(self, dbe): """ Return the energy from liquid-amorphous two-state model. """ phase = dbe.phases[self.phase_name] param_search = site_ratio_normalization = self._site_ratio_normalization gd_param_query = ( (where('phase_name') == & \ (where('parameter_type') == 'GD') & \ (where('constituent_array').test(self._array_validity)) ) gd = self.redlich_kister_sum(phase, param_search, gd_param_query) if gd == S.Zero: return S.Zero return -v.R * v.T * log(1 + exp(-gd / (v.R * v.T))) / site_ratio_normalization
[docs] def einstein_energy(self, dbe): """ Return the energy based on the Einstein model. Note that THETA parameters are actually LN(THETA). All Redlich-Kister summation is done in log-space, then exp() is called on the result. """ phase = dbe.phases[self.phase_name] param_search = theta_param_query = ( (where('phase_name') == & \ (where('parameter_type') == 'THETA') & \ (where('constituent_array').test(self._array_validity)) ) lntheta = self.redlich_kister_sum(phase, param_search, theta_param_query) theta = exp(lntheta) if lntheta != 0: result = 1.5*v.R*theta + 3*v.R*v.T*log(1-exp(-theta/v.T)) else: result = 0 return result / self._site_ratio_normalization
@staticmethod def _quasi_mole_fraction(species_name, phase_name, constituent_array, site_ratios, substitutional_sublattice_idxs, ): """ Return an abstract syntax tree of the quasi mole fraction of the given species as a function of this phases's constituent site fractions. These mole fractions are "quasi" mole fractions because 1. Vacancies are treated as regular species - they have mole fractions defined and the site fraction of vacancies are not used to normalize the mole fractions of the real constituents by the 1 - y_{VA} factor. 2. The mole fractions are only computed over the sublattices that participate in the ordering/disordering. Species in non-ordering ("interstitial") sublattices do not contribute to the mole fractions that replace the site fractions. These constraints ensures that the ordering energy goes to zero when the substitutional sublattice is disordered, regardless of the occupancy of the interstitial sublattice. """ # Normalize site ratios site_ratio_normalization = 0 numerator = S.Zero for idx, sublattice in enumerate(constituent_array): # only count species from substitutional sublattices if idx not in substitutional_sublattice_idxs: continue if species_name in list(sublattice): site_ratio_normalization += site_ratios[idx] numerator += site_ratios[idx] * \ v.SiteFraction(phase_name, idx, species_name) if site_ratio_normalization == 0 and == 'VA': return 1 if site_ratio_normalization == 0: raise ValueError( f'Couldn\'t find {species_name} in a substitutional sublattice ' f'(indices: {substitutional_sublattice_idxs}) ' f'of the constituents {constituent_array}' ) return numerator / site_ratio_normalization @staticmethod def _partitioned_expr(disord_expr, ord_expr, disordered_mole_fraction_dict, ordered_mole_fraction_dict): """Return the expression from adding the disordered part and ordering part Given expressions E^{dis}(y^{dis}_i) and E^{ord}(y^{ord}_i), return: E^{dis}(x^{ord}_i) + (E^{ord}(y^{ord}_i) - E^{ord}(y^{ord}_i = x^{ord}_i)) where: * y^{dis}_i are the site fractions of the disordered phase * y^{ord}_i are the site fractions of the ordered phase * x^{ord}_i are the quasi mole fractions of the ordered phase (in terms of the ordered phase site fractions) """ disord_expr = disord_expr.xreplace(disordered_mole_fraction_dict) ordering_expr = ord_expr - ord_expr.xreplace(ordered_mole_fraction_dict) return disord_expr + ordering_expr
[docs] def atomic_ordering_energy(self, dbe): """ Return the atomic ordering contribution in symbolic form. If the current phase is anything other than the ordered phase in a paritioned order/disorder Gibbs energy model, this method will return zero. If the current phase is the ordered phase, ordering energy is computed by equation (18) of Connetable *et al.* [1]_: :math:`\Delta G^\mathrm{ord}(y_i) = G^\mathrm{ord}(y_i) - G^\mathrm{ord}(y_i = x_i)` This method must be the last energy contribution called because it plays several roles that require all other contributions to be defined: 1. The current AST in self.models represents the ordered energy :math:`G^\mathrm{ord}(y_i)`. To compute the ordering energy, all contributions to the ordered energy must have already been counted. 2. The true energy of the phase should be the sum of the disordered phase's energy and the ordering energy. That is, :math:`G = G^\mathrm{dis} + \Delta G^\mathrm{ord}(y_i)`. This method not only computes the ordering energy, but also replaces the other model contributions by the disordered phase's energy. 3. Physical properties are partitioned in the same way as the energy. See Section 5.8.6 of Lukas, Fries and Sundman [2]_. Notes ----- .. caution:: This method overwrites the ``self.models`` dictionary with the model contributions for the disordered phase. This method assumes that the first sublattice of the disordered phase is the substitutional sublattice and all other sublattices are interstitial. In the ordered phase, all sublattices with constituents that match the disordered substitutional sublattice will be treated as disordered (with site fractions replaced by quasi mole fractions in the ordered sublattices) and the interstitial sublattices will not have any site fractions substituted. References ---------- .. [1] Connetable et al., Calphad 2008, 32 (2), 361–370. doi: 10.1016/j.calphad.2008.01.002 .. [2] Lukas, Fries, and Sundman, Computational Thermodynamics: the Calphad Method, Cambridge University Press (2007). """ phase = dbe.phases[self.phase_name] ordered_phase_name = phase.model_hints.get('ordered_phase', None) disordered_phase_name = phase.model_hints.get('disordered_phase', None) if != ordered_phase_name: return S.Zero ordered_phase = dbe.phases[ordered_phase_name] constituents = [sorted(set(c).intersection(self.components)) for c in ordered_phase.constituents] disordered_phase = dbe.phases[disordered_phase_name] disordered_model = self.__class__(dbe, sorted(self.components), disordered_phase_name) # Get substitutional sublattice indices (for the ordered phase) and # validate that the number of interstitial sublattices is consistent # with the disordered phase. # Assumes first sublattice of the disordered phase is the sublattice # that can be come ordered: disordered_subl_constituents = disordered_phase.constituents[0] ordered_constituents = ordered_phase.constituents substitutional_sublattice_idxs = [] for idx, subl_constituents in enumerate(ordered_constituents): # Assumes that the ordered phase sublattice describes the ordering # if it has exactly the same constituents. Could be a source of # false positives if any interstitial sublattices have the same # constituents as the disordered sublattice, but there's not an # explicit way to specify which sublattices are ordering. We try to # compensate for this assumption by validating (next). if len(disordered_subl_constituents.symmetric_difference(subl_constituents)) == 0: substitutional_sublattice_idxs.append(idx) # validate num_substitutional_sublattice_idxs = len(substitutional_sublattice_idxs) num_ordered_interstitial_subls = len(ordered_phase.sublattices) - num_substitutional_sublattice_idxs num_disordered_interstitial_subls = len(disordered_phase.sublattices) - 1 if num_ordered_interstitial_subls != num_disordered_interstitial_subls: raise ValueError( f'Number of interstitial sublattices for the disordered phase ' f'({num_disordered_interstitial_subls}) and the ordered phase ' f'({num_ordered_interstitial_subls}) do not match. Got ' f'substitutional sublattice indices of {substitutional_sublattice_idxs}.' ) # We also validate that no physical properties have ordered # contributions because the underlying physical property needs to # paritioned and substituted for the physical property in the disordered # expression. This can be safely removed when partitioned # physical properties are correctly substituted into the disordered # energy. for contrib, value in self.models.items(): # To handle ordering in user-defined subclasses, we assume that all properties # that are not reference, ideal, or excess are physical contributions. if contrib in ('ref', 'idmix', 'xsmix'): continue if value != S.Zero: warnings.warn( f"The order-disorder model for \"{self.phase_name}\" has a contribution from " f"the physical property model `{dict(self.contributions)[contrib]}`. " f"Partitioned physical properties are not correctly substituted into the " f"disordered part of the energy. THE GIBBS ENERGY CALCULATED FOR THIS PHASE " f"MAY BE INCORRECT. Please see the discussion in " f" for more details." ) # Save all of the ordered energy contributions # Needs to extract a copy of self.models.values because the values will # be updated to the disordered energy contributions later ordered_energy = Add(*list(self.models.values())) # Compute the molefraction_dict, which will map ordered phase site # fractions to the quasi mole fractions representing the disordered state molefraction_dict = {} ordered_sitefracs = [x for x in ordered_energy.free_symbols if isinstance(x, v.SiteFraction)] for sitefrac in ordered_sitefracs: if sitefrac.sublattice_index in substitutional_sublattice_idxs: molefraction_dict[sitefrac] = \ self._quasi_mole_fraction(sitefrac.species, ordered_phase_name, constituents, ordered_phase.sublattices, substitutional_sublattice_idxs, ) # Compute the variable_rename_dict, which will map disordered phase site # fractions to the quasi mole fractions representing the disordered state variable_rename_dict = {} disordered_sitefracs = [x for x in if isinstance(x, v.SiteFraction)] for atom in disordered_sitefracs: if atom.sublattice_index == 0: # only the first sublattice is substitutional variable_rename_dict[atom] = \ self._quasi_mole_fraction(atom.species, ordered_phase_name, constituents, ordered_phase.sublattices, substitutional_sublattice_idxs, ) else: shifted_subl_index = atom.sublattice_index + num_substitutional_sublattice_idxs - 1 variable_rename_dict[atom] = \ v.SiteFraction(ordered_phase_name, shifted_subl_index, atom.species) # 1: Compute the ordering energy # Step 2 will put the disordered parts into the correct model # contributions. There's no technical reason for doing it this way # compared to setting the AST to the _partitioned_expr for the total # energy - this is more for bookkeeping of the model contributions. ordering_energy = self._partitioned_expr(S.Zero, ordered_energy, {}, molefraction_dict) # 2: Replace the ordered energy contributions with the disordered contributions self.models.clear() for name, value in disordered_model.models.items(): self.models[name] = value.xreplace(variable_rename_dict) # 3: Handle physical properties, these also are contributed to by the # disordered phase *and* an "ordering" contribution. For now, we only # handle the magnetic parameters, since the other parameters are not # stored as properties (e.g. Einstein THETA). # TODO: Note that these do not affect the Gibbs energy expression! # The disordered model's energetic contribution from physical # properties needs to use the partitioned property in the disordered # energy contribution. This is not possible at the time of writing. self.TC = self.curie_temperature = self._partitioned_expr(disordered_model.TC, self.TC, variable_rename_dict, molefraction_dict) self.BMAG = self.beta = self._partitioned_expr(disordered_model.BMAG, self.BMAG, variable_rename_dict, molefraction_dict) self.NT = self.neel_temperature = self._partitioned_expr(disordered_model.NT, self.NT, variable_rename_dict, molefraction_dict) return ordering_energy
# TODO: fix case for VA interactions: L(PHASE,A,VA:VA;0)-type parameters
[docs] def shift_reference_state(self, reference_states, dbe, contrib_mods=None, output=('GM', 'HM', 'SM', 'CPM'), fmt_str="{}R"): """ Add new attributes for calculating properties w.r.t. an arbitrary pure element reference state. Parameters ---------- reference_states : Iterable of ReferenceState Pure element ReferenceState objects. Must include all the pure elements defined in the current model. dbe : Database Database containing the relevant parameters. output : Iterable, optional Parameters to subtract the ReferenceState from, defaults to ('GM', 'HM', 'SM', 'CPM'). contrib_mods : Mapping, optional Map of {model contribution: new value}. Used to adjust the pure reference model contributions at the time this is called, since the `models` attribute of the pure element references are effectively static after calling this method. fmt_str : str, optional String that will be formatted with the `output` parameter name. Defaults to "{}R", e.g. the transformation of 'GM' -> 'GMR' """ # Error checking # We ignore the case that the ref states are overspecified (same ref states can be used in different models w/ different active pure elements) model_pure_elements = set(get_pure_elements(dbe, self.components)) refstate_pure_elements_list = get_pure_elements(dbe, [r.species for r in reference_states]) refstate_pure_elements = set(refstate_pure_elements_list) if len(refstate_pure_elements_list) != len(refstate_pure_elements): raise DofError("Multiple ReferenceState objects exist for at least one pure element: {}".format(refstate_pure_elements_list)) if not refstate_pure_elements.issuperset(model_pure_elements): raise DofError("Non-existent ReferenceState for pure components {} in {} for {}".format(model_pure_elements.difference(refstate_pure_elements), self, self.phase_name)) contrib_mods = contrib_mods or {} def _pure_element_test(constituent_array): all_comps = set() for sublattice in constituent_array: if len(sublattice) != 1: return False all_comps.add(sublattice[0].name) pure_els = all_comps.intersection(model_pure_elements) return len(pure_els) == 1 # Remove interactions from a copy of the Database, avoids any element/VA interactions. endmember_only_dbe = copy.deepcopy(dbe) endmember_only_dbe._parameters.remove(~where('constituent_array').test(_pure_element_test)) reference_dict = {out: [] for out in output} # output: terms list for ref_state in reference_states: if ref_state.species not in self.components: continue mod_pure = self.__class__(endmember_only_dbe, [ref_state.species, v.Species('VA')], ref_state.phase_name, parameters=self._parameters_arg) # apply the modifications to the Models for contrib, new_val in contrib_mods.items(): mod_pure.models[contrib] = new_val # set all the free site fractions to one, this should effectively delete any mixing terms spuriously added, e.g. idmix site_frac_subs = {sf: 1 for sf in mod_pure.ast.free_symbols if isinstance(sf, v.SiteFraction)} for mod_key, mod_val in mod_pure.models.items(): mod_pure.models[mod_key] = mod_val.subs(site_frac_subs) moles = self.moles(ref_state.species) # get the output property of interest, substitute the fixed state variables (e.g. T=298.15) and add the pure element moles weighted term to the list of terms # substitution of fixed state variables has to happen after getting the attribute in case there are any derivatives involving that state variable for out in reference_dict.keys(): mod_out = getattr(mod_pure, out).subs(ref_state.fixed_statevars) reference_dict[out].append(mod_out*moles) # set the attribute on the class for out, terms in reference_dict.items(): reference_contrib = Add(*terms) referenced_value = getattr(self, out) - reference_contrib setattr(self, fmt_str.format(out), referenced_value)
[docs]class TestModel(Model): """ Test Model object for global minimization. Equation 15.2 in: P.M. Pardalos, H.E. Romeijn (Eds.), Handbook of Global Optimization, vol. 2. Kluwer Academic Publishers, Boston/Dordrecht/London (2002) Parameters ---------- dbf : Database Ignored by TestModel but retained for API compatibility. comps : sequence Names of components to consider in the calculation. phase : str Name of phase model to build. solution : sequence, optional Float array locating the true minimum. Same length as 'comps'. If not specified, randomly generated and saved to self.solution Methods ------- None yet. Examples -------- None yet. """ def __init__(self, dbf, comps, phase, solution=None, kmax=None): self.components = set(comps) if 'VA' in self.components: raise ValueError('Vacancies are unsupported in TestModel') self.models = dict() variables = [v.SiteFraction(phase.upper(), 0, x) for x in sorted(self.components)] if solution is None: solution = np.random.dirichlet(np.ones_like(variables, dtype=np.int_)) self.solution = dict(list(zip(variables, solution))) kmax = kmax if kmax is not None else 2 scale_factor = 1e4 * len(self.components) ampl_scale = 1e3 * np.ones(kmax, dtype=np.float_) freq_scale = 10 * np.ones(kmax, dtype=np.float_) polys = Add(*[ampl_scale[i] * sin(freq_scale[i] * Add(*[Add(*[(varname - sol)**(j+1) for varname, sol in self.solution.items()]) for j in range(kmax)]))**2 for i in range(kmax)]) self.models['test'] = scale_factor * Add(*[(varname - sol)**2 for varname, sol in self.solution.items()]) + polys