Source code for pymatgen.analysis.reaction_calculator

# coding: utf-8
# Copyright (c) Pymatgen Development Team.
# Distributed under the terms of the MIT License.

This module provides classes that define a chemical reaction.

import logging
import numpy as np
import re

from monty.json import MSONable
from pymatgen.core.composition import Composition
from pymatgen.entries.computed_entries import ComputedEntry
from monty.json import MontyDecoder
from monty.fractions import gcd_float

from itertools import combinations, chain

__author__ = "Shyue Ping Ong, Anubhav Jain"
__copyright__ = "Copyright 2011, The Materials Project"
__version__ = "2.0"
__maintainer__ = "Shyue Ping Ong"
__email__ = ""
__status__ = "Production"
__date__ = "Jul 11 2012"

logger = logging.getLogger(__name__)

[docs]class BalancedReaction(MSONable): """ An object representing a complete chemical reaction. """ # Tolerance for determining if a particular component fraction is > 0. TOLERANCE = 1e-6 def __init__(self, reactants_coeffs, products_coeffs): """ Reactants and products to be specified as dict of {Composition: coeff}. Args: reactants_coeffs ({Composition: float}): Reactants as dict of {Composition: amt}. products_coeffs ({Composition: float}): Products as dict of {Composition: amt}. """ # sum reactants and products all_reactants = sum( [k * v for k, v in reactants_coeffs.items()], Composition({}) ) all_products = sum([k * v for k, v in products_coeffs.items()], Composition({})) if not all_reactants.almost_equals(all_products, rtol=0, atol=self.TOLERANCE): raise ReactionError("Reaction is unbalanced!") self._els = all_reactants.elements self.reactants_coeffs = reactants_coeffs self.products_coeffs = products_coeffs # calculate net reaction coefficients self._coeffs = [] self._els = [] self._all_comp = [] for c in set(list(reactants_coeffs.keys()) + list(products_coeffs.keys())): coeff = products_coeffs.get(c, 0) - reactants_coeffs.get(c, 0) if abs(coeff) > self.TOLERANCE: self._all_comp.append(c) self._coeffs.append(coeff)
[docs] def calculate_energy(self, energies): """ Calculates the energy of the reaction. Args: energies ({Composition: float}): Energy for each composition. E.g ., {comp1: energy1, comp2: energy2}. Returns: reaction energy as a float. """ return sum([amt * energies[c] for amt, c in zip(self._coeffs, self._all_comp)])
[docs] def normalize_to(self, comp, factor=1): """ Normalizes the reaction to one of the compositions. By default, normalizes such that the composition given has a coefficient of 1. Another factor can be specified. Args: comp (Composition): Composition to normalize to factor (float): Factor to normalize to. Defaults to 1. """ scale_factor = abs(1 / self._coeffs[self._all_comp.index(comp)] * factor) self._coeffs = [c * scale_factor for c in self._coeffs]
[docs] def normalize_to_element(self, element, factor=1): """ Normalizes the reaction to one of the elements. By default, normalizes such that the amount of the element is 1. Another factor can be specified. Args: element (Element/Specie): Element to normalize to. factor (float): Factor to normalize to. Defaults to 1. """ all_comp = self._all_comp coeffs = self._coeffs current_el_amount = (sum([all_comp[i][element] * abs(coeffs[i]) for i in range(len(all_comp))]) / 2) scale_factor = factor / current_el_amount self._coeffs = [c * scale_factor for c in coeffs]
[docs] def get_el_amount(self, element): """ Returns the amount of the element in the reaction. Args: element (Element/Specie): Element in the reaction Returns: Amount of that element in the reaction. """ return (sum([self._all_comp[i][element] * abs(self._coeffs[i]) for i in range(len(self._all_comp))]) / 2)
@property def elements(self): """ List of elements in the reaction """ return self._els[:] @property def coeffs(self): """ Final coefficients of the calculated reaction """ return self._coeffs[:] @property def all_comp(self): """ List of all compositions in the reaction. """ return self._all_comp @property def reactants(self): """ List of reactants """ return [self._all_comp[i] for i in range(len(self._all_comp)) if self._coeffs[i] < 0] @property def products(self): """ List of products """ return [self._all_comp[i] for i in range(len(self._all_comp)) if self._coeffs[i] > 0]
[docs] def get_coeff(self, comp): """ Returns coefficient for a particular composition """ return self._coeffs[self._all_comp.index(comp)]
[docs] def normalized_repr_and_factor(self): """ Normalized representation for a reaction For example, ``4 Li + 2 O -> 2Li2O`` becomes ``2 Li + O -> Li2O`` """ return self._str_from_comp(self._coeffs, self._all_comp, True)
@property def normalized_repr(self): """ A normalized representation of the reaction. All factors are converted to lowest common factors. """ return self.normalized_repr_and_factor()[0] def __eq__(self, other): if other is None: return False for comp in self._all_comp: coeff2 = other.get_coeff(comp) if comp in other._all_comp else 0 if abs(self.get_coeff(comp) - coeff2) > self.TOLERANCE: return False return True def __hash__(self): return 7 @classmethod def _str_from_formulas(cls, coeffs, formulas): reactant_str = [] product_str = [] for amt, formula in zip(coeffs, formulas): if abs(amt + 1) < cls.TOLERANCE: reactant_str.append(formula) elif abs(amt - 1) < cls.TOLERANCE: product_str.append(formula) elif amt < -cls.TOLERANCE: reactant_str.append("{:.4g} {}".format(-amt, formula)) elif amt > cls.TOLERANCE: product_str.append("{:.4g} {}".format(amt, formula)) return " + ".join(reactant_str) + " -> " + " + ".join(product_str) @classmethod def _str_from_comp(cls, coeffs, compositions, reduce=False): r_coeffs = np.zeros(len(coeffs)) r_formulas = [] for i, (amt, comp) in enumerate(zip(coeffs, compositions)): formula, factor = comp.get_reduced_formula_and_factor() r_coeffs[i] = amt * factor r_formulas.append(formula) if reduce: factor = 1 / gcd_float(np.abs(r_coeffs)) r_coeffs *= factor else: factor = 1 return cls._str_from_formulas(r_coeffs, r_formulas), factor def __str__(self): return self._str_from_comp(self._coeffs, self._all_comp)[0] __repr__ = __str__
[docs] def as_entry(self, energies): """ Returns a ComputedEntry representation of the reaction. :return: """ relevant_comp = [comp * abs(coeff) for coeff, comp in zip(self._coeffs, self._all_comp)] comp = sum(relevant_comp, Composition()) entry = ComputedEntry(0.5 * comp, self.calculate_energy(energies)) = self.__str__() return entry
[docs] def as_dict(self): """ Returns: A dictionary representation of BalancedReaction. """ return { "@module": self.__class__.__module__, "@class": self.__class__.__name__, "reactants": { str(comp): coeff for comp, coeff in self.reactants_coeffs.items() }, "products": { str(comp): coeff for comp, coeff in self.products_coeffs.items() }, }
[docs] @classmethod def from_dict(cls, d): """ Args: d (dict): from as_dict() Returns: A BalancedReaction object. """ reactants = {Composition(comp): coeff for comp, coeff in d["reactants"].items()} products = {Composition(comp): coeff for comp, coeff in d["products"].items()} return cls(reactants, products)
[docs] @staticmethod def from_string(rxn_string): """ Generates a balanced reaction from a string. The reaction must already be balanced. Args: rxn_string: The reaction string. For example, "4 Li + O2-> 2Li2O" Returns: BalancedReaction """ rct_str, prod_str = rxn_string.split("->") def get_comp_amt(comp_str): return { Composition( float( or 1) for m in re.finditer( r"([\d\.]*(?:[eE]-?[\d\.]+)?)\s*([A-Z][\w\.\(\)]*)", comp_str ) } return BalancedReaction(get_comp_amt(rct_str), get_comp_amt(prod_str))
[docs]class Reaction(BalancedReaction): """ A more flexible class representing a Reaction. The reaction amounts will be automatically balanced. Reactants and products can swap sides so that all coefficients are positive, however this class will find the solution with the minimum number of swaps and coefficients of 0. Normalizes so that the *FIRST* product (or products, if underdetermined) has a coefficient of one. """ def __init__(self, reactants, products): """ Reactants and products to be specified as list of pymatgen.core.structure.Composition. e.g., [comp1, comp2] Args: reactants ([Composition]): List of reactants. products ([Composition]): List of products. """ self._input_reactants = reactants self._input_products = products self._all_comp = reactants + products self._num_comp = len(self.all_comp) all_elems = sorted({elem for c in self._all_comp for elem in c.elements}) self._num_elems = len(all_elems) comp_matrix = np.array([[c[el] for el in all_elems] for c in self._all_comp]).T rank = np.linalg.matrix_rank(comp_matrix) diff = self._num_comp - rank num_constraints = diff if diff >= 2 else 1 self._lowest_num_errors = np.inf # an error = a component changing sides or disappearing self._coeffs = self._balance_coeffs(comp_matrix, num_constraints) self._els = all_elems def _balance_coeffs(self, comp_matrix, max_num_constraints): first_product_idx = len(self._input_reactants) # start with simplest product constraints, work towards most complex reactant constraints product_constraints = chain.from_iterable( [ combinations(range(first_product_idx, self._num_comp), n_constr) for n_constr in range(max_num_constraints, 0, -1) ] ) reactant_constraints = chain.from_iterable( [ combinations(range(0, first_product_idx), n_constr) for n_constr in range(max_num_constraints, 0, -1) ] ) best_soln = None balanced = False for constraints in chain(product_constraints, reactant_constraints): n_constr = len(constraints) comp_and_constraints = np.append( comp_matrix, np.zeros((n_constr, self._num_comp)), axis=0 ) b = np.zeros((self._num_elems + n_constr, 1)) b[-n_constr:] = 1 if min(constraints) >= first_product_idx else -1 for num, idx in enumerate(constraints): comp_and_constraints[self._num_elems + num, idx] = 1 # arbitrarily fix coeff to 1 coeffs = np.matmul(np.linalg.pinv(comp_and_constraints), b) if np.allclose(np.matmul(comp_matrix, coeffs), np.zeros((self._num_elems, 1))): balanced = True expected_signs = np.array([-1] * len(self._input_reactants) + [+1] * len(self._input_products)) num_errors = np.sum(np.multiply(expected_signs, coeffs.T) < self.TOLERANCE) if num_errors == 0: self._lowest_num_errors = 0 return np.squeeze(coeffs) elif num_errors < self._lowest_num_errors: self._lowest_num_errors = num_errors best_soln = coeffs if not balanced: raise ReactionError("Reaction cannot be balanced.") return np.squeeze(best_soln)
[docs] def copy(self): """ Returns a copy of the Reaction object. """ return Reaction(self.reactants, self.products)
[docs] def as_dict(self): """ Returns: A dictionary representation of Reaction. """ return { "@module": self.__class__.__module__, "@class": self.__class__.__name__, "reactants": [comp.as_dict() for comp in self._input_reactants], "products": [comp.as_dict() for comp in self._input_products], }
[docs] @classmethod def from_dict(cls, d): """ Args: d (dict): from as_dict() Returns: A Reaction object. """ reactants = [Composition(sym_amt) for sym_amt in d["reactants"]] products = [Composition(sym_amt) for sym_amt in d["products"]] return cls(reactants, products)
[docs]class ReactionError(Exception): """ Exception class for Reactions. Allows more information in exception messages to cover situations not covered by standard exception classes. """ def __init__(self, msg): """ Create a ReactionError. Args: msg (str): More information about the ReactionError. """ self.msg = msg def __str__(self): return self.msg
[docs]class ComputedReaction(Reaction): """ Convenience class to generate a reaction from ComputedEntry objects, with some additional attributes, such as a reaction energy based on computed energies. """ def __init__(self, reactant_entries, product_entries): """ Args: reactant_entries ([ComputedEntry]): List of reactant_entries. product_entries ([ComputedEntry]): List of product_entries. """ self._reactant_entries = reactant_entries self._product_entries = product_entries self._all_entries = reactant_entries + product_entries reactant_comp = set( [e.composition.get_reduced_composition_and_factor()[0] for e in reactant_entries]) product_comp = set( [e.composition.get_reduced_composition_and_factor()[0] for e in product_entries]) super().__init__(list(reactant_comp), list(product_comp)) @property def all_entries(self): """ Equivalent of all_comp but returns entries, in the same order as the coefficients. """ entries = [] for c in self._all_comp: for e in self._all_entries: if e.composition.reduced_formula == c.reduced_formula: entries.append(e) break return entries @property def calculated_reaction_energy(self): """ Returns (float): The calculated reaction energy. """ calc_energies = {} for entry in self._reactant_entries + self._product_entries: (comp, factor) = entry.composition.get_reduced_composition_and_factor() calc_energies[comp] = min( calc_energies.get(comp, float("inf")), / factor) return self.calculate_energy(calc_energies)
[docs] def as_dict(self): """ Returns: A dictionary representation of ComputedReaction. """ return { "@module": self.__class__.__module__, "@class": self.__class__.__name__, "reactants": [e.as_dict() for e in self._reactant_entries], "products": [e.as_dict() for e in self._product_entries], }
[docs] @classmethod def from_dict(cls, d): """ Args: d (dict): from as_dict() Returns: A ComputedReaction object. """ dec = MontyDecoder() reactants = [dec.process_decoded(e) for e in d["reactants"]] products = [dec.process_decoded(e) for e in d["products"]] return cls(reactants, products)