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import numpy as np
from ase.calculators.emt import EMT
from ase.db import connect
from ase.eos import EquationOfState
from ase.lattice.cubic import FaceCenteredCubic
def relax(input_atoms, ref_db):
atoms_string = input_atoms.get_chemical_symbols()
# Open connection to the database with reference data
db = connect(ref_db)
# Load our model structure which is just FCC
atoms = FaceCenteredCubic('X', latticeconstant=1.0)
atoms.set_chemical_symbols(atoms_string)
# Compute the average lattice constant of the metals in this individual
# and the sum of energies of the constituent metals in the fcc lattice
# we will need this for calculating the heat of formation
a = 0
ei = 0
for m in set(atoms_string):
dct = db.get(metal=m)
count = atoms_string.count(m)
a += count * dct.latticeconstant
ei += count * dct.energy_per_atom
a /= len(atoms_string)
atoms.set_cell([a, a, a], scale_atoms=True)
# Since calculations are extremely fast with EMT we can also do a volume
# relaxation
atoms.calc = EMT()
eps = 0.05
volumes = (a * np.linspace(1 - eps, 1 + eps, 9)) ** 3
energies = []
for v in volumes:
atoms.set_cell([v ** (1.0 / 3)] * 3, scale_atoms=True)
energies.append(atoms.get_potential_energy())
eos = EquationOfState(volumes, energies)
v1, ef, _B = eos.fit()
latticeconstant = v1 ** (1.0 / 3)
# Calculate the heat of formation by subtracting ef with ei
hof = (ef - ei) / len(atoms)
# Place the calculated parameters in the info dictionary of the
# input_atoms object
input_atoms.info['key_value_pairs']['hof'] = hof
# Raw score must always be set
# Use one of the following two; they are equivalent
input_atoms.info['key_value_pairs']['raw_score'] = -hof
# set_raw_score(input_atoms, -hof)
input_atoms.info['key_value_pairs']['latticeconstant'] = latticeconstant
# Setting the atoms_string directly for easier analysis
atoms_string = ''.join(input_atoms.get_chemical_symbols())
input_atoms.info['key_value_pairs']['atoms_string'] = atoms_string
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