def fit_elastic():
from ase.db import connect
atoms = bulk("Al") * (2, 2, 2)
elastic = ElasticConstants(atoms, DB_NAME)
db = connect(DB_NAME)
stresses = []
strains = []
for row in db.select('id>=25'):
stress = np.array(row["stress"])
stresses.append(stress)
strain = np.array(db.get(id=row.init_struct).data["strain"])
strains.append(strain)
from ase.spacegroup import Spacegroup
spg = Spacegroup(123)
print(spg.get_rotations())
C = elastic.get(stresses=stresses, strains=strains, spg=225, perm="zxy")
print("Bulk:")
print("Voigt: {}".format(elastic.bulk_modulus(mode="V") / GPa))
print("Reuss: {}".format(elastic.bulk_modulus(mode="R") / GPa))
print("VRH: {}".format(elastic.bulk_modulus(mode="VRH") / GPa))
print("Shear:")
print("Voigt: {}".format(elastic.shear_modulus(mode="V") / GPa))
print("Reuss: {}".format(elastic.shear_modulus(mode="R") / GPa))
print("VRH: {}".format(elastic.shear_modulus(mode="VRH") / GPa))
print("Shear: {}".format(elastic.shear_modulus(mode="VRH")))
print("Poisson: {}".format(elastic.poisson_ratio))
#np.set_printoptions(precision=2)
print(C)
np.savetxt("data/C_MgSi100_225.csv", C, delimiter=",")
def _symmetrize_elastic_tensor(self, spg=1, perm="xyz"):
if spg == 1:
return
permut_lut = {"xyz": 0, "zxy": -1, "yzx": -2}
from ase.spacegroup import Spacegroup
spg = Spacegroup(spg)
sym_op = spg.get_rotations()
full = self._to_full_rank4(self.elastic_tensor)
new_tensor = np.zeros((3, 3, 3, 3))
for op in sym_op:
op = np.roll(op, permut_lut[perm], (0, 1))
avg_tensor = np.zeros((3, 3, 3, 3))
avg_tensor = np.einsum("pl,ijkl->ijkp", op, full)
avg_tensor = np.einsum("ok,ijkp->ijop", op, avg_tensor)
avg_tensor = np.einsum("nj,ijop->inop", op, avg_tensor)
avg_tensor = np.einsum("mi,inop->mnop", op, avg_tensor)
new_tensor += avg_tensor
new_tensor /= len(sym_op)
self.elastic_tensor = self._to_mandel_rank4(new_tensor)
