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from __future__ import division, print_function
import numpy as np
from ase.build import bulk
from ase.calculators.lj import LennardJones
from ase.constraints import UnitCellFilter
from ase.optimize import BFGS
# Theoretical infinite-cutoff LJ FCC unit cell parameters
vol0 = 4 * 0.91615977036 # theoretical minimum
a0 = vol0**(1 / 3)
a = bulk('X', 'fcc', a=a0)
cell0 = a.get_cell()
a.calc = LennardJones()
a.set_cell(np.dot(a.cell,
[[1.02, 0, 0.03],
[0, 0.99, -0.02],
[0.1, -0.01, 1.03]]),
scale_atoms=True)
a *= (1, 2, 3)
cell0 *= np.array([1, 2, 3])[:, np.newaxis]
a.rattle()
# Verify analytical stress tensor against numerical value
s_analytical = a.get_stress()
s_numerical = a.calc.calculate_numerical_stress(a, 1e-5)
s_p_err = 100 * (s_numerical - s_analytical) / s_numerical
print("Analytical stress:\n", s_analytical)
print("Numerical stress:\n", s_numerical)
print("Percent error in stress:\n", s_p_err)
assert np.all(abs(s_p_err) < 1e-5)
# Minimize unit cell
opt = BFGS(UnitCellFilter(a))
opt.run(fmax=1e-3)
# Verify minimized unit cell using Niggli tensors
g_minimized = np.dot(a.cell, a.cell.T)
g_theory = np.dot(cell0, cell0.T)
g_p_err = 100 * (g_minimized - g_theory) / g_theory
print("Minimized Niggli tensor:\n", g_minimized)
print("Theoretical Niggli tensor:\n", g_theory)
print("Percent error in Niggli tensor:\n", g_p_err)
assert np.all(abs(g_p_err) < 1)
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