def test_forces_random_structure(self):
atoms = FaceCenteredCubic('H', size=[2,2,2], latticeconstant=2.37126)
calc = Polydisperse(InversePowerLawPotential(1.0, 1.4, 0.1, 3, 1, 2.22))
atoms.set_masses(masses=np.repeat(1.0, len(atoms)))
atoms.set_array("size", np.random.uniform(1.0, 2.22, size=len(atoms)), dtype=float)
atoms.set_calculator(calc)
f = atoms.get_forces()
fn = calc.calculate_numerical_forces(atoms, d=0.0001)
self.assertArrayAlmostEqual(f, fn, tol=self.tol)
def test_symmetry_sparse(self):
"""
Test the symmetry of the dense Hessian matrix
"""
atoms = FaceCenteredCubic('H', size=[2,2,2], latticeconstant=2.37126)
calc = Polydisperse(InversePowerLawPotential(1.0, 1.4, 0.1, 3, 1, 2.22))
atoms.set_masses(masses=np.repeat(1.0, len(atoms)))
atoms.set_array("size", np.random.uniform(1.0, 2.22, size=len(atoms)), dtype=float)
atoms.set_calculator(calc)
dyn = FIRE(atoms)
dyn.run(fmax=1e-5)
H = calc.hessian_matrix(atoms)
H = H.todense()
self.assertArrayAlmostEqual(np.sum(np.abs(H-H.T)), 0, tol=1e-5)
def test_hessian_random_structure(self):
"""
Test the computation of the Hessian matrix
"""
atoms = FaceCenteredCubic('H', size=[2,2,2], latticeconstant=2.37126)
calc = Polydisperse(InversePowerLawPotential(1.0, 1.4, 0.1, 3, 1, 2.22))
atoms.set_masses(masses=np.repeat(1.0, len(atoms)))
atoms.set_array("size", np.random.uniform(1.0, 2.22, size=len(atoms)), dtype=float)
atoms.set_calculator(calc)
dyn = FIRE(atoms)
dyn.run(fmax=1e-5)
H_analytical = calc.hessian_matrix(atoms)
H_analytical = H_analytical.todense()
H_numerical = fd_hessian(atoms, dx=1e-5, indices=None)
H_numerical = H_numerical.todense()
self.assertArrayAlmostEqual(H_analytical, H_numerical, tol=self.tol)
def test_hessian_divide_by_masses(self):
"""
Test the computation of the Hessian matrix
"""
atoms = FaceCenteredCubic('H', size=[2,2,2], latticeconstant=2.37126)
atoms.set_array("size", np.random.uniform(1.0, 2.22, size=len(atoms)), dtype=float)
masses_n = np.random.randint(1, 10, size=len(atoms))
atoms.set_masses(masses=masses_n)
calc = Polydisperse(InversePowerLawPotential(1.0, 1.4, 0.1, 3, 1, 2.22))
atoms.set_calculator(calc)
dyn = FIRE(atoms)
dyn.run(fmax=1e-5)
D_analytical = calc.hessian_matrix(atoms, divide_by_masses=True)
D_analytical = D_analytical.todense()
H_analytical = calc.hessian_matrix(atoms)
H_analytical = H_analytical.todense()
masses_nc = masses_n.repeat(3)
H_analytical /= np.sqrt(masses_nc.reshape(-1,1)*masses_nc.reshape(1,-1))
self.assertArrayAlmostEqual(H_analytical, D_analytical, tol=self.tol)
