def setUp(self):
element_profile = {'Ni': {'r': 0.5, 'w': 1}}
describer1 = BispectrumCoefficients(rcutfac=4.1, twojmax=8,
element_profile=element_profile,
quadratic=False,
pot_fit=True)
model1 = LinearModel(describer=describer1)
model1.model.coef_ = coeff
model1.model.intercept_ = intercept
snap1 = SNAPotential(model=model1)
snap1.specie = Element('Ni')
self.ff_settings1 = snap1
describer2 = BispectrumCoefficients(rcutfac=4.1, twojmax=8,
element_profile=element_profile,
quadratic=True,
pot_fit=True)
model2 = LinearModel(describer=describer2)
model2.model.coef_ = coeff
model2.model.intercept_ = intercept
snap2 = SNAPotential(model=model2)
snap2.specie = Element('Ni')
self.ff_settings2 = snap2
self.struct = Structure.from_spacegroup('Fm-3m',
Lattice.cubic(3.506),
['Ni'], [[0, 0, 0]])
def test_subscripts():
def from_lmp_doc(twojmax, diagonal):
js = []
for j1 in range(0, twojmax + 1):
if diagonal == 2:
js.append([j1, j1, j1])
elif diagonal == 1:
for j in range(0, min(twojmax, 2 * j1) + 1, 2):
js.append([j1, j1, j])
elif diagonal == 0:
for j2 in range(0, j1 + 1):
for j in range(j1 - j2, min(twojmax, j1 + j2) + 1, 2):
js.append([j1, j2, j])
elif diagonal == 3:
for j2 in range(0, j1 + 1):
for j in range(j1 - j2, min(twojmax, j1 + j2) + 1, 2):
if j >= j1:
js.append([j1, j2, j])
return js
profile = {"Mo": {"r": 0.5, "w": 1}}
for d in range(4):
for tjm in range(11):
bc = BispectrumCoefficients(1.0,
twojmax=tjm,
element_profile=profile,
quadratic=False,
diagonalstyle=d)
np.testing.assert_equal(bc.subscripts, from_lmp_doc(tjm, d))
def from_config(param_file, coeff_file, **kwargs):
"""
Initialize potentials with parameters file and coefficient file.
Args:
param_file (str): The file storing the configuration of potentials.
coeff_file (str): The file storing the coefficients of potentials.
Return:
SNAPotential.
"""
with open(coeff_file) as f:
coeff_lines = f.readlines()
coeff_lines = [
line for line in coeff_lines if not line.startswith('#')
]
specie, r, w = coeff_lines[1].split()
r, w = float(r), int(w)
element_profile = {specie: {'r': r, 'w': w}}
rcut_pattern = re.compile('rcutfac (.*?)\n', re.S)
twojmax_pattern = re.compile('twojmax (\d*)\n', re.S)
rfac_pattern = re.compile('rfac0 (.*?)\n', re.S)
rmin_pattern = re.compile('rmin0 (.*?)\n', re.S)
diagonalstyle_pattern = re.compile('diagonalstyle (.*?)\n', re.S)
quadratic_pattern = re.compile('quadraticflag (.*?)(?=\n|$)', re.S)
with zopen(param_file, 'rt') as f:
param_lines = f.read()
rcut = float(rcut_pattern.findall(param_lines)[-1])
twojmax = int(twojmax_pattern.findall(param_lines)[-1])
rfac = float(rfac_pattern.findall(param_lines)[-1])
rmin = int(rmin_pattern.findall(param_lines)[-1])
diagonal = int(diagonalstyle_pattern.findall(param_lines)[-1])
if quadratic_pattern.findall(param_lines):
quadratic = bool(int(quadratic_pattern.findall(param_lines)[-1]))
else:
quadratic = False
describer = BispectrumCoefficients(rcutfac=rcut,
twojmax=twojmax,
rfac0=rfac,
element_profile=element_profile,
rmin0=rmin,
diagonalstyle=diagonal,
quadratic=quadratic,
pot_fit=True)
model = LinearModel(describer=describer, **kwargs)
model.model.coef_ = np.array(coeff_lines[2:], dtype=np.float)
model.model.intercept_ = 0
snap = SNAPotential(model=model)
snap.specie = Element(specie)
return snap
def setUp(self):
element_profile = {'Ni': {'r': 0.5, 'w': 1}}
describer = BispectrumCoefficients(rcutfac=4.1, twojmax=8,
element_profile=element_profile,
pot_fit=True)
model = LinearModel(describer=describer)
model.model.coef_ = coeff
model.model.intercept_ = intercept
snap = SNAPotential(model=model)
snap.specie = Element('Ni')
self.ff_settings = snap
def setUp(self):
element_profile = {'Ni': {'r': 0.5, 'w': 1}}
describer = BispectrumCoefficients(rcutfac=4.1, twojmax=8,
element_profile=element_profile,
pot_fit=True)
model = LinearModel(describer=describer)
model.model.coef_ = coeff
model.model.intercept_ = intercept
snap = SNAPotential(model=model)
snap.specie = Element('Ni')
self.struct = Structure.from_spacegroup('Fm-3m',
Lattice.cubic(3.506),
['Ni'], [[0, 0, 0]])
self.ff_settings = snap
def test_describe(self):
s = Structure.from_spacegroup(225, Lattice.cubic(5.69169),
['Na', 'Cl'], [[0, 0, 0], [0, 0, 0.5]])
profile = dict(Na=dict(r=0.3, w=0.9), Cl=dict(r=0.7, w=3.0))
s *= [2, 2, 2]
structures = [s] * 10
for s in structures:
n = np.random.randint(4)
inds = np.random.randint(16, size=n)
s.remove_sites(inds)
bc_atom = BispectrumCoefficients(5,
3,
profile,
diagonalstyle=2,
quadratic=False,
pot_fit=False)
df_atom = bc_atom.describe_all(structures)
for i, s in enumerate(structures):
df_s = df_atom.xs(i, level='input_index')
self.assertEqual(df_s.shape, (len(s), 4))
self.assertTrue(df_s.equals(bc_atom.describe(s)))
bc_pot = BispectrumCoefficients(5,
3,
profile,
diagonalstyle=2,
quadratic=False,
pot_fit=True)
df_pot = bc_pot.describe_all(structures, include_stress=True)
for i, s in enumerate(structures):
df_s = df_pot.xs(i, level='input_index')
self.assertEqual(df_s.shape, ((1 + len(s) * 3 + 6, 10)))
self.assertTrue(
df_s.equals(bc_pot.describe(s, include_stress=True)))
sna = df_s.iloc[0]
for specie in ['Na', 'Cl']:
self.assertAlmostEqual(
sna[specie, 'n'],
s.composition.fractional_composition[specie])
np.testing.assert_array_equal(df_s[specie, 'n'][1:],
np.zeros(len(s) * 3 + 6))