def strucs():
"""Create two random structures."""
np.random.seed(0)
positions_1 = np.random.rand(n_atoms, 3)
positions_2 = np.random.rand(n_atoms, 3)
structure_1 = struc.Structure(cell, species, positions_1)
structure_2 = struc.Structure(cell, species, positions_2)
strucs = [structure_1, structure_2]
yield strucs
def another_env(cutoffs, delt):
cell = 10.0 * np.eye(3)
# atomic structure 1
pos_1 = np.vstack([[0, 0, 0], 0.1 * random([3, 3])])
pos_1[1, 1] += 1
pos_1[2, 0] += 1
pos_1[3, :2] += 1
pos_2 = deepcopy(pos_1)
pos_2[0][0] = delt
pos_3 = deepcopy(pos_1)
pos_3[0][0] = -delt
species_1 = [1, 1, 1, 1]
test_structure_1 = struc.Structure(cell, species_1, pos_1)
test_structure_2 = struc.Structure(cell, species_1, pos_2)
test_structure_3 = struc.Structure(cell, species_1, pos_3)
# atom 0, original position
env1_1_0 = env.AtomicEnvironment(test_structure_1, 0, cutoffs)
# atom 0, 0 perturbe along x
env1_2_0 = env.AtomicEnvironment(test_structure_2, 0, cutoffs)
# atom 1, 0 perturbe along x
env1_2_1 = env.AtomicEnvironment(test_structure_2, 1, cutoffs)
# atom 2, 0 perturbe along x
env1_2_2 = env.AtomicEnvironment(test_structure_2, 2, cutoffs)
# atom 0, 0 perturbe along -x
env1_3_0 = env.AtomicEnvironment(test_structure_3, 0, cutoffs)
# atom 1, 0 perturbe along -x
env1_3_1 = env.AtomicEnvironment(test_structure_3, 1, cutoffs)
# atom 2, 0 perturbe along -x
env1_3_2 = env.AtomicEnvironment(test_structure_3, 2, cutoffs)
# create env 2
pos_1 = np.vstack([[0, 0, 0], 0.1 * random([3, 3])])
pos_1[1, 1] += 1
pos_1[2, 0] += 1
pos_1[3, :2] += 1
pos_2 = deepcopy(pos_1)
pos_2[0][0] = delt
pos_3 = deepcopy(pos_1)
pos_3[0][0] = -delt
species_2 = [1, 2, 2, 1]
test_structure_1 = struc.Structure(cell, species_2, pos_1)
env2_1_0 = env.AtomicEnvironment(test_structure_1, 0, cutoffs)
return env1_1_0, env1_2_0, env1_3_0, \
env1_2_1, env1_3_1, env1_2_2, env1_3_2, env2_1_0
def test_three_body_grad():
# create env 1
cell = np.eye(3)
cutoffs = np.array([1, 1])
positions_1 = [np.array([0., 0., 0.]),
np.array([random(), random(), random()]),
np.array([random(), random(), random()])]
species_1 = [1, 2, 1]
atom_1 = 0
test_structure_1 = struc.Structure(cell, species_1, positions_1)
env1 = env.AtomicEnvironment(test_structure_1, atom_1, cutoffs)
# create env 2
positions_1 = [np.array([0., 0., 0.]),
np.array([random(), random(), random()]),
np.array([random(), random(), random()])]
species_2 = [1, 1, 2]
atom_2 = 0
test_structure_1 = struc.Structure(cell, species_2, positions_1)
env2 = env.AtomicEnvironment(test_structure_1, atom_2, cutoffs)
sig = random()
ls = random()
d1 = randint(1, 3)
d2 = randint(1, 3)
hyps = np.array([sig, ls])
grad_test = en.three_body_grad(env1, env2, d1, d2, hyps, cutoffs)
delta = 1e-8
new_sig = sig + delta
new_ls = ls + delta
sig_derv_brute = (en.three_body(env1, env2, d1, d2,
np.array([new_sig, ls]),
cutoffs) -
en.three_body(env1, env2, d1, d2,
hyps, cutoffs)) / delta
l_derv_brute = (en.three_body(env1, env2, d1, d2,
np.array([sig, new_ls]),
cutoffs) -
en.three_body(env1, env2, d1, d2,
hyps, cutoffs)) / delta
tol = 1e-4
assert(np.isclose(grad_test[1][0], sig_derv_brute, atol=tol))
assert(np.isclose(grad_test[1][1], l_derv_brute, atol=tol))
def test_three_body_force_en():
"""Check that the analytical force/en kernel matches finite difference of
energy kernel."""
# create env 1
delt = 1e-8
cell = np.eye(3)
cutoffs = np.array([1, 1])
positions_1 = [
np.array([0., 0., 0.]),
np.array([random(), random(), random()]),
np.array([random(), random(), random()])
]
positions_2 = deepcopy(positions_1)
positions_2[0][0] = delt
species_1 = [1, 2, 1]
atom_1 = 0
test_structure_1 = struc.Structure(cell, species_1, positions_1)
test_structure_2 = struc.Structure(cell, species_1, positions_2)
env1_1 = env.AtomicEnvironment(test_structure_1, atom_1, cutoffs)
env1_2 = env.AtomicEnvironment(test_structure_2, atom_1, cutoffs)
# create env 2
positions_1 = [
np.array([0., 0., 0.]),
np.array([random(), random(), random()]),
np.array([random(), random(), random()])
]
species_2 = [1, 1, 2]
atom_2 = 0
test_structure_1 = struc.Structure(cell, species_2, positions_1)
env2 = env.AtomicEnvironment(test_structure_1, atom_2, cutoffs)
sig = random()
ls = random()
d1 = 1
hyps = np.array([sig, ls])
# check force kernel
calc1 = en.three_body_en(env1_2, env2, hyps, cutoffs)
calc2 = en.three_body_en(env1_1, env2, hyps, cutoffs)
kern_finite_diff = (calc1 - calc2) / delt
kern_analytical = en.three_body_force_en(env1_1, env2, d1, hyps, cutoffs)
tol = 1e-4
assert (np.isclose(-kern_finite_diff / 3, kern_analytical, atol=tol))
def force_envs(strucs):
"""Perturb atom 0 in both structures up and down and in all directions."""
signs = [1, -1]
dims = [0, 1, 2]
force_envs = []
for structure in strucs:
sign_envs_curr = []
for sign in signs:
dim_envs_curr = []
for dim in dims:
positions_pert = np.copy(structure.positions)
positions_pert[0, dim] += delta * sign
struc_pert = struc.Structure(structure.cell,
structure.coded_species,
positions_pert)
atom_envs = []
for n in range(structure.nat):
env_curr = env.AtomicEnvironment(struc_pert, n, cutoffs)
atom_envs.append(env_curr)
dim_envs_curr.append(atom_envs)
sign_envs_curr.append(dim_envs_curr)
force_envs.append(sign_envs_curr)
yield force_envs
def predict_atom_diag_var_2b(atom_env, gp_model, force_kernel):
bond_array = atom_env.bond_array_2
ctype = atom_env.ctype
var = 0
for m in range(bond_array.shape[0]):
ri1 = bond_array[m, 0]
ci1 = bond_array[m, 1:]
etype1 = atom_env.etypes[m]
# build up a struc of triplet for prediction
cell = np.eye(3) * 100
positions = np.array([np.zeros(3), ri1 * ci1])
species = np.array([ctype, etype1])
spc_struc = struc.Structure(cell, species, positions)
spc_struc.coded_species = np.array(species)
env12 = env.AtomicEnvironment(spc_struc, 0, gp_model.cutoffs)
coord = np.copy(env12.bond_array_2[0, 1:])
# env12.bond_array_2[0, 1:] = np.array([1., 0., 0.])
if force_kernel:
v12 = np.zeros(3)
for d in range(3):
_, v12[d] = gp_model.predict(env12, d + 1)
print("v12", np.sqrt(v12), coord)
else:
_, v12 = gp_model.predict_local_energy_and_var(env12)
var += np.sqrt(v12)
var = var**2
return var
def generate_mb_envs_pos(positions0,
species_1,
cutoffs,
cell,
delt,
d1,
mask=None):
positions = [positions0]
noa = len(positions0)
positions_2 = deepcopy(positions0)
positions_2[0][d1 - 1] = delt
positions += [positions_2]
positions_3 = deepcopy(positions[0])
positions_3[0][d1 - 1] = -delt
positions += [positions_3]
test_struc = []
for i in range(3):
test_struc += [struc.Structure(cell, species_1, positions[i])]
env_0 = []
env_p = []
env_m = []
for i in range(noa):
env_0 += [env.AtomicEnvironment(test_struc[0], i, cutoffs)]
env_p += [env.AtomicEnvironment(test_struc[1], i, cutoffs)]
env_m += [env.AtomicEnvironment(test_struc[2], i, cutoffs)]
return [env_0, env_p, env_m]
def get_structure_from_input(self, prev_pos_init):
positions, species, cell, masses = \
self.dft_module.parse_dft_input(self.dft_input)
self.structure = struc.Structure(
cell=cell, species=species, positions=positions, mass_dict=masses,
prev_positions=prev_pos_init, species_labels=species)
def output_md_structures(self):
"""
Returns structure objects corresponding to the MD frames of an OTF run.
:return:
"""
structures = []
cell = self.header["cell"]
species = self.header["species"]
for i in range(len(self.position_list)):
if not self.calculate_energy:
energy = 0
else:
energy = self.energies[i]
cur_struc = struc.Structure(
cell=cell,
species=species,
positions=self.position_list[i],
forces=self.force_list[i],
stds=self.uncertainty_list[i],
)
cur_struc.energy = energy
# cur_struc.stress = self.stress_list[i]
structures.append(cur_struc)
return structures
def make_gp(self,
cell=None,
kernel=None,
kernel_grad=None,
algo=None,
call_no=None,
cutoffs=None,
hyps=None,
init_gp=None,
energy_force_kernel=None,
hyp_no=None,
par=True):
if init_gp is None:
# Use run's values as extracted from header
# TODO Allow for kernel gradient in header
if cell is None:
cell = self.header['cell']
if kernel is None:
kernel = self.header['kernel']
if kernel_grad is None:
raise Exception('Kernel gradient not supplied')
if algo is None:
algo = self.header['algo']
if cutoffs is None:
cutoffs = self.header['cutoffs']
if call_no is None:
call_no = len(self.gp_position_list)
if hyp_no is None:
hyp_no = call_no
if hyps is None:
gp_hyps = self.gp_hyp_list[hyp_no - 1][-1]
else:
gp_hyps = hyps
gp_model = \
gp.GaussianProcess(kernel, kernel_grad, gp_hyps,
cutoffs, opt_algorithm=algo,
energy_force_kernel=energy_force_kernel,
par=par)
else:
gp_model = init_gp
call_no = len(self.gp_position_list)
gp_hyps = self.gp_hyp_list[hyp_no - 1][-1]
gp_model.hyps = gp_hyps
for (positions, forces, atoms, _, species) in \
zip(self.gp_position_list[:call_no],
self.gp_force_list[:call_no],
self.gp_atom_list[:call_no], self.gp_hyp_list[:call_no],
self.gp_species_list[:call_no]):
struc_curr = struc.Structure(cell, species, positions)
gp_model.update_db(struc_curr, forces, custom_range=atoms)
gp_model.set_L_alpha()
return gp_model
def structure(otf_object):
# create test structure
otf_cell = otf_object.header['cell']
species = np.array([47, 53] * 27)
positions = otf_object.position_list[-1]
test_struc = struc.Structure(otf_cell, species, positions)
yield test_struc
del test_struc
def dft_input_to_structure(qe_input: str):
"""
Parses a qe input and returns the atoms in the file as a Structure object
:param qe_input: QE Input file to parse
:return:
"""
positions, species, cell, masses = parse_dft_input(qe_input)
_, coded_species = struc.get_unique_species(species)
return struc.Structure(positions=positions, species=coded_species,
cell=cell, mass_dict=masses, species_labels=species)
def make_gp(
self,
cell=None,
call_no=None,
hyps=None,
init_gp=None,
hyp_no=None,
**kwargs,
):
if "restart" in self.header and self.header["restart"] > 0:
assert (
init_gp is not None
), "Please input the init_gp as the gp model dumppedbefore restarting otf."
if call_no is None:
call_no = len(self.gp_position_list)
if hyp_no is None:
hyp_no = len(self.gp_hyp_list) # use the last hyps by default
if hyps is None:
# check out the last non-empty element from the list
hyps = self.gp_hyp_list[hyp_no - 1]
if cell is None:
cell = self.header["cell"]
if init_gp is None:
# Use run's values as extracted from header
# TODO Allow for kernel gradient in header
dictionary = deepcopy(self.header)
dictionary["hyps"] = hyps
for k in kwargs:
if kwargs[k] is not None:
dictionary[k] = kwargs[k]
gp_model = GaussianProcess.from_dict(dictionary)
else:
gp_model = init_gp
gp_model.hyps = hyps
for (positions, forces, atoms, species) in zip(
self.gp_position_list[:call_no],
self.gp_force_list[:call_no],
self.gp_atom_list[:call_no],
self.gp_species_list[:call_no],
):
struc_curr = struc.Structure(cell, species, positions)
gp_model.update_db(struc_curr, forces, custom_range=atoms)
gp_model.set_L_alpha()
return gp_model
def make_gp(self, cell=None, kernel_name=None, algo=None,
call_no=None, cutoffs=None, hyps=None, init_gp=None,
hyp_no=None, par=True, kernel=None):
if init_gp is None:
# Use run's values as extracted from header
# TODO Allow for kernel gradient in header
if cell is None:
cell = self.header['cell']
if kernel_name is None:
kernel_name = self.header['kernel_name']
if algo is None:
algo = self.header['algo']
if cutoffs is None:
cutoffs = self.header['cutoffs']
if call_no is None:
call_no = len(self.gp_position_list)
if hyp_no is None:
hyp_no = call_no
if hyps is None:
gp_hyps = self.gp_hyp_list[hyp_no-1][-1]
else:
gp_hyps = hyps
if (kernel is not None) and (kernel_name is None):
DeprecationWarning("kernel replaced with kernel_name")
kernel_name = kernel.__name__
gp_model = \
gp.GaussianProcess(kernel_name=kernel_name,
hyps=gp_hyps,
cutoffs=cutoffs, opt_algorithm=algo,
par=par)
else:
gp_model = init_gp
call_no = len(self.gp_position_list)
gp_hyps = self.gp_hyp_list[hyp_no-1][-1]
gp_model.hyps = gp_hyps
for (positions, forces, atoms, _, species) in \
zip(self.gp_position_list[:call_no],
self.gp_force_list[:call_no],
self.gp_atom_list[:call_no], self.gp_hyp_list[:call_no],
self.gp_species_list[:call_no]):
struc_curr = struc.Structure(cell, species, positions)
gp_model.update_db(struc_curr, forces, custom_range=atoms)
gp_model.set_L_alpha()
return gp_model
def make_gp(
self,
cell=None,
call_no=None,
hyps=None,
init_gp=None,
hyp_no=None,
**kwargs,
):
if call_no is None:
call_no = len(self.gp_position_list)
if hyp_no is None:
hyp_no = call_no
if hyps is None:
# check out the last non-empty element from the list
for icall in reversed(range(hyp_no)):
if len(self.gp_hyp_list[icall]) > 0:
hyps = self.gp_hyp_list[icall][-1]
break
if cell is None:
cell = self.header['cell']
if init_gp is None:
# Use run's values as extracted from header
# TODO Allow for kernel gradient in header
dictionary = deepcopy(self.header)
dictionary['hyps'] = hyps
for k in kwargs:
if kwargs[k] is not None:
dictionary[k] = kwargs[k]
gp_model = \
GaussianProcess.from_dict(dictionary)
else:
gp_model = init_gp
gp_model.hyps = hyps
for (positions, forces, atoms, _, species) in \
zip(self.gp_position_list[:call_no],
self.gp_force_list[:call_no],
self.gp_atom_list[:call_no], self.gp_hyp_list[:call_no],
self.gp_species_list[:call_no]):
struc_curr = struc.Structure(cell, species, positions)
gp_model.update_db(struc_curr, forces, custom_range=atoms)
gp_model.set_L_alpha()
return gp_model
def get_grid_env(GP, species, bodies):
if isinstance(GP.cutoffs, dict):
max_cut = np.max(list(GP.cutoffs.values()))
else:
max_cut = np.max(GP.cutoffs)
big_cell = np.eye(3) * 100
positions = [[(i + 1) / (bodies + 1) * 0.1, 0, 0] for i in range(bodies)]
grid_struc = struc.Structure(big_cell, species, positions)
grid_env = env.AtomicEnvironment(grid_struc,
0,
GP.cutoffs,
cutoffs_mask=GP.hyps_mask)
return grid_env
def predict_atom_diag_var_3b(atom_env, gp_model, force_kernel):
bond_array = atom_env.bond_array_3
triplets = atom_env.triplet_counts
cross_bond_inds = atom_env.cross_bond_inds
cross_bond_dists = atom_env.cross_bond_dists
ctype = atom_env.ctype
var = 0
pred_dict = {}
for m in range(bond_array.shape[0]):
ri1 = bond_array[m, 0]
ci1 = bond_array[m, 1:]
etype1 = atom_env.etypes[m]
for n in range(triplets[m]):
ind1 = cross_bond_inds[m, m + n + 1]
ri2 = bond_array[ind1, 0]
ci2 = bond_array[ind1, 1:]
etype2 = atom_env.etypes[ind1]
ri3 = cross_bond_dists[m, m + n + 1]
# build up a struc of triplet for prediction
cell = np.eye(3) * 100
positions = np.array([np.zeros(3), ri1 * ci1, ri2 * ci2])
species = np.array([ctype, etype1, etype2])
spc_struc = struc.Structure(cell, species, positions)
spc_struc.coded_species = np.array(species)
env12 = env.AtomicEnvironment(spc_struc, 0, gp_model.cutoffs)
# env12.bond_array_3[0, 1:] = np.array([1., 0., 0.])
# env12.bond_array_3[1, 1:] = np.array([0., 0., 0.])
if force_kernel:
v12 = np.zeros(3)
for d in range(3):
_, v12[d] = gp_model.predict(env12, d + 1)
print("v12", np.sqrt(v12), env12.ctype, env12.etypes)
else:
_, v12 = gp_model.predict_local_energy_and_var(env12)
spc = f"{env12.ctype}_{env12.etypes[0]}_{env12.etypes[1]}"
if spc in pred_dict:
pred_dict[spc] += np.sqrt(v12)
else:
pred_dict[spc] = np.sqrt(v12)
var += np.sqrt(v12)
var = var**2
print(pred_dict)
return var
def fit(self, parent_data):
for image in parent_data:
train_structure = struc.Structure(image.get_cell(),
image.get_atomic_numbers(),
image.get_positions())
forces = image.get_forces(apply_constraint=False)
energy = image.get_potential_energy(apply_constraint=False)
self.gp_model.update_db(train_structure,
forces, [],
energy,
mode="all",
update_qr=True)
def partial_fit(self, new_dataset):
for image in new_dataset:
train_structure = struc.Structure(image.get_cell(),
image.get_atomic_numbers(),
image.get_positions())
forces = image.get_forces(apply_constraint=False)
energy = image.get_potential_energy(apply_constraint=False)
self.gp_model.update_db(
train_structure,
forces,
self.update_gp_range,
energy,
mode=self.update_gp_mode,
update_qr=True,
)
def output_md_structures(self):
"""
Returns structure objects corresponding to the MD frames of an OTF run.
:return:
"""
positions = self.position_list
structures = []
cell = self.header['cell']
species = self.header['species']
forces = self.force_list
stds = self.uncertainty_list
for i in range(len(positions)):
cur_struc = struc.Structure(cell=cell, species=species,
positions=positions[i])
cur_struc.forces = forces[i]
cur_struc.stds = stds[i]
structures.append(cur_struc)
return structures
def build_bond_struc(self, struc_params):
'''
build a bond structure, used in grid generating
'''
cutoff = np.min(self.GP.cutoffs)
cell = struc_params['cube_lat']
mass_dict = struc_params['mass_dict']
bond_struc = []
for bodies in [2, 3]:
species = [struc_params['species'] for i in range(bodies)]
positions = [[(i+1)/(bodies+1)*cutoff, 0, 0] \
for i in range(bodies)]
bond_struc.append(
struc.Structure(cell, species, positions, mass_dict))
if self.bodies == '2':
return bond_struc[0]
elif self.bodies == '3':
return bond_struc[1]
elif self.bodies == '2+3':
return bond_struc
def stress_envs(strucs):
"""Strain both structures up and down and in all directions."""
stress_envs = []
signs = [1, -1]
for structure in strucs:
sign_envs_curr = []
for sign in signs:
strain_envs_curr = []
for m in range(3):
for n in range(m, 3):
cell_pert = np.copy(structure.cell)
positions_pert = np.copy(structure.positions)
# Strain the cell.
for p in range(3):
cell_pert[p, m] += structure.cell[p, n] * delta * sign
# Strain the positions.
for k in range(structure.nat):
positions_pert[
k, m] += structure.positions[k, n] * delta * sign
struc_pert = struc.Structure(cell_pert,
structure.coded_species,
positions_pert)
atom_envs = []
for q in range(structure.nat):
env_curr = env.AtomicEnvironment(
struc_pert, q, cutoffs)
atom_envs.append(env_curr)
strain_envs_curr.append(atom_envs)
sign_envs_curr.append(strain_envs_curr)
stress_envs.append(sign_envs_curr)
yield stress_envs
def generate_envs(cutoffs, delta):
"""
create environment with perturbation on
direction i
"""
# create env 1
# perturb the x direction of atom 0 for +- delta
cell = np.eye(3) * np.max(cutoffs + 0.1)
atom_1 = 0
pos_1 = np.vstack([[0, 0, 0], random([3, 3])])
pos_2 = deepcopy(pos_1)
pos_2[0][0] = delta
pos_3 = deepcopy(pos_1)
pos_3[0][0] = -delta
species_1 = [1, 2, 1, 1] # , 1, 1, 2, 1, 2]
test_structure_1 = struc.Structure(cell, species_1, pos_1)
test_structure_2 = struc.Structure(cell, species_1, pos_2)
test_structure_3 = struc.Structure(cell, species_1, pos_3)
env1_1 = env.AtomicEnvironment(test_structure_1, atom_1, cutoffs)
env1_2 = env.AtomicEnvironment(test_structure_2, atom_1, cutoffs)
env1_3 = env.AtomicEnvironment(test_structure_3, atom_1, cutoffs)
# create env 2
# perturb the y direction
pos_1 = np.vstack([[0, 0, 0], random([3, 3])])
pos_2 = deepcopy(pos_1)
pos_2[0][1] = delta
pos_3 = deepcopy(pos_1)
pos_3[0][1] = -delta
atom_2 = 0
species_2 = [1, 1, 2, 1] #, 2, 1, 2, 2, 2]
test_structure_1 = struc.Structure(cell, species_2, pos_1)
test_structure_2 = struc.Structure(cell, species_2, pos_2)
test_structure_3 = struc.Structure(cell, species_2, pos_3)
env2_1 = env.AtomicEnvironment(test_structure_1, atom_2, cutoffs)
env2_2 = env.AtomicEnvironment(test_structure_2, atom_2, cutoffs)
env2_3 = env.AtomicEnvironment(test_structure_3, atom_2, cutoffs)
return env1_1, env1_2, env1_3, env2_1, env2_2, env2_3
def generate_envs(cutoffs, delta):
# create env 1
cell = np.eye(3)
positions_1 = np.vstack([[0, 0, 0], random([3, 3])])
positions_2 = np.copy(positions_1)
positions_2[0][0] = delta
positions_3 = np.copy(positions_1)
positions_3[0][0] = -delta
species_1 = [1, 2, 1, 1, 1, 1, 2, 1, 2]
atom_1 = 0
test_structure_1 = struc.Structure(cell, species_1, positions_1)
test_structure_2 = struc.Structure(cell, species_1, positions_2)
test_structure_3 = struc.Structure(cell, species_1, positions_3)
env1_1 = env.AtomicEnvironment(test_structure_1, atom_1, cutoffs)
env1_2 = env.AtomicEnvironment(test_structure_2, atom_1, cutoffs)
env1_3 = env.AtomicEnvironment(test_structure_3, atom_1, cutoffs)
# create env 2
positions_1 = np.vstack([[0, 0, 0], random([3, 3])])
positions_2 = np.copy(positions_1)
positions_2[0][1] = delta
positions_3 = np.copy(positions_1)
positions_3[0][1] = -delta
atom_2 = 0
species_2 = [1, 1, 2, 1, 2, 1, 2, 2, 2]
test_structure_1 = struc.Structure(cell, species_2, positions_1)
test_structure_2 = struc.Structure(cell, species_2, positions_2)
test_structure_3 = struc.Structure(cell, species_2, positions_3)
env2_1 = env.AtomicEnvironment(test_structure_1, atom_2, cutoffs)
env2_2 = env.AtomicEnvironment(test_structure_2, atom_2, cutoffs)
env2_3 = env.AtomicEnvironment(test_structure_3, atom_2, cutoffs)
return env1_1, env1_2, env1_3, env2_1, env2_2, env2_3
N_Ag += 1
else:
N_Pd += 1
species = ['Ag'] * N_Ag + ['Pd'] * N_Pd
nat = len(species)
dump.close()
energies = np.zeros(len(lammps_files))
"""Print forces and GP standard deviations to make sure the values look reasonable."""
out_f = open('gp_f.txt', 'w')
# loop over NEB structures
for count, lammps_file in enumerate(tqdm(lammps_files)):
"""The lammps_parser function, defined in dump_parser.py in this directory, extracts the coordinates from a dump file and converts the BOX_BOUNDS output into an array of Bravais lattice vectors."""
positions, cell = lammps_parser(lammps_file)
structure = struc.Structure(cell, species, positions)
# loop over atoms in the structure
local_energies = np.zeros(nat)
for n in range(structure.nat):
"""Construct an atomic environment object, which stores all the interatomic distances within 2- and 3-body cutoff spheres that are needed to compute the local energy assigned to the atom."""
chemenv = env.AtomicEnvironment(structure, n, gp_model.cutoffs)
for i in range(3):
force, var = gp_model.predict(chemenv, i + 1)
structure.forces[n][i] = float(force)
structure.stds[n][i] = np.sqrt(np.abs(var))
local_energies[n] = gp_model.predict_local_energy(chemenv)
out_f.write('Image = ' + str(count) + '\n')
out_f.write('forces:')
out_f.write(str(structure.forces))
def __init__(self,
dft_input: str,
dt: float,
number_of_steps: int,
gp: gp.GaussianProcess,
dft_loc: str,
std_tolerance_factor: float = 1,
prev_pos_init: np.ndarray = None,
par: bool = False,
skip: int = 0,
init_atoms: List[int] = None,
calculate_energy=False,
output_name='otf_run',
max_atoms_added=1,
freeze_hyps=10,
rescale_steps=[],
rescale_temps=[],
dft_softwarename="qe",
no_cpus=1,
npool=None,
mpi="srun"):
self.dft_input = dft_input
self.dt = dt
self.number_of_steps = number_of_steps
self.gp = gp
self.dft_loc = dft_loc
self.std_tolerance = std_tolerance_factor
self.skip = skip
self.dft_step = True
self.freeze_hyps = freeze_hyps
self.dft_module = dft_software[dft_softwarename]
# parse input file
positions, species, cell, masses = \
self.dft_module.parse_dft_input(self.dft_input)
_, coded_species = struc.get_unique_species(species)
self.structure = struc.Structure(cell=cell,
species=coded_species,
positions=positions,
mass_dict=masses,
prev_positions=prev_pos_init,
species_labels=species)
self.noa = self.structure.positions.shape[0]
self.atom_list = list(range(self.noa))
self.curr_step = 0
self.max_atoms_added = max_atoms_added
# initialize local energies
if calculate_energy:
self.local_energies = np.zeros(self.noa)
else:
self.local_energies = None
# set atom list for initial dft run
if init_atoms is None:
self.init_atoms = [int(n) for n in range(self.noa)]
else:
self.init_atoms = init_atoms
self.dft_count = 0
# set pred function
if not par and not calculate_energy:
self.pred_func = predict.predict_on_structure
elif par and not calculate_energy:
self.pred_func = predict.predict_on_structure_par
elif not par and calculate_energy:
self.pred_func = predict.predict_on_structure_en
elif par and calculate_energy:
self.pred_func = predict.predict_on_structure_par_en
self.par = par
# set rescale attributes
self.rescale_steps = rescale_steps
self.rescale_temps = rescale_temps
self.output = Output(output_name, always_flush=True)
# set number of cpus and npool for qe runs
self.no_cpus = no_cpus
self.npool = npool
self.mpi = mpi
def build_bond_struc(self, struc_params):
'''
build a bond structure, used in grid generating
'''
cutoff = np.min(self.cutoffs)
cell = struc_params['cube_lat']
species_list = struc_params['species']
N_spc = len(species_list)
# ------------------- 2 body (2 atoms (1 bond) config) ---------------
bond_struc_2 = []
spc_2 = []
if 2 in self.bodies:
bodies = 2
for spc1_ind, spc1 in enumerate(species_list):
for spc2 in species_list[spc1_ind:]:
species = [spc1, spc2]
spc_2.append(species)
positions = [[(i+1)/(bodies+1)*cutoff, 0, 0]
for i in range(bodies)]
spc_struc = \
struc.Structure(cell, species, positions)
spc_struc.coded_species = np.array(species)
bond_struc_2.append(spc_struc)
# ------------------- 3 body (3 atoms (1 triplet) config) -------------
bond_struc_3 = []
spc_3 = []
if 3 in self.bodies:
bodies = 3
for spc1_ind in range(N_spc):
spc1 = species_list[spc1_ind]
for spc2_ind in range(N_spc): # (spc1_ind, N_spc):
spc2 = species_list[spc2_ind]
for spc3_ind in range(N_spc): # (spc2_ind, N_spc):
spc3 = species_list[spc3_ind]
species = [spc1, spc2, spc3]
spc_3.append(species)
positions = [[(i+1)/(bodies+1)*cutoff, 0, 0]
for i in range(bodies)]
spc_struc = struc.Structure(cell, species, positions)
spc_struc.coded_species = np.array(species)
bond_struc_3.append(spc_struc)
# if spc1 != spc2:
# species = [spc2, spc3, spc1]
# spc_3.append(species)
# positions = [[(i+1)/(bodies+1)*cutoff, 0, 0] \
# for i in range(bodies)]
# spc_struc = struc.Structure(cell, species, positions)
# spc_struc.coded_species = np.array(species)
# bond_struc_3.append(spc_struc)
# if spc2 != spc3:
# species = [spc3, spc1, spc2]
# spc_3.append(species)
# positions = [[(i+1)/(bodies+1)*cutoff, 0, 0] \
# for i in range(bodies)]
# spc_struc = struc.Structure(cell, species, positions)
# spc_struc.coded_species = np.array(species)
# bond_struc_3.append(spc_struc)
self.bond_struc = [bond_struc_2, bond_struc_3]
self.spcs = [spc_2, spc_3]
def test_two_plus_three_body_force():
"""Check that the analytical force kernel matches finite difference of
energy kernel."""
# create env 1
delt = 1e-5
cell = np.eye(3)
cutoffs = np.array([1, 0.9])
positions_1 = [np.array([0., 0., 0.]),
np.array([random(), random(), random()]),
np.array([random(), random(), random()])]
positions_2 = deepcopy(positions_1)
positions_2[0][0] = delt
positions_3 = deepcopy(positions_1)
positions_3[0][0] = -delt
species_1 = [1, 2, 1]
atom_1 = 0
test_structure_1 = struc.Structure(cell, species_1, positions_1)
test_structure_2 = struc.Structure(cell, species_1, positions_2)
test_structure_3 = struc.Structure(cell, species_1, positions_3)
env1_1 = env.AtomicEnvironment(test_structure_1, atom_1, cutoffs)
env1_2 = env.AtomicEnvironment(test_structure_2, atom_1, cutoffs)
env1_3 = env.AtomicEnvironment(test_structure_3, atom_1, cutoffs)
# create env 2
positions_1 = [np.array([0., 0., 0.]),
np.array([random(), random(), random()]),
np.array([random(), random(), random()])]
positions_2 = deepcopy(positions_1)
positions_2[0][1] = delt
positions_3 = deepcopy(positions_1)
positions_3[0][1] = -delt
species_2 = [1, 1, 2]
atom_2 = 0
test_structure_1 = struc.Structure(cell, species_2, positions_1)
test_structure_2 = struc.Structure(cell, species_2, positions_2)
test_structure_3 = struc.Structure(cell, species_2, positions_3)
env2_1 = env.AtomicEnvironment(test_structure_1, atom_2, cutoffs)
env2_2 = env.AtomicEnvironment(test_structure_2, atom_2, cutoffs)
env2_3 = env.AtomicEnvironment(test_structure_3, atom_2, cutoffs)
# set hyperparameters
sig1 = random()
ls1 = random()
sig2 = random()
ls2 = random()
d1 = 1
d2 = 2
hyps = np.array([sig1, ls1, sig2, ls2])
# check force kernel
calc1 = en.two_plus_three_en(env1_2, env2_2, hyps, cutoffs)
calc2 = en.two_plus_three_en(env1_3, env2_3, hyps, cutoffs)
calc3 = en.two_plus_three_en(env1_2, env2_3, hyps, cutoffs)
calc4 = en.two_plus_three_en(env1_3, env2_2, hyps, cutoffs)
kern_finite_diff = (calc1 + calc2 - calc3 - calc4) / (4*delt**2)
kern_analytical = en.two_plus_three_body(env1_1, env2_1,
d1, d2, hyps, cutoffs)
tol = 1e-4
assert(np.isclose(kern_finite_diff, kern_analytical, atol=tol))
def test_two_plus_three_body_grad():
# create env 1
cell = np.eye(3)
cutoffs = np.array([1, 1])
positions_1 = [np.array([0., 0., 0.]),
np.array([random(), random(), random()]),
np.array([random(), random(), random()])]
species_1 = [1, 2, 1]
atom_1 = 0
test_structure_1 = struc.Structure(cell, species_1, positions_1)
env1 = env.AtomicEnvironment(test_structure_1, atom_1, cutoffs)
# create env 2
positions_1 = [np.array([0., 0., 0.]),
np.array([random(), random(), random()]),
np.array([random(), random(), random()])]
species_2 = [1, 1, 2]
atom_2 = 0
test_structure_1 = struc.Structure(cell, species_2, positions_1)
env2 = env.AtomicEnvironment(test_structure_1, atom_2, cutoffs)
# set hyperparameters
sig1 = random()
ls1 = random()
sig2 = random()
ls2 = random()
d1 = randint(1, 3)
d2 = randint(1, 3)
delta = 1e-8
hyps = np.array([sig1, ls1, sig2, ls2])
hyps1 = np.array([sig1+delta, ls1, sig2, ls2])
hyps2 = np.array([sig1, ls1+delta, sig2, ls2])
hyps3 = np.array([sig1, ls1, sig2+delta, ls2])
hyps4 = np.array([sig1, ls1, sig2, ls2+delta])
grad_test = en.two_plus_three_body_grad(env1, env2, d1, d2, hyps, cutoffs)
sig1_derv_brute = (en.two_plus_three_body(env1, env2, d1, d2,
hyps1, cutoffs) -
en.two_plus_three_body(env1, env2, d1, d2,
hyps, cutoffs)) / delta
l1_derv_brute = \
(en.two_plus_three_body(env1, env2, d1, d2, hyps2, cutoffs) -
en.two_plus_three_body(env1, env2, d1, d2, hyps, cutoffs)) / delta
sig2_derv_brute = \
(en.two_plus_three_body(env1, env2, d1, d2,
hyps3, cutoffs) -
en.two_plus_three_body(env1, env2, d1, d2,
hyps, cutoffs)) / delta
l2_derv_brute = \
(en.two_plus_three_body(env1, env2, d1, d2, hyps4, cutoffs) -
en.two_plus_three_body(env1, env2, d1, d2, hyps, cutoffs)) / delta
tol = 1e-4
assert(np.isclose(grad_test[1][0], sig1_derv_brute, atol=tol))
assert(np.isclose(grad_test[1][1], l1_derv_brute, atol=tol))
assert(np.isclose(grad_test[1][2], sig2_derv_brute, atol=tol))
assert(np.isclose(grad_test[1][3], l2_derv_brute, atol=tol))
def test_two_plus_three_body_force_en():
"""Check that the analytical force/en kernel matches finite difference of
energy kernel."""
# create env 1
delt = 1e-8
cell = np.eye(3)
cutoffs = np.array([1, 1])
positions_1 = [np.array([0., 0., 0.]),
np.array([random(), random(), random()]),
np.array([random(), random(), random()]),
np.array([random(), random(), random()]),
np.array([random(), random(), random()]),
np.array([random(), random(), random()]),
np.array([random(), random(), random()]),
np.array([random(), random(), random()]),
np.array([random(), random(), random()])]
positions_2 = deepcopy(positions_1)
positions_2[0][0] = delt
species_1 = [1, 2, 1]
atom_1 = 0
test_structure_1 = struc.Structure(cell, species_1, positions_1)
test_structure_2 = struc.Structure(cell, species_1, positions_2)
env1_1 = env.AtomicEnvironment(test_structure_1, atom_1, cutoffs)
env1_2 = env.AtomicEnvironment(test_structure_2, atom_1, cutoffs)
# create env 2
positions_1 = [np.array([0., 0., 0.]),
np.array([random(), random(), random()]),
np.array([random(), random(), random()]),
np.array([random(), random(), random()]),
np.array([random(), random(), random()]),
np.array([random(), random(), random()]),
np.array([random(), random(), random()]),
np.array([random(), random(), random()]),
np.array([random(), random(), random()])]
species_2 = [1, 1, 2]
atom_2 = 0
test_structure_1 = struc.Structure(cell, species_2, positions_1)
env2 = env.AtomicEnvironment(test_structure_1, atom_2, cutoffs)
# set hyperparameters
sig1 = random()
ls1 = random()
sig2 = random()
ls2 = random()
d1 = 1
hyps = np.array([sig1, ls1, sig2, ls2])
# check force kernel
calc1 = en.two_body_en(env1_2, env2, hyps[0:2], cutoffs)
calc2 = en.two_body_en(env1_1, env2, hyps[0:2], cutoffs)
calc3 = en.three_body_en(env1_2, env2, hyps[2:4], cutoffs)
calc4 = en.three_body_en(env1_1, env2, hyps[2:4], cutoffs)
kern_finite_diff = (calc1 - calc2) / (2 * delt) + \
(calc3 - calc4) / (3 * delt)
kern_analytical = \
en.two_plus_three_force_en(env1_1, env2, d1, hyps, cutoffs)
tol = 1e-4
assert(np.isclose(-kern_finite_diff, kern_analytical, atol=tol))
