def test():
"""Guassian/Neural force call.
Checks consistency of pure-python and fortran versions.
"""
images = make_images()
for fortran in [False, True]:
label = 'forcecall/%s' % fortran
calc = Amp(
descriptor=Gaussian(
cutoff=cutoff,
Gs=Gs,
fortran=fortran,
),
model=NeuralNetwork(
hiddenlayers=hiddenlayers,
weights=weights,
scalings=scalings,
activation=activation,
mode='atom-centered',
fprange=fingerprints_range,
fortran=fortran,
),
label=label,
)
if fortran is False:
reference_energies = [
calc.get_potential_energy(image) for image in images
]
else:
predicted_energies = [
calc.get_potential_energy(image) for image in images
]
for image_no in range(len(predicted_energies)):
assert (abs(predicted_energies[image_no] -
reference_energies[image_no]) < 10.**(-5.)), \
'Calculated energy value of image %i by \
fortran version is not consistent with the \
value of python version.' % (image_no + 1)
if fortran is False:
reference_forces = [calc.get_forces(image) for image in images]
else:
predicted_forces = [calc.get_forces(image) for image in images]
for image_no in range(len(predicted_forces)):
for index in range(np.shape(predicted_forces[image_no])[0]):
for k in range(np.shape(predicted_forces[image_no])[1]):
assert (abs(predicted_forces[image_no][index][k] -
reference_forces[image_no][index][k]) <
10.**(-5.)), \
'Calculated %i force of atom %i of \
image %i by fortran version is not \
consistent with the value of python \
version.' % (k, index, image_no + 1)
def train_test():
label = 'train_test_g5/calc'
train_images = generate_data(2)
elements = ['Pt', 'Cu']
G = make_symmetry_functions(elements=elements,
type='G2',
etas=np.logspace(np.log10(0.05),
np.log10(5.),
num=4))
G += make_symmetry_functions(elements=elements,
type='G5',
etas=[0.005],
zetas=[1., 4.],
gammas=[+1., -1.])
G = {element: G for element in elements}
calc = Amp(descriptor=Gaussian(Gs=G),
model=NeuralNetwork(hiddenlayers=(3, 3)),
label=label,
cores=1)
loss = LossFunction(convergence={'energy_rmse': 0.02, 'force_rmse': 0.03})
calc.model.lossfunction = loss
calc.train(images=train_images, )
for image in train_images:
print("energy = %s" % str(calc.get_potential_energy(image)))
print("forces = %s" % str(calc.get_forces(image)))
# Test that we can re-load this calculator and call it again.
del calc
calc2 = Amp.load(label + '.amp')
for image in train_images:
print("energy = %s" % str(calc2.get_potential_energy(image)))
print("forces = %s" % str(calc2.get_forces(image)))
def train_test():
"""Gaussian/KRR train test."""
label = 'train_test/calc'
train_images = generate_data(2)
traj = Trajectory('trainingset.traj', mode='w')
for image in train_images:
traj.write(image)
calc = Amp(descriptor=Gaussian(),
model=KernelRidge(forcetraining=True, trainingimages='trainingset.traj'),
label=label,
cores=1)
calc.train(images=train_images,)
for image in train_images:
print("energy = %s" % str(calc.get_potential_energy(image)))
print("forces = %s" % str(calc.get_forces(image)))
# Test that we can re-load this calculator and call it again.
del calc
calc2 = Amp.load(label + '.amp')
for image in train_images:
print("energy = %s" % str(calc2.get_potential_energy(image)))
print("forces = %s" % str(calc2.get_forces(image)))
def train_data(images, setup_only=False):
label = 'nodeplot_test/calc'
train_images = images
calc = Amp(descriptor=Gaussian(),
model=NeuralNetwork(hiddenlayers=(5, 5)),
label=label,
cores=1)
loss = LossFunction(convergence={'energy_rmse': 0.02, 'force_rmse': 0.02})
calc.model.lossfunction = loss
if not setup_only:
calc.train(images=train_images, )
for image in train_images:
print("energy =", calc.get_potential_energy(image))
print("forces =", calc.get_forces(image))
else:
images = hash_images(train_images)
calc.descriptor.calculate_fingerprints(images=images,
log=calc._log,
parallel={'cores': 1},
calculate_derivatives=False)
calc.model.fit(trainingimages=images,
descriptor=calc.descriptor,
log=calc._log,
parallel={'cores': 1},
only_setup=True)
return calc
def train_data(images, setup_only=False):
label = 'nodeplot_test/calc'
train_images = images
calc = Amp(descriptor=Gaussian(),
model=NeuralNetwork(hiddenlayers=(5, 5)),
label=label,
cores=1)
loss = LossFunction(convergence={'energy_rmse': 0.02,
'force_rmse': 0.02})
calc.model.lossfunction = loss
if not setup_only:
calc.train(images=train_images, )
for image in train_images:
print "energy =", calc.get_potential_energy(image)
print "forces =", calc.get_forces(image)
else:
images = hash_images(train_images)
calc.descriptor.calculate_fingerprints(images=images,
log=calc.log,
cores=1,
calculate_derivatives=False)
calc.model.fit(trainingimages=images,
descriptor=calc.descriptor,
log=calc.log,
cores=1,
only_setup=True)
return calc
def test():
images = make_images()
for fortran in [False, True]:
label = 'forcecall/%s' % fortran
calc = Amp(descriptor=Gaussian(cutoff=cutoff,
Gs=Gs,
fortran=fortran,),
model=NeuralNetwork(hiddenlayers=hiddenlayers,
weights=weights,
scalings=scalings,
activation=activation,
mode='atom-centered',
fprange=fingerprints_range,
fortran=fortran,),
label=label,)
if fortran is False:
reference_energies = [calc.get_potential_energy(image)
for image in images]
else:
predicted_energies = [calc.get_potential_energy(image)
for image in images]
for image_no in range(len(predicted_energies)):
assert (abs(predicted_energies[image_no] -
reference_energies[image_no]) < 10.**(-5.)), \
'Calculated energy value of image %i by \
fortran version is not consistent with the \
value of python version.' % (image_no + 1)
if fortran is False:
reference_forces = [calc.get_forces(image) for image in images]
else:
predicted_forces = [calc.get_forces(image) for image in images]
for image_no in range(len(predicted_forces)):
for index in range(np.shape(predicted_forces[image_no])[0]):
for k in range(np.shape(predicted_forces[image_no])[1]):
assert (abs(predicted_forces[image_no][index][k] -
reference_forces[image_no][index][k]) <
10.**(-5.)), \
'Calculated %i force of atom %i of \
image %i by fortran version is not \
consistent with the value of python \
version.' % (k, index, image_no + 1)
def train_test():
label = 'train_test/calc'
train_images = generate_data(2)
calc = Amp(descriptor=Gaussian(),
model=NeuralNetwork(hiddenlayers=(3, 3)),
label=label,
cores=1)
loss = LossFunction(convergence={'energy_rmse': 0.02,
'force_rmse': 0.02})
calc.model.lossfunction = loss
calc.train(images=train_images,)
for image in train_images:
print "energy =", calc.get_potential_energy(image)
print "forces =", calc.get_forces(image)
def train_test():
"""Gaussian/tflow train test."""
perform, reason = check_perform()
if not perform:
print('Skipping this test because {}.'.format(reason))
return
from amp.model.tflow import NeuralNetwork
label = 'train_test/calc'
train_images = generate_data(2)
convergence = {'energy_rmse': 0.02, 'force_rmse': 0.02}
calc = Amp(descriptor=Gaussian(),
model=NeuralNetwork(hiddenlayers=(3, 3),
convergenceCriteria=convergence),
label=label,
cores=1)
calc.train(images=train_images, )
for image in train_images:
print("energy =", calc.get_potential_energy(image))
print("forces =", calc.get_forces(image))
def train_test():
"""Gaussian/Neural train test."""
label = 'train_test/calc'
train_images = generate_data(2)
calc = Amp(descriptor=Gaussian(),
model=NeuralNetwork(hiddenlayers=(3, 3)),
label=label,
cores=1)
loss = LossFunction(convergence={'energy_rmse': 0.02, 'force_rmse': 0.03})
calc.model.lossfunction = loss
calc.train(images=train_images, )
for image in train_images:
print("energy = %s" % str(calc.get_potential_energy(image)))
print("forces = %s" % str(calc.get_forces(image)))
# Test that we can re-load this calculator and call it again.
del calc
calc2 = Amp.load(label + '.amp')
for image in train_images:
print("energy = %s" % str(calc2.get_potential_energy(image)))
print("forces = %s" % str(calc2.get_forces(image)))
def test_calcs():
"""Gaussian/Neural non-periodic standard.
Checks that the answer matches that expected from previous Mathematica
calculations.
"""
#: Making the list of non-periodic images
images = [
Atoms(
symbols="PdOPd2",
pbc=np.array([False, False, False], dtype=bool),
calculator=EMT(),
cell=np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]),
positions=np.array([[0.0, 0.0, 0.0], [0.0, 2.0, 0.0],
[0.0, 0.0, 3.0], [1.0, 0.0, 0.0]]),
),
Atoms(
symbols="PdOPd2",
pbc=np.array([False, False, False], dtype=bool),
calculator=EMT(),
cell=np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]),
positions=np.array([[0.0, 1.0, 0.0], [1.0, 2.0, 1.0],
[-1.0, 1.0, 2.0], [1.0, 3.0, 2.0]]),
),
Atoms(
symbols="PdO",
pbc=np.array([False, False, False], dtype=bool),
calculator=EMT(),
cell=np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]),
positions=np.array([[2.0, 1.0, -1.0], [1.0, 2.0, 1.0]]),
),
Atoms(
symbols="Pd2O",
pbc=np.array([False, False, False], dtype=bool),
calculator=EMT(),
cell=np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]),
positions=np.array([[-2.0, -1.0, -1.0], [1.0, 2.0, 1.0],
[3.0, 4.0, 4.0]]),
),
Atoms(
symbols="Cu",
pbc=np.array([False, False, False], dtype=bool),
calculator=EMT(),
cell=np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]),
positions=np.array([[0.0, 0.0, 0.0]]),
),
]
[a.get_potential_energy() for a in images]
# Parameters
hiddenlayers = {"O": (2, ), "Pd": (2, ), "Cu": (2, )}
Gs = {}
Gs["G2_etas"] = [0.2]
Gs["G2_rs_s"] = [0]
Gs["G4_etas"] = [0.4]
Gs["G4_zetas"] = [1]
Gs["G4_gammas"] = [1]
Gs["cutoff"] = 6.5
elements = ["O", "Pd", "Cu"]
G = make_symmetry_functions(elements=elements,
type="G2",
etas=Gs["G2_etas"])
G += make_symmetry_functions(
elements=elements,
type="G4",
etas=Gs["G4_etas"],
zetas=Gs["G4_zetas"],
gammas=Gs["G4_gammas"],
)
hashed_images = hash_images(images, Gs)
descriptor = Gaussian(Gs=G, cutoff=Gs["cutoff"])
descriptor.calculate_fingerprints(hashed_images,
calculate_derivatives=True)
fingerprints_range = calculate_fingerprints_range(descriptor,
hashed_images)
weights = OrderedDict([
(
"O",
OrderedDict([
(
1,
np.array([
[0.5, 0.5],
[0.5, 0.5],
[0.5, 0.5],
[0.5, 0.5],
[0.5, 0.5],
[0.5, 0.5],
[0.5, 0.5],
[0.5, 0.5],
[0.5, 0.5],
[0.5, 0.5],
]),
),
(2, np.matrix([[0.5], [0.5], [0.5]])),
]),
),
(
"Pd",
OrderedDict([
(
1,
np.array([
[0.5, 0.5],
[0.5, 0.5],
[0.5, 0.5],
[0.5, 0.5],
[0.5, 0.5],
[0.5, 0.5],
[0.5, 0.5],
[0.5, 0.5],
[0.5, 0.5],
[0.5, 0.5],
]),
),
(2, np.array([[0.5], [0.5], [0.5]])),
]),
),
(
"Cu",
OrderedDict([
(
1,
np.array([
[0.5, 0.5],
[0.5, 0.5],
[0.5, 0.5],
[0.5, 0.5],
[0.5, 0.5],
[0.5, 0.5],
[0.5, 0.5],
[0.5, 0.5],
[0.5, 0.5],
[0.5, 0.5],
]),
),
(2, np.array([[0.5], [0.5], [0.5]])),
]),
),
])
scalings = OrderedDict([
("O", OrderedDict([("intercept", 0), ("slope", 1)])),
("Pd", OrderedDict([("intercept", 0), ("slope", 1)])),
("Cu", OrderedDict([("intercept", 0), ("slope", 1)])),
])
calc = Amp(
descriptor,
model=NeuralNetwork(
hiddenlayers=hiddenlayers,
weights=weights,
scalings=scalings,
activation="linear",
fprange=fingerprints_range,
mode="atom-centered",
fortran=False,
),
logging=False,
)
amp_energies = [calc.get_potential_energy(image) for image in images]
amp_forces = [calc.get_forces(image) for image in images]
amp_forces = np.concatenate(amp_forces)
device = "cpu"
dataset = AtomsDataset(images,
descriptor=DummyGaussian,
cores=1,
label='test',
Gs=Gs,
forcetraining=True)
fp_length = dataset.fp_length
batch_size = len(dataset)
dataloader = DataLoader(dataset,
batch_size,
collate_fn=collate_amp,
shuffle=False)
model = FullNN(elements, [fp_length, 2, 2], device, forcetraining=True)
for name, layer in model.named_modules():
if isinstance(layer, Dense):
layer.activation = None
init.constant_(layer.weight, 0.5)
init.constant_(layer.bias, 0.5)
for batch in dataloader:
input_data = [batch[0], len(batch[1]), batch[3]]
for element in elements:
input_data[0][element][0] = (
input_data[0][element][0].to(device).requires_grad_(True))
fp_primes = batch[4]
energy_pred, force_pred = model(input_data, fp_primes)
for idx, i in enumerate(amp_energies):
assert round(i, 4) == round(
energy_pred.tolist()[idx][0],
4), "The predicted energy of image %i is wrong!" % (idx + 1)
print("Energy predictions are correct!")
for idx, sample in enumerate(amp_forces):
for idx_d, value in enumerate(sample):
predict = force_pred.tolist()[idx][idx_d]
assert abs(value - predict) < 0.00001, (
"The predicted force of image % i, direction % i is wrong! Values: %s vs %s"
% (idx + 1, idx_d, value, force_pred.tolist()[idx][idx_d]))
print("Force predictions are correct!")
def test_calcs():
"""Gaussian/Neural non-periodic standard.
Checks that the answer matches that expected from previous Mathematica
calculations.
"""
#: Making the list of non-periodic images
images = [
Atoms(
symbols="PdOPd2",
pbc=np.array([False, False, False], dtype=bool),
calculator=EMT(),
cell=np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]),
positions=np.array([[0.0, 0.0, 0.0], [0.0, 2.0, 0.0],
[0.0, 0.0, 3.0], [1.0, 0.0, 0.0]]),
),
Atoms(
symbols="PdOPd2",
pbc=np.array([False, False, False], dtype=bool),
calculator=EMT(),
cell=np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]),
positions=np.array([[0.0, 1.0, 0.0], [1.0, 2.0, 1.0],
[-1.0, 1.0, 2.0], [1.0, 3.0, 2.0]]),
),
Atoms(
symbols="PdO",
pbc=np.array([False, False, False], dtype=bool),
calculator=EMT(),
cell=np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]),
positions=np.array([[2.0, 1.0, -1.0], [1.0, 2.0, 1.0]]),
),
Atoms(
symbols="Pd2O",
pbc=np.array([False, False, False], dtype=bool),
calculator=EMT(),
cell=np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]),
positions=np.array([[-2.0, -1.0, -1.0], [1.0, 2.0, 1.0],
[3.0, 4.0, 4.0]]),
),
Atoms(
symbols="Cu",
pbc=np.array([False, False, False], dtype=bool),
calculator=EMT(),
cell=np.array([[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 1.0]]),
positions=np.array([[0.0, 0.0, 0.0]]),
),
]
# Parameters
hiddenlayers = {"O": (2, ), "Pd": (2, ), "Cu": (2, )}
Gs = {}
Gs["G2_etas"] = [0.2]
Gs["G2_rs_s"] = [0]
Gs["G4_etas"] = [0.4]
Gs["G4_zetas"] = [1]
Gs["G4_gammas"] = [1]
Gs["cutoff"] = 6.5
elements = ["O", "Pd", "Cu"]
G = make_symmetry_functions(elements=elements,
type="G2",
etas=Gs["G2_etas"])
G += make_symmetry_functions(
elements=elements,
type="G4",
etas=Gs["G4_etas"],
zetas=Gs["G4_zetas"],
gammas=Gs["G4_gammas"],
)
amp_images = amp_hash(images)
descriptor = Gaussian(Gs=G, cutoff=Gs["cutoff"])
descriptor.calculate_fingerprints(amp_images, calculate_derivatives=True)
fingerprints_range = calculate_fingerprints_range(descriptor, amp_images)
np.random.seed(1)
O_weights_1 = np.random.rand(10, 2)
O_weights_2 = np.random.rand(1, 3).reshape(-1, 1)
np.random.seed(2)
Pd_weights_1 = np.random.rand(10, 2)
Pd_weights_2 = np.random.rand(1, 3).reshape(-1, 1)
np.random.seed(3)
Cu_weights_1 = np.random.rand(10, 2)
Cu_weights_2 = np.random.rand(1, 3).reshape(-1, 1)
weights = OrderedDict([
("O", OrderedDict([(1, O_weights_1), (2, O_weights_2)])),
("Pd", OrderedDict([(1, Pd_weights_1), (2, Pd_weights_2)])),
("Cu", OrderedDict([(1, Cu_weights_1), (2, Cu_weights_2)])),
])
scalings = OrderedDict([
("O", OrderedDict([("intercept", 0), ("slope", 1)])),
("Pd", OrderedDict([("intercept", 0), ("slope", 1)])),
("Cu", OrderedDict([("intercept", 0), ("slope", 1)])),
])
calc = Amp(
descriptor,
model=NeuralNetwork(
hiddenlayers=hiddenlayers,
weights=weights,
scalings=scalings,
activation="tanh",
fprange=fingerprints_range,
mode="atom-centered",
fortran=False,
),
logging=False,
)
amp_energies = [calc.get_potential_energy(image) for image in images]
amp_forces = [calc.get_forces(image) for image in images]
amp_forces = np.concatenate(amp_forces)
torch_O_weights_1 = torch.FloatTensor(O_weights_1[:-1, :]).t()
torch_O_bias_1 = torch.FloatTensor(O_weights_1[-1, :])
torch_O_weights_2 = torch.FloatTensor(O_weights_2[:-1, :]).t()
torch_O_bias_2 = torch.FloatTensor(O_weights_2[-1, :])
torch_Pd_weights_1 = torch.FloatTensor(Pd_weights_1[:-1, :]).t()
torch_Pd_bias_1 = torch.FloatTensor(Pd_weights_1[-1, :])
torch_Pd_weights_2 = torch.FloatTensor(Pd_weights_2[:-1, :]).t()
torch_Pd_bias_2 = torch.FloatTensor(Pd_weights_2[-1, :])
torch_Cu_weights_1 = torch.FloatTensor(Cu_weights_1[:-1, :]).t()
torch_Cu_bias_1 = torch.FloatTensor(Cu_weights_1[-1, :])
torch_Cu_weights_2 = torch.FloatTensor(Cu_weights_2[:-1, :]).t()
torch_Cu_bias_2 = torch.FloatTensor(Cu_weights_2[-1, :])
device = "cpu"
dataset = AtomsDataset(
images,
descriptor=Gaussian,
cores=1,
label="consistency",
Gs=Gs,
forcetraining=True,
)
fp_length = dataset.fp_length
batch_size = len(dataset)
dataloader = DataLoader(dataset,
batch_size,
collate_fn=collate_amp,
shuffle=False)
model = FullNN(elements, [fp_length, 2, 2], device, forcetraining=True)
model.state_dict()["elementwise_models.O.model_net.0.weight"].copy_(
torch_O_weights_1)
model.state_dict()["elementwise_models.O.model_net.0.bias"].copy_(
torch_O_bias_1)
model.state_dict()["elementwise_models.O.model_net.2.weight"].copy_(
torch_O_weights_2)
model.state_dict()["elementwise_models.O.model_net.2.bias"].copy_(
torch_O_bias_2)
model.state_dict()["elementwise_models.Pd.model_net.0.weight"].copy_(
torch_Pd_weights_1)
model.state_dict()["elementwise_models.Pd.model_net.0.bias"].copy_(
torch_Pd_bias_1)
model.state_dict()["elementwise_models.Pd.model_net.2.weight"].copy_(
torch_Pd_weights_2)
model.state_dict()["elementwise_models.Pd.model_net.2.bias"].copy_(
torch_Pd_bias_2)
model.state_dict()["elementwise_models.Cu.model_net.0.weight"].copy_(
torch_Cu_weights_1)
model.state_dict()["elementwise_models.Cu.model_net.0.bias"].copy_(
torch_Cu_bias_1)
model.state_dict()["elementwise_models.Cu.model_net.2.weight"].copy_(
torch_Cu_weights_2)
model.state_dict()["elementwise_models.Cu.model_net.2.bias"].copy_(
torch_Cu_bias_2)
import torch.nn as nn
for name, layer in model.named_modules():
if isinstance(layer, MLP):
layer.model_net = nn.Sequential(layer.model_net, Tanh())
for batch in dataloader:
x = to_tensor(batch[0], device)
y = to_tensor(batch[1], device)
energy_pred, force_pred = model(x)
for idx, i in enumerate(amp_energies):
assert round(i, 4) == round(
energy_pred.tolist()[idx][0],
4), "The predicted energy of image %i is wrong!" % (idx + 1)
print("Energy predictions are correct!")
for idx, sample in enumerate(amp_forces):
for idx_d, value in enumerate(sample):
predict = force_pred.tolist()[idx][idx_d]
assert abs(value - predict) < 0.0001, (
"The predicted force of image % i, direction % i is wrong! Values: %s vs %s"
% (idx + 1, idx_d, value, force_pred.tolist()[idx][idx_d]))
print("Force predictions are correct!")
def non_periodic_test():
"""Gaussian/tflowNeural non-periodic."""
perform, reason = check_perform()
if not perform:
print('Skipping this test because {}'.format(reason))
return
from amp.model.tflow import NeuralNetwork
# Making the list of non-periodic images
images = [Atoms(symbols='PdOPd2',
pbc=np.array([False, False, False], dtype=bool),
cell=np.array(
[[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]]),
positions=np.array(
[[0., 0., 0.],
[0., 2., 0.],
[0., 0., 3.],
[1., 0., 0.]])),
Atoms(symbols='PdOPd2',
pbc=np.array([False, False, False], dtype=bool),
cell=np.array(
[[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]]),
positions=np.array(
[[0., 1., 0.],
[1., 2., 1.],
[-1., 1., 2.],
[1., 3., 2.]])),
Atoms(symbols='PdO',
pbc=np.array([False, False, False], dtype=bool),
cell=np.array(
[[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]]),
positions=np.array(
[[2., 1., -1.],
[1., 2., 1.]])),
Atoms(symbols='Pd2O',
pbc=np.array([False, False, False], dtype=bool),
cell=np.array(
[[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]]),
positions=np.array(
[[-2., -1., -1.],
[1., 2., 1.],
[3., 4., 4.]])),
Atoms(symbols='Cu',
pbc=np.array([False, False, False], dtype=bool),
cell=np.array(
[[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]]),
positions=np.array(
[[0., 0., 0.]]))]
# Correct energies and forces
correct_energies = [14.231186811226152, 14.327219917287948,
5.5742510565528285, 9.41456771216968,
-0.5019297954597407]
correct_forces = \
[[[-0.05095024246182649, -0.10709193432146558, -0.09734321482638622],
[-0.044550772904033635, 0.2469763195486647, -0.07617425912869778],
[-0.02352490951707703, -0.050782839419131864, 0.24409220250631508],
[0.11902592488293715, -0.08910154580806727, -0.07057472855123109]],
[[-0.024868720575099375, -0.07417891957113862,
-0.12121240797223251],
[0.060156158438252574, 0.017517013378773042,
-0.020047135079325505],
[-0.10901144291312388, -0.06671262448352767, 0.06581556263014315],
[0.07372400504997068, 0.12337453067589325, 0.07544398042141486]],
[[0.10151747265164626, -0.10151747265164626, -0.20303494530329252],
[-0.10151747265164626, 0.10151747265164626, 0.20303494530329252]],
[[-0.00031177673224312745, -0.00031177673224312745,
-0.0002078511548287517],
[0.004823209772264884, 0.004823209772264884,
0.006975000714861393],
[-0.004511433040021756, -0.004511433040021756,
-0.006767149560032641]],
[[0.0, 0.0, 0.0]]]
# Parameters
Gs = {'O': [{'type': 'G2', 'element': 'Pd', 'eta': 0.8},
{'type': 'G4', 'elements': [
'Pd', 'Pd'], 'eta':0.2, 'gamma':0.3, 'zeta':1},
{'type': 'G4', 'elements': ['O', 'Pd'], 'eta':0.3, 'gamma':0.6,
'zeta':0.5}],
'Pd': [{'type': 'G2', 'element': 'Pd', 'eta': 0.2},
{'type': 'G4', 'elements': ['Pd', 'Pd'],
'eta':0.9, 'gamma':0.75, 'zeta':1.5},
{'type': 'G4', 'elements': ['O', 'Pd'], 'eta':0.4,
'gamma':0.3, 'zeta':4}],
'Cu': [{'type': 'G2', 'element': 'Cu', 'eta': 0.8},
{'type': 'G4', 'elements': ['Cu', 'O'],
'eta':0.2, 'gamma':0.3, 'zeta':1},
{'type': 'G4', 'elements': ['Cu', 'Cu'], 'eta':0.3,
'gamma':0.6, 'zeta':0.5}]}
hiddenlayers = {'O': (2, 1), 'Pd': (2, 1), 'Cu': (2, 1)}
weights = OrderedDict([('O', OrderedDict([(1, np.matrix([[-2.0, 6.0],
[3.0, -3.0],
[1.5, -0.9],
[-2.5, -1.5]])),
(2, np.matrix([[5.5],
[3.6],
[1.4]]))])),
('Pd', OrderedDict([(1, np.matrix([[-1.0, 3.0],
[2.0, 4.2],
[1.0, -0.7],
[-3.0, 2.0]])),
(2, np.matrix([[4.0],
[0.5],
[3.0]]))])),
('Cu', OrderedDict([(1, np.matrix([[0.0, 1.0],
[-1.0, -2.0],
[2.5, -1.9],
[-3.5, 0.5]])),
(2, np.matrix([[0.5],
[1.6],
[-1.4]]))]))])
scalings = OrderedDict([('O', OrderedDict([('intercept', -2.3),
('slope', 4.5)])),
('Pd', OrderedDict([('intercept', 1.6),
('slope', 2.5)])),
('Cu', OrderedDict([('intercept', -0.3),
('slope', -0.5)]))])
fingerprints_range = {"Cu": np.array([[0.0, 0.0], [0.0, 0.0], [0.0, 0.0]]),
"O": np.array([[0.2139617720858539,
2.258090276328769],
[0.0, 1.085656080548734],
[0.0, 0.0]]),
"Pd": np.array([[0.0, 1.4751761770313006],
[0.0, 0.28464992134267897],
[0.0, 0.20167521020630502]])}
# Testing pure-python and fortran versions of Gaussian-neural force call
for fortran in [False, True]:
for cores in range(1, 6):
label = 'call-nonperiodic/%s-%i' % (fortran, cores)
calc = Amp(descriptor=Gaussian(cutoff=6.5,
Gs=Gs,
fortran=fortran),
model=NeuralNetwork(hiddenlayers=hiddenlayers,
weights=weights,
scalings=scalings,
activation='sigmoid',
fprange=fingerprints_range),
label=label,
dblabel=label,
cores=cores)
predicted_energies = [calc.get_potential_energy(image) for image in
images]
for image_no in range(len(predicted_energies)):
print(predicted_energies[image_no])
print(correct_energies[image_no])
diff = abs(predicted_energies[image_no] -
correct_energies[image_no])
assert (diff < 10.**(-3.)), \
'The predicted energy of image %i is wrong!' % (
image_no + 1)
predicted_forces = [calc.get_forces(image) for image in images]
for image_no in range(len(predicted_forces)):
print('predicted forces:')
print(predicted_forces[image_no])
print('correct forces:')
print(np.array(correct_forces[image_no]))
for index in range(np.shape(predicted_forces[image_no])[0]):
for direction in range(
np.shape(predicted_forces[image_no])[1]):
diff = abs(predicted_forces[image_no][index][
direction] -
correct_forces[image_no][index][direction])
assert (diff < 10.**(-3.)), \
'The predicted %i force of atom %i of image %i ' \
'is wrong!' % (direction, index, image_no + 1)
def periodic_test():
# Making the list of periodic images
images = [Atoms(symbols='PdOPd',
pbc=np.array([True, False, False], dtype=bool),
cell=np.array(
[[2., 0., 0.],
[0., 2., 0.],
[0., 0., 2.]]),
positions=np.array(
[[0.5, 1., 0.5],
[1., 0.5, 1.],
[1.5, 1.5, 1.5]])),
Atoms(symbols='PdO',
pbc=np.array([True, True, False], dtype=bool),
cell=np.array(
[[2., 0., 0.],
[0., 2., 0.],
[0., 0., 2.]]),
positions=np.array(
[[0.5, 1., 0.5],
[1., 0.5, 1.]])),
Atoms(symbols='Cu',
pbc=np.array([True, True, False], dtype=bool),
cell=np.array(
[[1.8, 0., 0.],
[0., 1.8, 0.],
[0., 0., 1.8]]),
positions=np.array(
[[0., 0., 0.]]))]
# Correct energies and forces
correct_energies = [3.8560954326995978, 1.6120748520627273,
0.19433107801410093]
correct_forces = \
[[[0.14747720528015523, -3.3010645563584973, 3.3008168318984463],
[0.03333579762326405, 9.050780376599887, -0.42608278400777605],
[-0.1808130029034193, -5.7497158202413905, -2.8747340478906698]],
[[6.5035267996045045 * (10.**(-6.)),
-6.503526799604495 * (10.**(-6.)),
0.00010834689201069249],
[-6.5035267996045045 * (10.**(-6.)),
6.503526799604495 * (10.**(-6.)),
-0.00010834689201069249]],
[[0.0, 0.0, 0.0]]]
# Parameters
Gs = {'O': [{'type': 'G2', 'element': 'Pd', 'eta': 0.8},
{'type': 'G4', 'elements': ['O', 'Pd'], 'eta':0.3, 'gamma':0.6,
'zeta':0.5}],
'Pd': [{'type': 'G2', 'element': 'Pd', 'eta': 0.2},
{'type': 'G4', 'elements': ['Pd', 'Pd'],
'eta':0.9, 'gamma':0.75, 'zeta':1.5}],
'Cu': [{'type': 'G2', 'element': 'Cu', 'eta': 0.8},
{'type': 'G4', 'elements': ['Cu', 'Cu'], 'eta':0.3,
'gamma':0.6, 'zeta':0.5}]}
hiddenlayers = {'O': (2,), 'Pd': (2,), 'Cu': (2,)}
weights = OrderedDict([('O', OrderedDict([(1, np.matrix([[-2.0, 6.0],
[3.0, -3.0],
[1.5, -0.9]])),
(2, np.matrix([[5.5],
[3.6],
[1.4]]))])),
('Pd', OrderedDict([(1, np.matrix([[-1.0, 3.0],
[2.0, 4.2],
[1.0, -0.7]])),
(2, np.matrix([[4.0],
[0.5],
[3.0]]))])),
('Cu', OrderedDict([(1, np.matrix([[0.0, 1.0],
[-1.0, -2.0],
[2.5, -1.9]])),
(2, np.matrix([[0.5],
[1.6],
[-1.4]]))]))])
scalings = OrderedDict([('O', OrderedDict([('intercept', -2.3),
('slope', 4.5)])),
('Pd', OrderedDict([('intercept', 1.6),
('slope', 2.5)])),
('Cu', OrderedDict([('intercept', -0.3),
('slope', -0.5)]))])
fingerprints_range = {"Cu": np.array([[2.8636310860653253,
2.8636310860653253],
[1.5435994865298275,
1.5435994865298275]]),
"O": np.array([[2.9409056366723028,
2.972494902604392],
[1.9522542722823606,
4.0720361595017245]]),
"Pd": np.array([[2.4629488092411096,
2.6160138774087125],
[0.27127576524253594,
0.5898312261433813]])}
# Testing pure-python and fortran versions of Gaussian-neural force call
for fortran in [False, True]:
for cores in range(1, 4):
label = 'call-periodic/%s-%i' % (fortran, cores)
calc = Amp(descriptor=Gaussian(cutoff=4.,
Gs=Gs,
fortran=fortran),
model=NeuralNetwork(hiddenlayers=hiddenlayers,
weights=weights,
scalings=scalings,
activation='tanh',
fprange=fingerprints_range,
mode='atom-centered',
fortran=fortran),
label=label,
dblabel=label,
cores=cores)
predicted_energies = [calc.get_potential_energy(image) for image in
images]
for image_no in range(len(predicted_energies)):
diff = abs(predicted_energies[image_no] -
correct_energies[image_no])
assert (diff < 10.**(-14.)), \
'The predicted energy of image %i is wrong!' % (
image_no + 1)
predicted_forces = [calc.get_forces(image) for image in images]
for image_no in range(len(predicted_forces)):
for index in range(np.shape(predicted_forces[image_no])[0]):
for direction in range(
np.shape(predicted_forces[image_no])[1]):
diff = abs(predicted_forces[image_no][index][
direction] -
correct_forces[image_no][index][direction])
assert (diff < 10.**(-11.)), \
'The predicted %i force of atom %i of image' \
' %i is wrong!' % (direction,
index,
image_no + 1)
def test():
###########################################################################
# Parameters
weights = \
OrderedDict([(1, np.array([[0.14563579, 0.19176385],
[-0.01991609, 0.35873379],
[-0.27988951, 0.03490866],
[0.19195185, 0.43116313],
[0.41035737, 0.02617128],
[-0.13235187, -0.23112657],
[-0.29065111, 0.23865951],
[0.05854897, 0.24249052],
[0.13660673, 0.19288898],
[0.31894165, -0.41831075],
[-0.23522261, -0.24009372],
[-0.14450575, -0.15275409],
[0., 0.]])),
(2, np.array([[-0.27415999],
[0.28538579],
[0.]])),
(3, np.array([[0.32147131],
[0.]]))])
scalings = OrderedDict([('intercept', 3.), ('slope', 2.)])
images = generate_images()
###########################################################################
# Testing pure-python and fortran versions of CartesianNeural on different
# number of processes
for fortran in [False, True]:
calc = Amp(
descriptor=None,
regression=NeuralNetwork(hiddenlayers=(2, 1),
weights=weights,
scalings=scalings,
activation='tanh'),
fortran=fortran,
)
predicted_energies = [
calc.get_potential_energy(image) for image in images
]
for image_no in range(len(predicted_energies)):
assert (abs(predicted_energies[image_no] -
correct_predicted_energies[image_no]) <
5 * 10.**(-10.)), \
'The calculated energy of image %i is wrong!' % (image_no + 1)
predicted_forces = [calc.get_forces(image) for image in images]
for image_no in range(len(predicted_forces)):
for index in range(np.shape(predicted_forces[image_no])[0]):
for direction in range(3):
assert (abs(predicted_forces[image_no][index][direction] -
correct_predicted_forces[image_no][index]
[direction]) < 5 * 10.**(-10.)), \
'The calculated %i force of atom %i of image %i is' \
'wrong!' % (direction, index, image_no + 1)
def test():
"""Gaussian/Neural numeric-analytic consistency."""
images = generate_data()
regressor = Regressor(optimizer='BFGS')
_G = make_symmetry_functions(type='G2',
etas=[0.05, 5.],
elements=['Cu', 'Pt'])
_G += make_symmetry_functions(type='G4',
etas=[0.005],
zetas=[1., 4.],
gammas=[1.],
elements=['Cu', 'Pt'])
Gs = {'Cu': _G, 'Pt': _G}
calc = Amp(descriptor=Gaussian(Gs=Gs),
model=NeuralNetwork(
hiddenlayers=(2, 1),
regressor=regressor,
randomseed=42,
),
cores=1)
step = 0
for d in [None, 0.00001]:
for fortran in [True, False]:
for cores in [1, 2]:
step += 1
label = \
'numeric_analytic_test/analytic-%s-%i' % (fortran, cores) \
if d is None \
else 'numeric_analytic_test/numeric-%s-%i' \
% (fortran, cores)
print(label)
loss = LossFunction(convergence={
'energy_rmse': 10**10,
'force_rmse': 10**10
},
d=d)
calc.set_label(label)
calc.dblabel = 'numeric_analytic_test/analytic-True-1'
calc.model.lossfunction = loss
calc.descriptor.fortran = fortran
calc.model.fortran = fortran
calc.cores = cores
calc.train(images=images, )
if step == 1:
ref_energies = []
ref_forces = []
for image in images:
ref_energies += [calc.get_potential_energy(image)]
ref_forces += [calc.get_forces(image)]
ref_dloss_dparameters = \
calc.model.lossfunction.dloss_dparameters
else:
energies = []
forces = []
for image in images:
energies += [calc.get_potential_energy(image)]
forces += [calc.get_forces(image)]
dloss_dparameters = \
calc.model.lossfunction.dloss_dparameters
for image_no in range(2):
diff = abs(energies[image_no] - ref_energies[image_no])
assert (diff < 10.**(-13.)), \
'The calculated value of energy of image %i is ' \
'wrong!' % (image_no + 1)
for atom_no in range(len(images[0])):
for i in range(3):
diff = abs(forces[image_no][atom_no][i] -
ref_forces[image_no][atom_no][i])
assert (diff < 10.**(-10.)), \
'The calculated %i force of atom %i of ' \
'image %i is wrong!' \
% (i, atom_no, image_no + 1)
# Checks analytical and numerical dloss_dparameters
for _ in range(len(ref_dloss_dparameters)):
diff = abs(dloss_dparameters[_] -
ref_dloss_dparameters[_])
assert(diff < 10 ** (-10.)), \
'The calculated value of loss function ' \
'derivative is wrong!'
# Checks analytical and numerical forces
forces = []
for image in images:
image.set_calculator(calc)
forces += [calc.calculate_numerical_forces(image, d=d)]
for atom_no in range(len(images[0])):
for i in range(3):
diff = abs(forces[image_no][atom_no][i] -
ref_forces[image_no][atom_no][i])
print('{:3d} {:1d} {:7.1e}'.format(atom_no, i, diff))
assert (diff < 10.**(-6.)), \
'The calculated %i force of atom %i of ' \
'image %i is wrong! (Diff = %f)' \
% (i, atom_no, image_no + 1, diff)
def periodic_test():
"""Gaussian/tflowNeural periodic."""
perform, reason = check_perform()
if not perform:
print('Skipping this test because {}'.format(reason))
return
from amp.model.tflow import NeuralNetwork
# Making the list of periodic images
images = [Atoms(symbols='PdOPd',
pbc=np.array([True, False, False], dtype=bool),
cell=np.array(
[[2., 0., 0.],
[0., 2., 0.],
[0., 0., 2.]]),
positions=np.array(
[[0.5, 1., 0.5],
[1., 0.5, 1.],
[1.5, 1.5, 1.5]])),
Atoms(symbols='PdO',
pbc=np.array([True, True, False], dtype=bool),
cell=np.array(
[[2., 0., 0.],
[0., 2., 0.],
[0., 0., 2.]]),
positions=np.array(
[[0.5, 1., 0.5],
[1., 0.5, 1.]])),
Atoms(symbols='Cu',
pbc=np.array([True, True, False], dtype=bool),
cell=np.array(
[[1.8, 0., 0.],
[0., 1.8, 0.],
[0., 0., 1.8]]),
positions=np.array(
[[0., 0., 0.]]))]
# Correct energies and forces
correct_energies = [3.8560954326995978, 1.6120748520627273,
0.19433107801410093]
correct_forces = \
[[[0.14747720528015523, -3.3010645563584973, 3.3008168318984463],
[0.03333579762326405, 9.050780376599887, -0.42608278400777605],
[-0.1808130029034193, -5.7497158202413905, -2.8747340478906698]],
[[6.5035267996045045 * (10.**(-6.)),
-6.503526799604495 * (10.**(-6.)),
0.00010834689201069249],
[-6.5035267996045045 * (10.**(-6.)),
6.503526799604495 * (10.**(-6.)),
-0.00010834689201069249]],
[[0.0, 0.0, 0.0]]]
# Parameters
Gs = {'O': [{'type': 'G2', 'element': 'Pd', 'eta': 0.8},
{'type': 'G4', 'elements': ['O', 'Pd'], 'eta':0.3, 'gamma':0.6,
'zeta':0.5}],
'Pd': [{'type': 'G2', 'element': 'Pd', 'eta': 0.2},
{'type': 'G4', 'elements': ['Pd', 'Pd'],
'eta':0.9, 'gamma':0.75, 'zeta':1.5}],
'Cu': [{'type': 'G2', 'element': 'Cu', 'eta': 0.8},
{'type': 'G4', 'elements': ['Cu', 'Cu'], 'eta':0.3,
'gamma':0.6, 'zeta':0.5}]}
hiddenlayers = {'O': (2, 1), 'Pd': (2, 1), 'Cu': (2, 1)}
weights = OrderedDict([('O', OrderedDict([(1, np.matrix([[-2.0, 6.0],
[3.0, -3.0],
[1.5, -0.9]])),
(2, np.matrix([[5.5],
[3.6],
[1.4]]))])),
('Pd', OrderedDict([(1, np.matrix([[-1.0, 3.0],
[2.0, 4.2],
[1.0, -0.7]])),
(2, np.matrix([[4.0],
[0.5],
[3.0]]))])),
('Cu', OrderedDict([(1, np.matrix([[0.0, 1.0],
[-1.0, -2.0],
[2.5, -1.9]])),
(2, np.matrix([[0.5],
[1.6],
[-1.4]]))]))])
scalings = OrderedDict([('O', OrderedDict([('intercept', -2.3),
('slope', 4.5)])),
('Pd', OrderedDict([('intercept', 1.6),
('slope', 2.5)])),
('Cu', OrderedDict([('intercept', -0.3),
('slope', -0.5)]))])
fingerprints_range = {"Cu": np.array([[2.8636310860653253,
2.8636310860653253],
[1.5435994865298275,
1.5435994865298275]]),
"O": np.array([[2.9409056366723028,
2.972494902604392],
[1.9522542722823606,
4.0720361595017245]]),
"Pd": np.array([[2.4629488092411096,
2.6160138774087125],
[0.27127576524253594,
0.5898312261433813]])}
# Testing pure-python and fortran versions of Gaussian-neural force call
for fortran in [False, True]:
for cores in range(1, 4):
label = 'call-periodic/%s-%i' % (fortran, cores)
calc = Amp(descriptor=Gaussian(cutoff=4.,
Gs=Gs,
fortran=fortran),
model=NeuralNetwork(hiddenlayers=hiddenlayers,
weights=weights,
scalings=scalings,
activation='tanh',
fprange=fingerprints_range,
unit_type="double"),
label=label,
dblabel=label,
cores=cores)
predicted_energies = [calc.get_potential_energy(image) for image in
images]
for image_no in range(len(predicted_energies)):
print(predicted_energies[image_no])
print(correct_energies[image_no])
diff = abs(predicted_energies[image_no] -
correct_energies[image_no])
assert (diff < 10.**(-14.)), \
'The predicted energy of image %i is wrong!' % (
image_no + 1)
predicted_forces = [calc.get_forces(image) for image in images]
for image_no in range(len(predicted_forces)):
print('predicted forces:')
print(predicted_forces[image_no])
print('correct forces:')
print(np.array(correct_forces[image_no]))
for index in range(np.shape(predicted_forces[image_no])[0]):
for direction in range(
np.shape(predicted_forces[image_no])[1]):
diff = abs(predicted_forces[image_no][index][
direction] -
correct_forces[image_no][index][direction])
assert (diff < 10.**(-11.)), \
'The predicted %i force of atom %i of image' \
' %i is wrong!' % (direction,
index,
image_no + 1)
def test():
images = generate_data(2)
regressor = Regressor(optimizer='BFGS')
calc = Amp(descriptor=Gaussian(),
model=NeuralNetwork(hiddenlayers=(3, 3),
regressor=regressor,),
cores=1)
step = 0
for d in [None, 0.00001]:
for fortran in [True, False]:
for cores in [1, 2]:
step += 1
label = \
'numeric_analytic_test/analytic-%s-%i' % (fortran, cores) \
if d is None \
else 'numeric_analytic_test/numeric-%s-%i' \
% (fortran, cores)
print(label)
loss = LossFunction(convergence={'energy_rmse': 10 ** 10,
'force_rmse': 10 ** 10},
d=d)
calc.set_label(label)
calc.dblabel = 'numeric_analytic_test/analytic-True-1'
calc.model.lossfunction = loss
calc.descriptor.fortran = fortran
calc.model.fortran = fortran
calc.cores = cores
calc.train(images=images,)
if step == 1:
ref_energies = []
ref_forces = []
for image in images:
ref_energies += [calc.get_potential_energy(image)]
ref_forces += [calc.get_forces(image)]
ref_dloss_dparameters = \
calc.model.lossfunction.dloss_dparameters
else:
energies = []
forces = []
for image in images:
energies += [calc.get_potential_energy(image)]
forces += [calc.get_forces(image)]
dloss_dparameters = \
calc.model.lossfunction.dloss_dparameters
for image_no in range(2):
diff = abs(energies[image_no] - ref_energies[image_no])
assert (diff < 10.**(-13.)), \
'The calculated value of energy of image %i is ' \
'wrong!' % (image_no + 1)
for atom_no in range(6):
for i in range(3):
diff = abs(forces[image_no][atom_no][i] -
ref_forces[image_no][atom_no][i])
assert (diff < 10.**(-10.)), \
'The calculated %i force of atom %i of ' \
'image %i is wrong!' \
% (i, atom_no, image_no + 1)
# Checks analytical and numerical dloss_dparameters
for _ in range(len(ref_dloss_dparameters)):
diff = abs(dloss_dparameters[_] -
ref_dloss_dparameters[_])
assert(diff < 10 ** (-10.)), \
'The calculated value of loss function ' \
'derivative is wrong!'
# Checks analytical and numerical forces
forces = []
for image in images:
image.set_calculator(calc)
forces += [calc.calculate_numerical_forces(image, d=d)]
for atom_no in range(6):
for i in range(3):
diff = abs(forces[image_no][atom_no][i] -
ref_forces[image_no][atom_no][i])
assert (diff < 10.**(-9.)), \
'The calculated %i force of atom %i of ' \
'image %i is wrong!' % (i, atom_no, image_no + 1)
def non_periodic_test():
# Making the list of non-periodic images
images = [Atoms(symbols='PdOPd2',
pbc=np.array([False, False, False], dtype=bool),
cell=np.array(
[[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]]),
positions=np.array(
[[0., 0., 0.],
[0., 2., 0.],
[0., 0., 3.],
[1., 0., 0.]])),
Atoms(symbols='PdOPd2',
pbc=np.array([False, False, False], dtype=bool),
cell=np.array(
[[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]]),
positions=np.array(
[[0., 1., 0.],
[1., 2., 1.],
[-1., 1., 2.],
[1., 3., 2.]])),
Atoms(symbols='PdO',
pbc=np.array([False, False, False], dtype=bool),
cell=np.array(
[[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]]),
positions=np.array(
[[2., 1., -1.],
[1., 2., 1.]])),
Atoms(symbols='Pd2O',
pbc=np.array([False, False, False], dtype=bool),
cell=np.array(
[[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]]),
positions=np.array(
[[-2., -1., -1.],
[1., 2., 1.],
[3., 4., 4.]])),
Atoms(symbols='Cu',
pbc=np.array([False, False, False], dtype=bool),
cell=np.array(
[[1., 0., 0.],
[0., 1., 0.],
[0., 0., 1.]]),
positions=np.array(
[[0., 0., 0.]]))]
# Correct energies and forces
correct_energies = [14.231186811226152, 14.327219917287948,
5.5742510565528285, 9.41456771216968,
-0.5019297954597407]
correct_forces = \
[[[-0.05095024246182649, -0.10709193432146558, -0.09734321482638622],
[-0.044550772904033635, 0.2469763195486647, -0.07617425912869778],
[-0.02352490951707703, -0.050782839419131864, 0.24409220250631508],
[0.11902592488293715, -0.08910154580806727, -0.07057472855123109]],
[[-0.024868720575099375, -0.07417891957113862,
-0.12121240797223251],
[0.060156158438252574, 0.017517013378773042,
-0.020047135079325505],
[-0.10901144291312388, -0.06671262448352767, 0.06581556263014315],
[0.07372400504997068, 0.12337453067589325, 0.07544398042141486]],
[[0.10151747265164626, -0.10151747265164626, -0.20303494530329252],
[-0.10151747265164626, 0.10151747265164626, 0.20303494530329252]],
[[-0.00031177673224312745, -0.00031177673224312745,
-0.0002078511548287517],
[0.004823209772264884, 0.004823209772264884,
0.006975000714861393],
[-0.004511433040021756, -0.004511433040021756,
-0.006767149560032641]],
[[0.0, 0.0, 0.0]]]
# Parameters
Gs = {'O': [{'type': 'G2', 'element': 'Pd', 'eta': 0.8},
{'type': 'G4', 'elements': [
'Pd', 'Pd'], 'eta':0.2, 'gamma':0.3, 'zeta':1},
{'type': 'G4', 'elements': ['O', 'Pd'], 'eta':0.3, 'gamma':0.6,
'zeta':0.5}],
'Pd': [{'type': 'G2', 'element': 'Pd', 'eta': 0.2},
{'type': 'G4', 'elements': ['Pd', 'Pd'],
'eta':0.9, 'gamma':0.75, 'zeta':1.5},
{'type': 'G4', 'elements': ['O', 'Pd'], 'eta':0.4,
'gamma':0.3, 'zeta':4}],
'Cu': [{'type': 'G2', 'element': 'Cu', 'eta': 0.8},
{'type': 'G4', 'elements': ['Cu', 'O'],
'eta':0.2, 'gamma':0.3, 'zeta':1},
{'type': 'G4', 'elements': ['Cu', 'Cu'], 'eta':0.3,
'gamma':0.6, 'zeta':0.5}]}
hiddenlayers = {'O': (2,), 'Pd': (2,), 'Cu': (2,)}
weights = OrderedDict([('O', OrderedDict([(1, np.matrix([[-2.0, 6.0],
[3.0, -3.0],
[1.5, -0.9],
[-2.5, -1.5]])),
(2, np.matrix([[5.5],
[3.6],
[1.4]]))])),
('Pd', OrderedDict([(1, np.matrix([[-1.0, 3.0],
[2.0, 4.2],
[1.0, -0.7],
[-3.0, 2.0]])),
(2, np.matrix([[4.0],
[0.5],
[3.0]]))])),
('Cu', OrderedDict([(1, np.matrix([[0.0, 1.0],
[-1.0, -2.0],
[2.5, -1.9],
[-3.5, 0.5]])),
(2, np.matrix([[0.5],
[1.6],
[-1.4]]))]))])
scalings = OrderedDict([('O', OrderedDict([('intercept', -2.3),
('slope', 4.5)])),
('Pd', OrderedDict([('intercept', 1.6),
('slope', 2.5)])),
('Cu', OrderedDict([('intercept', -0.3),
('slope', -0.5)]))])
fingerprints_range = {"Cu": np.array([[0.0, 0.0], [0.0, 0.0], [0.0, 0.0]]),
"O": np.array([[0.2139617720858539,
2.258090276328769],
[0.0, 1.085656080548734],
[0.0, 0.0]]),
"Pd": np.array([[0.0, 1.4751761770313006],
[0.0, 0.28464992134267897],
[0.0, 0.20167521020630502]])}
# Testing pure-python and fortran versions of Gaussian-neural force call
for fortran in [False, True]:
for cores in range(1, 6):
label = 'call-nonperiodic/%s-%i' % (fortran, cores)
calc = Amp(descriptor=Gaussian(cutoff=6.5,
Gs=Gs,
fortran=fortran),
model=NeuralNetwork(hiddenlayers=hiddenlayers,
weights=weights,
scalings=scalings,
activation='sigmoid',
fprange=fingerprints_range,
mode='atom-centered',
fortran=fortran),
label=label,
dblabel=label,
cores=cores)
predicted_energies = [calc.get_potential_energy(image) for image in
images]
for image_no in range(len(predicted_energies)):
diff = abs(predicted_energies[image_no] -
correct_energies[image_no])
assert (diff < 10.**(-15.)), \
'The predicted energy of image %i is wrong!' % (
image_no + 1)
predicted_forces = [calc.get_forces(image) for image in images]
for image_no in range(len(predicted_forces)):
for index in range(np.shape(predicted_forces[image_no])[0]):
for direction in range(
np.shape(predicted_forces[image_no])[1]):
diff = abs(predicted_forces[image_no][index][
direction] -
correct_forces[image_no][index][direction])
assert (diff < 10.**(-15.)), \
'The predicted %i force of atom %i of image %i ' \
'is wrong!' % (direction, index, image_no + 1)
