def train_images(images, HL, E_conv):
Hidden_Layer=tuple(HL)
calc = Amp(descriptor=Gaussian(), model=NeuralNetwork(hiddenlayers=Hidden_Layer))
calc.model.lossfunction = LossFunction(convergence={'energy_rmse': E_conv})
#calc.model.lossfunction = LossFunction(force_coefficient=-0.1)
calc.train(images=images, overwrite=True)
from mlutils.neb import accelerate_neb
from amp import Amp
from amp.descriptor.gaussian import Gaussian
from amp.model.neuralnetwork import NeuralNetwork
from amp.model import LossFunction
from ase.calculators.emt import EMT
initial = 'initial.traj'
final = 'final.traj'
Gs = None
n = 5
cutoff = 6.5
amp_calc = Amp(descriptor=Gaussian(cutoff=cutoff, fortran=True, Gs=Gs),
model=NeuralNetwork(hiddenlayers=(n, n),
fortran=True,
checkpoints=None))
convergence = {'energy_rmse': 0.0001, 'force_rmse': 0.01}
amp_calc.model.lossfunction = LossFunction(convergence=convergence)
dft_calc = EMT()
neb = accelerate_neb(initial=initial,
final=final,
tolerance=0.05,
maxiter=200,
fmax=0.05,
ifmax=1.,
step=2.,
metric='fmax')
#!/usr/bin/python # mute from amp import Amp amp = Amp() amp.pwm.mute()
def train_rnn(baseframe=100,
tframe=8,
total_step=10,
traj='ethane.traj',
convergence={
'energy_rmse': 0.25,
'force_rmse': 0.5
},
elements=['C', 'H', 'O'],
hiddenlayers=(64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64),
optim='ADAM',
cores=4):
"""Gaussian/tflow train test."""
p = ple()
label = 'amp'
all_images = Trajectory(traj)
nimg, mean_e = get_mean_energy(all_images)
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} # Gs=G
if not isfile('amp.amp'):
print('\nset up calculator ...\n')
calc = Amp(descriptor=Gaussian(mode='atom-centered', Gs=G),
model=NeuralNetwork(hiddenlayers=hiddenlayers,
convergenceCriteria=convergence,
activation='tanh',
energy_coefficient=1.0,
force_coefficient=None,
optimizationMethod=optim,
parameters={'energyMeanScale': mean_e},
maxTrainingEpochs=100000),
label=label,
cores=cores) # 'l-BFGS-b' or 'ADAM'
trained_images = [all_images[j] for j in range(0, baseframe)]
calc.train(overwrite=True, images=trained_images)
del calc
else:
calc = Amp.load('amp.amp')
calc.model.parameters['convergence'] = convergence
calc.model.lossfunction = LossFunction(convergence=convergence)
trained_images = [all_images[j] for j in range(0, baseframe)]
calc.train(overwrite=True, images=trained_images)
del calc
tstep = int((nimg - baseframe) / tframe)
if total_step > tstep:
total_step = tstep
print('Max train cycle of %d is allowed.' % total_step)
edfts, eamps, eamps_ = [], [], []
dolabel = True
basestep = int(baseframe / tframe)
for step in range(basestep, total_step + basestep):
new_images = [
all_images[j]
for j in range(0 + step * tframe, tframe + step * tframe)
]
trained_images.extend(new_images)
x, edft, eamp, eamp_ = [], [], [], []
ii = step * tframe
# ----- test -----
calc1 = Amp.load('amp.amp')
for i, image in enumerate(new_images):
x.append(ii)
eamp_.append(calc1.get_potential_energy(image))
eamps_.append(eamp_[-1])
edft.append(image.get_potential_energy())
edfts.append(edft[-1])
ii += 1
del calc1
# ----- train -----
calc = Amp.load('amp.amp')
calc.model.lossfunction = LossFunction(convergence=convergence)
# calc.model.convergenceCriteria=convergence
calc.train(overwrite=True, images=trained_images)
del calc
# ----- test -----
calc2 = Amp.load('amp.amp')
print('\n---- current training result ---- \n')
for i, image in enumerate(new_images):
eamp.append(calc2.get_potential_energy(image))
eamps.append(eamp[-1])
print("energy(AMP) = %f energy(DFT) = %f" % (eamp[-1], edft[i]))
# print("forces = %s" % str(calc2.get_forces(image)))
del calc2
plot_energies(edfts, eamps, eamp_=None)
system('epstopdf energies.eps')
p.scatter(x, edft, eamp, eamp_, dolabel=dolabel)
if dolabel:
dolabel = False
p.plot()
system('epstopdf energies_scatter.eps')
# -*- coding: utf-8 -*-
"""
Created on Sun Apr 12 21:54:34 2020
@author: srava
"""
from ase.io import read
from ase.io.trajectory import Trajectory
import numpy as np
from ase.calculators.emt import EMT
from amp import Amp
from amp.descriptor.gaussian import Gaussian
from amp.utilities import hash_images
from amp.model.neuralnetwork import NeuralNetwork
import matplotlib.pyplot as plt
calc = Amp(descriptor=Gaussian(), model=NeuralNetwork(),
label='calc')
calc.model.lossfunction.parameters['convergence'].update(
{'energy_rmse': 0.05,})
calc.train(images='training_data.traj')
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 train_images(images):
calc = Amp(descriptor=Gaussian(), model=NeuralNetwork(hiddenlayers=(10, 10, 10)))
calc.model.lossfunction = LossFunction(convergence={'energy_rmse': 0.001})
calc.model.lossfunction = LossFunction(force_coefficient=-0.1)
calc.train(images=images, overwrite=True)
#!/usr/bin/env python
from amp import Amp
from amp.descriptor import *
from amp.regression import *
calc = Amp(label="./",
descriptor=Behler(cutoff=6.0),
regression=NeuralNetwork(hiddenlayers=(2, 3)))
calc.train(
"../train.db", # The training data
cores=1,
global_search=None, # not found the simulated annealing feature useful
extend_variables=False) # feature does not work properly and will crash
def test():
pwd = os.getcwd()
os.mkdir(os.path.join(pwd, 'consistnone'))
images = generate_images()
count = 0
for global_search in [None, 'SA']:
for fortran in [False, True]:
for extend_variables in [False, True]:
for data_format in ['db', 'json']:
for save_memory in [False]:
for cores in range(1, 7):
string = 'consistnone/%s-%s-%s-%s-%s-%i'
label = string % (global_search, fortran,
extend_variables, data_format,
save_memory, cores)
if global_search is 'SA':
global_search = \
SimulatedAnnealing(temperature=10, steps=5)
calc = Amp(descriptor=None,
regression=NeuralNetwork(
hiddenlayers=(2, 1),
activation='tanh',
weights=weights,
scalings=scalings,
),
fortran=fortran,
label=label)
calc.train(images=images,
energy_goal=10.**10.,
force_goal=10.**10.,
cores=cores,
data_format=data_format,
save_memory=save_memory,
global_search=global_search,
extend_variables=extend_variables)
if count == 0:
reference_cost_function = calc.cost_function
reference_energy_rmse = \
calc.energy_per_atom_rmse
reference_force_rmse = calc.force_rmse
ref_cost_fxn_variable_derivatives = \
calc.der_variables_cost_function
else:
assert (abs(calc.cost_function -
reference_cost_function) <
10.**(-10.)), \
'''Cost function value for %r fortran, %r
data format, %r save_memory, and %i cores is
not consistent with the value of python version
on single core.''' % (fortran, data_format,
save_memory, cores)
assert (abs(calc.energy_per_atom_rmse -
reference_energy_rmse) <
10.**(-10.)), \
'''Energy rmse value for %r fortran, %r data
format, %r save_memory, and %i cores is not
consistent with the value of python version on
single core.''' % (fortran, data_format,
save_memory, cores)
assert (abs(calc.force_rmse -
reference_force_rmse) < 10.**(-10.)), \
'''Force rmse value for %r fortran, %r data
format, %r save_memory, and %i cores is not
consistent with the value of python version on
single core.''' % (fortran, data_format,
save_memory, cores)
for _ in range(
len(ref_cost_fxn_variable_derivatives)):
assert (calc.der_variables_cost_function[_] -
ref_cost_fxn_variable_derivatives[_] <
10.**(-10.))
'''Derivative of the cost function for %r
fortran, %r data format, %r save_memory, and %i
cores is not consistent with the value of
python version on single
core. ''' % (fortran, data_format, save_memory,
cores)
count = count + 1
def test():
images = make_training_images()
for descriptor in [None, Gaussian()]:
for global_search in [
None, SimulatedAnnealing(temperature=10, steps=5)
]:
for data_format in ['json', 'db']:
for save_memory in [
False,
]:
for fortran in [False, True]:
for cores in range(1, 4):
print(descriptor, global_search, data_format,
save_memory, fortran, cores)
pwd = os.getcwd()
testdir = 'read_write_test'
os.mkdir(testdir)
os.chdir(testdir)
regression = NeuralNetwork(hiddenlayers=(
5,
5,
))
calc = Amp(
label='calc',
descriptor=descriptor,
regression=regression,
fortran=fortran,
)
# Should start with new variables
calc.train(
images,
energy_goal=0.01,
force_goal=10.,
global_search=global_search,
extend_variables=True,
data_format=data_format,
save_memory=save_memory,
cores=cores,
)
# Test that we cannot overwrite. (Strange code
# here because we *want* it to raise an
# exception...)
try:
calc.train(
images,
energy_goal=0.01,
force_goal=10.,
global_search=global_search,
extend_variables=True,
data_format=data_format,
save_memory=save_memory,
cores=cores,
)
except IOError:
pass
else:
raise RuntimeError(
'Code allowed to overwrite!')
# Test that we can manually overwrite.
# Should start with existing variables
calc.train(
images,
energy_goal=0.01,
force_goal=10.,
global_search=global_search,
extend_variables=True,
data_format=data_format,
save_memory=save_memory,
overwrite=True,
cores=cores,
)
label = 'testdir'
if not os.path.exists(label):
os.mkdir(label)
# New directory calculator.
calc = Amp(
label='testdir/calc',
descriptor=descriptor,
regression=regression,
fortran=fortran,
)
# Should start with new variables
calc.train(
images,
energy_goal=0.01,
force_goal=10.,
global_search=global_search,
extend_variables=True,
data_format=data_format,
save_memory=save_memory,
cores=cores,
)
# Open existing, save under new name.
calc = Amp(
load='calc',
label='calc2',
descriptor=descriptor,
regression=regression,
fortran=fortran,
)
# Should start with existing variables
calc.train(
images,
energy_goal=0.01,
force_goal=10.,
global_search=global_search,
extend_variables=True,
data_format=data_format,
save_memory=save_memory,
cores=cores,
)
label = 'calc_new'
if not os.path.exists(label):
os.mkdir(label)
# Change label and re-train
calc.set_label('calc_new/calc')
# Should start with existing variables
calc.train(
images,
energy_goal=0.01,
force_goal=10.,
global_search=global_search,
extend_variables=True,
data_format=data_format,
save_memory=save_memory,
cores=cores,
)
# Open existing without specifying new name.
calc = Amp(
load='calc',
descriptor=descriptor,
regression=regression,
fortran=fortran,
)
# Should start with existing variables
calc.train(
images,
energy_goal=0.01,
force_goal=10.,
global_search=global_search,
extend_variables=True,
data_format=data_format,
save_memory=save_memory,
cores=cores,
)
os.chdir(pwd)
shutil.rmtree(testdir, ignore_errors=True)
del calc, regression
images = [atoms]
g2_etas = [0.005]
g2_rs_s = [0] * 4
g4_etas = [0.005]
g4_zetas = [1., 4.]
g4_gammas = [1., -1.]
cutoff = 4
make_amp_descriptors_simple_nn(images, g2_etas, g2_rs_s, g4_etas, g4_zetas,
g4_gammas, cutoff)
G = make_symmetry_functions(elements=elements, type='G2', etas=g2_etas)
#Add Rs parameter (0.0 for default) to comply with my version of AMP
#for g in G:
# g['Rs'] = 0.0
G += make_symmetry_functions(elements=elements,
type='G4',
etas=g4_etas,
zetas=g4_zetas,
gammas=g4_gammas)
calc = Amp(descriptor=Gaussian(Gs=G, cutoff=4.),
cores=ncores,
model=NeuralNetwork(hiddenlayers=hiddenlayers))
calc.model.lossfunction = LossFunction(convergence=convergence,
force_coefficient=0.001)
calc.train(images=images)
#import os
#from ase import Atoms, Atom, units
#import ase.io
from amp import Amp
from amp.descriptor.gaussian import Gaussian
from amp.model.neuralnetwork import NeuralNetwork
from amp.model import LossFunction
calc = Amp(descriptor=Gaussian(),
model=NeuralNetwork(hiddenlayers=(10, 10, 10)))
calc.model.lossfunction = LossFunction(convergence={
'energy_rmse': 0.02,
'force_rmse': 0.03
})
calc.train(images='geoopt_LCAO.traj')
def train_amp(baseframe=200,
traj='ethane.traj',
convergence={
'energy_rmse': 0.25,
'force_rmse': 0.5
},
elements=['C', 'H', 'O'],
cores=4):
"""Gaussian/tflow train test."""
p = ple()
label = 'amp'
all_images = Trajectory(traj)
nimg, mean_e = get_mean_energy(all_images)
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} # Gs=G
if not isfile('amp.amp'):
# print('\nset up calculator ...\n')
calc = Amp(descriptor=Gaussian(mode='atom-centered', Gs=G),
model=NeuralNetwork(hiddenlayers=(1024, 1024, 1024, 512,
512, 256, 256, 256, 256,
128, 128),
convergenceCriteria=convergence,
activation='tanh',
energy_coefficient=1.0,
force_coefficient=None,
optimizationMethod='ADAM',
parameters={'energyMeanScale': mean_e},
maxTrainingEpochs=100000),
label=label,
cores=cores) # 'l-BFGS-b' or 'ADAM'
trained_images = [all_images[j] for j in range(0, baseframe)]
calc.train(overwrite=True, images=trained_images)
del calc
else:
calc = Amp.load('amp.amp')
calc.model.parameters['convergence'] = convergence
calc.model.lossfunction = LossFunction(convergence=convergence)
trained_images = [all_images[j] for j in range(0, baseframe)]
calc.train(overwrite=True, images=trained_images)
del calc
edfts, eamps, eamps_ = [], [], []
dolabel = True
basestep = int(baseframe / tframe)
system('epstopdf energies.eps')
p.scatter(x, edft, eamp, eamp_, dolabel=dolabel)
p.plot()
plot_energies(edfts, eamps, eamp_=eamps_)
system('epstopdf energies_scatter.eps')
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():
###########################################################################
# 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 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)
{"type":"G2", "element":"Pd", "eta":400., "Rs":4.0},
{"type":"G2", "element":"Pd", "eta":400., "Rs":5.0},
{"type":"G2", "element":"Pd", "eta":400., "Rs":6.0},
{"type":"G4", "elements":["Pd", "Pd"],"eta":0.005, "gamma":-1.0, "zeta":100.0, "theta_s":-1.0},
{"type":"G4", "elements":["Pd", "Pd"],"eta":0.005, "gamma":-1.0, "zeta":100.0, "theta_s":-1.5},
{"type":"G4", "elements":["Pd", "Pd"],"eta":0.005, "gamma":-1.0, "zeta":100.0, "theta_s":-1.75},
{"type":"G4", "elements":["Pd", "Pd"],"eta":0.005, "gamma":-1.0, "zeta":100.0, "theta_s":-2.0},
{"type":"G4", "elements":["Pd", "Pd"],"eta":0.005, "gamma":-1.0, "zeta":300.0, "theta_s":-2.15},
{"type":"G4", "elements":["Pd", "Pd"],"eta":0.005, "gamma":-1.0, "zeta":300.0, "theta_s":-2.25},
{"type":"G4", "elements":["Pd", "Pd"],"eta":0.005, "gamma":1.0, "zeta":160.0, "theta_s":0.0},
{"type":"G4", "elements":["Pd", "Pd"],"eta":0.005, "gamma":1.0, "zeta":320.0, "theta_s":0.25},
{"type":"G4", "elements":["Pd", "Pd"],"eta":0.005, "gamma":1.0, "zeta":640.0, "theta_s":0.35},
{"type":"G4", "elements":["Pd", "Pd"],"eta":0.005, "gamma":1.0, "zeta":160.0, "theta_s":0.5},
{"type":"G4", "elements":["Pd", "Pd"],"eta":0.005, "gamma":1.0, "zeta":160.0, "theta_s":0.75},
]}
calc = Amp(descriptor=Gaussian(Gs=Gs,
cutoff=Cosine(6.0)
),
cores=24,
model=NeuralNetwork(hiddenlayers=(50,), activation='sigmoid',
maxTrainingEpochs=20000,
energy_coefficient=1.0,
force_coefficient=0.1,
optimizationMethod='l-BFGS-b',
convergenceCriteria={'energy_rmse': 0.05,
'force_rmse': 0.05}))
calc.train(images='train.traj',overwrite=True)
def test():
"""Displaced atom test."""
###########################################################################
# Parameters
atoms = 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.]]))
###########################################################################
# 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
}]
}
hiddenlayers = {'O': (2, ), 'Pd': (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]]))]))
])
scalings = OrderedDict([
('O', OrderedDict([('intercept', -2.3), ('slope', 4.5)])),
('Pd', OrderedDict([('intercept', 1.6), ('slope', 2.5)]))
])
fingerprints_range = {
"O":
np.array([[0.21396177208585404, 2.258090276328769],
[0.0, 2.1579067008202975], [0.0, 0.0]]),
"Pd":
np.array([[0.0, 1.4751761770313006], [0.0, 0.697686078889583],
[0.0, 0.37848964715610417]])
}
###########################################################################
calc = Amp(descriptor=Gaussian(
cutoff=6.5,
Gs=Gs,
fortran=False,
),
model=NeuralNetwork(hiddenlayers=hiddenlayers,
weights=weights,
scalings=scalings,
fprange=fingerprints_range,
mode='atom-centered'),
cores=1)
atoms.set_calculator(calc)
e1 = atoms.get_potential_energy(apply_constraint=False)
e2 = calc.get_potential_energy(atoms)
f1 = atoms.get_forces(apply_constraint=False)
atoms[0].x += 0.5
boolean = atoms.calc.calculation_required(atoms, properties=['energy'])
e3 = atoms.get_potential_energy(apply_constraint=False)
e4 = calc.get_potential_energy(atoms)
f2 = atoms.get_forces(apply_constraint=False)
assert (e1 == e2 and e3 == e4 and abs(e1 - e3) > 10.**(-3.)
and (boolean is True)
and (not (f1 == f2).all())), 'Displaced-atom test broken!'
def test():
pwd = os.getcwd()
os.mkdir(os.path.join(pwd, 'rotation_test'))
for descriptor in [Gaussian(), Bispectrum(jmax=2.), Zernike(nmax=5)]:
pwd = os.getcwd()
os.mkdir(os.path.join(pwd, 'rotation_test/before_rot'))
os.mkdir(os.path.join(pwd, 'rotation_test/after_rot'))
# Non-rotated atomic configuration
atoms = Atoms([
Atom('Pt', (0., 0., 0.)),
Atom('Pt', (0., 0., 1.)),
Atom('Pt', (0., 2., 1.))
])
atoms.set_constraint(FixAtoms(indices=[0]))
atoms.set_calculator(EMT())
atoms.get_potential_energy()
atoms.get_forces(apply_constraint=False)
calc = Amp(descriptor=descriptor,
fortran=False,
label='rotation_test/before_rot/')
calc.train(images=[atoms],
force_goal=None,
energy_goal=10.**10.,
extend_variables=False,
global_search=None,
data_format='json')
###########################################################################
# Randomly Rotated (and translated) atomic configuration
rot = [random.random(), random.random(), random.random()]
for i in range(1, len(atoms)):
(atoms[i].x, atoms[i].y,
atoms[i].z) = rotate_atom(atoms[i].x, atoms[i].y, atoms[i].z,
rot[0] * np.pi, rot[1] * np.pi,
rot[2] * np.pi)
disp = [random.random(), random.random(), random.random()]
for atom in atoms:
atom.x += disp[0]
atom.y += disp[1]
atom.z += disp[2]
calc = Amp(descriptor=descriptor,
fortran=False,
label='rotation_test/after_rot/')
calc.train(images=[atoms],
force_goal=None,
energy_goal=10.**10.,
extend_variables=False,
global_search=None,
data_format='json')
###########################################################################
fp1 = paropen('rotation_test/before_rot/fingerprints.json', 'rb')
nonrotated_data = json.load(fp1)
fp2 = paropen('rotation_test/after_rot/fingerprints.json', 'rb')
rotated_data = json.load(fp2)
for hash1, hash2 in zip(nonrotated_data.keys(), rotated_data.keys()):
for index in nonrotated_data[hash1]:
for _ in range(len(nonrotated_data[hash1][index])):
assert abs(nonrotated_data[hash1][index][_] -
rotated_data[hash2][index][_]) < 10.**(-7.)
shutil.rmtree('rotation_test/before_rot')
shutil.rmtree('rotation_test/after_rot')
def test():
"""Guassian/Neural training.
Checks consistency of pure-python and fortran versions.
"""
images = make_images()
convergence = {'energy_rmse': 10.**10.,
'energy_maxresid': 10.**10.,
'force_rmse': 10.**10.,
'force_maxresid': 10.**10., }
regressor = Regressor(optimizer='BFGS')
count = 0
for fortran in [False, True]:
for cores in range(1, 2):
string = 'consistgauss/%s-%i'
label = string % (fortran, cores)
calc = Amp(descriptor=Gaussian(cutoff=cutoff,
Gs=Gs,
fortran=fortran,),
model=NeuralNetwork(hiddenlayers=hiddenlayers,
weights=weights,
scalings=scalings,
activation=activation,
fprange=fingerprints_range,
regressor=regressor,),
label=label,
cores=1)
lossfunction = LossFunction(convergence=convergence)
calc.model.lossfunction = lossfunction
calc.train(images=images,)
if count == 0:
ref_loss = calc.model.lossfunction.loss
ref_energy_loss = calc.model.lossfunction.energy_loss
ref_force_loss = calc.model.lossfunction.force_loss
ref_dloss_dparameters = \
calc.model.lossfunction.dloss_dparameters
else:
assert (abs(calc.model.lossfunction.loss -
ref_loss) < 10.**(-10.)), \
'''Loss function value for %r fortran, and %i cores is
not consistent with the value of python version
on single core.''' % (fortran, cores)
assert (abs(calc.model.lossfunction.energy_loss -
ref_energy_loss) <
10.**(-9.)), \
'''Energy rmse value for %r fortran, and %i cores is not
consistent with the value of python version on
single core.''' % (fortran, cores)
assert (abs(calc.model.lossfunction.force_loss -
ref_force_loss) <
10.**(-9.)), \
'''Force rmse value for %r fortran, and %i cores is not
consistent with the value of python version on
single core.''' % (fortran, cores)
for _ in range(len(ref_dloss_dparameters)):
assert (calc.model.lossfunction.dloss_dparameters[_] -
ref_dloss_dparameters[_] < 10.**(-10.))
'''Derivative of the cost function for %r
fortran, and %i
cores is not consistent with the value of
python version on single
core. ''' % (fortran, cores)
count = count + 1
def periodic_0th_bfgs_step_test():
"""Gaussian/Neural training periodic standard test.
Compares results to that expected from separate mathematica
calculations.
"""
# Making the list of 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.]]))
]
for image in images:
image.set_calculator(EMT())
image.get_potential_energy(apply_constraint=False)
image.get_forces(apply_constraint=False)
# 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)]))
])
# Correct values
if aseversion < 12: # EMT values have changed from 3.12.0 version
ref_loss = 8004.292841411172
ref_energyloss = (43.7360019403031**2.) * 3
ref_forceloss = (137.40994760947325**2.) * 3
ref_dloss_dparameters = np.array([
0.08141668748130322, 0.03231235582925534, 0.04388650395738586,
0.017417514465922313, 0.028431276597563077, 0.011283700608814465,
0.0941695726576061, -0.12322258890990219, 0.12679918754154568,
63.53960075374332, 0.01624770019548904, -86.6263955859162,
-0.01777752828707744, 86.22415217526024, 0.017745913074496918,
104.58358033298292, -96.73280209888215, -99.09843648905876,
-8.302880631972338, -1.2590007162074357, 8.302877346883133,
1.25875988418134, -8.302866610678247, -1.2563833805675353,
28.324298392680998, 28.093155094726413, -29.37874455931869,
-11.247473567044866, 11.119951466664787, -87.08582317481387,
-20.939485239182346, -125.73267675705365, -35.138524407482116
])
else:
ref_loss = 8004.287750978173
ref_energyloss = (43.73598563177581**2.) * 3
ref_forceloss = (137.409923023214**2.) * 3
ref_dloss_dparameters = np.array([
0.08141663280688925, 0.03231233413027478, 0.043886474485922956,
0.01741750276939638, 0.02843125750487539, 0.011283693031378718,
0.09416950941914284, -0.12322250616122936, 0.1267991023910503,
63.53958764057119, 0.016247696749304368, -86.62637753054923,
-0.01777752451341436, 86.22413420485914, 0.01774590930723711,
104.58353326982777, -96.73275667196937, -99.09839026204304,
-8.302877823431269, -1.2590002903842232, 8.302874538343092,
1.2587594584335775, -8.302863802141216, -1.2563829555383859,
28.32428881173613, 28.093145591893936, -29.37873462156934,
-11.24746601393696, 11.11994399919284, -87.08579155328007,
-20.93947792122797, -125.73262989900473, -35.13850819392253
])
# Testing pure-python and fortran versions of Gaussian-neural on different
# number of processes
for fortran in [False, True]:
for cores in range(1, 4):
label = 'train-periodic/%s-%i' % (fortran, cores)
print(label)
calc = Amp(descriptor=Gaussian(
cutoff=4.,
Gs=Gs,
fortran=fortran,
),
model=NeuralNetwork(
hiddenlayers=hiddenlayers,
weights=weights,
scalings=scalings,
activation='tanh',
regressor=regressor,
fortran=fortran,
),
label=label,
dblabel=label,
cores=cores)
lossfunction = LossFunction(convergence=convergence)
calc.model.lossfunction = lossfunction
calc.train(images=images, )
diff = abs(calc.model.lossfunction.loss - ref_loss)
print("diff at 414 =", diff)
assert (diff < 10.**(-10.)), \
'Calculated value of loss function is wrong!'
diff = abs(calc.model.lossfunction.energy_loss - ref_energyloss)
assert (diff < 10.**(-10.)), \
'Calculated value of energy per atom RMSE is wrong!'
diff = abs(calc.model.lossfunction.force_loss - ref_forceloss)
assert (diff < 10 ** (-9.)), \
'Calculated value of force RMSE is wrong!'
for _ in range(len(ref_dloss_dparameters)):
diff = abs(calc.model.lossfunction.dloss_dparameters[_] -
ref_dloss_dparameters[_])
assert(diff < 10 ** (-10.)), \
'Calculated value of loss function derivative is wrong!'
dblabel = label
secondlabel = '_' + label
calc = Amp(descriptor=Gaussian(cutoff=4., Gs=Gs, fortran=fortran),
model=NeuralNetwork(
hiddenlayers=hiddenlayers,
weights=weights,
scalings=scalings,
activation='tanh',
regressor=regressor,
fortran=fortran,
),
label=secondlabel,
dblabel=dblabel,
cores=cores)
lossfunction = LossFunction(convergence=convergence)
calc.model.lossfunction = lossfunction
calc.train(images=images, )
diff = abs(calc.model.lossfunction.loss - ref_loss)
assert (diff < 10.**(-10.)), \
'Calculated value of loss function is wrong!'
diff = abs(calc.model.lossfunction.energy_loss - ref_energyloss)
assert (diff < 10.**(-10.)), \
'Calculated value of energy per atom RMSE is wrong!'
diff = abs(calc.model.lossfunction.force_loss - ref_forceloss)
assert (diff < 10 ** (-9.)), \
'Calculated value of force RMSE is wrong!'
for _ in range(len(ref_dloss_dparameters)):
diff = abs(calc.model.lossfunction.dloss_dparameters[_] -
ref_dloss_dparameters[_])
assert(diff < 10 ** (-10.)), \
'Calculated value of loss function derivative is wrong!'
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 non_periodic_0th_bfgs_step_test():
"""Gaussian/Neural training non-periodic standard test.
Compares results to that expected from separate mathematica
calculations.
"""
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.]]))
]
for image in images:
image.set_calculator(EMT())
image.get_potential_energy(apply_constraint=False)
image.get_forces(apply_constraint=False)
# 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)]))
])
# Correct values
if aseversion < 12: # EMT values have changed from 3.12.0 version
ref_loss = 7144.8107853579895
ref_energyloss = (24.318837496016506**2.) * 5
ref_forceloss = (144.70282477494519**2.) * 5
ref_dloss_dparameters = np.array([
0, 0, 0, 0, 0, 0, 0.01374139170953901, 0.36318423812749656,
0.028312691567496464, 0.6012336354445753, 0.9659002689921986,
-1.289777005924742, -0.5718960934643078, -2.642566722179569,
-1.196039924610482, 0, 0, -2.72563797131018, -0.9080181024866707,
-0.7739948323226851, -0.29157894253717415, -2.0599829042717404,
-0.6156374289895887, -0.006086517460749253, -0.829678548408266,
0.0008092646745710161, 0.04161302703491613, 0.0034264690790135606,
-0.957800456897051, -0.006281929606579444, -0.2883588477371198,
-4.245777410962108, -4.3174120941045535, -8.02385959091948,
-3.240512651984099, -27.289862194988853, -26.8177742762544,
-82.45107056051073, -80.68167683508715
])
ref_energy_maxresid = 54.21915548269209
ref_force_maxresid = 791.6736436232306
else:
ref_loss = 7144.807220773296
ref_energyloss = (24.318829702548342**2.) * 5
ref_forceloss = (144.70279593472887**2.) * 5
ref_dloss_dparameters = np.array([
0, 0, 0, 0, 0, 0, 0.01374139170953901, 0.36318423812749656,
0.028312691567496464, 0.6012336354445753, 0.9659002689921986,
-1.2897765357544038, -0.5718958286530584, -2.642565840915077,
-1.1960394346870424, 0, 0, -2.7256370964673238,
-0.9080177898160631, -0.7739945904033205, -0.29157882294526083,
-2.0599825024556027, -0.6156371996742152, -0.006086514109432934,
-0.8296782839032163, 0.0008092653341775424, 0.04161306816722683,
0.0034264692325982156, -0.9578001030483714, -0.006281927374160914,
-0.28835874344086, -4.245775886469167, -4.317410633818672,
-8.02385959091948, -3.240512651984099, -27.289853042932705,
-26.81776520493048, -82.45104200076496, -80.68164887277251
])
ref_energy_maxresid = 54.21913802238612
ref_force_maxresid = 791.6734866205463
# Testing pure-python and fortran versions of Gaussian-neural on different
# number of processes
for fortran in [False, True]:
for cores in range(1, 6):
label = 'train-nonperiodic/%s-%i' % (fortran, cores)
print(label)
calc = Amp(descriptor=Gaussian(
cutoff=6.5,
Gs=Gs,
fortran=fortran,
),
model=NeuralNetwork(
hiddenlayers=hiddenlayers,
weights=weights,
scalings=scalings,
activation='sigmoid',
regressor=regressor,
fortran=fortran,
),
label=label,
dblabel=label,
cores=cores)
lossfunction = LossFunction(convergence=convergence)
calc.model.lossfunction = lossfunction
calc.train(images=images, )
diff = abs(calc.model.lossfunction.loss - ref_loss)
print("diff at 204 =", diff)
assert (diff < 10.**(-10.)), \
'Calculated value of loss function is wrong!'
diff = abs(calc.model.lossfunction.energy_loss - ref_energyloss)
assert (diff < 10.**(-10.)), \
'Calculated value of energy per atom RMSE is wrong!'
diff = abs(calc.model.lossfunction.force_loss - ref_forceloss)
assert (diff < 10 ** (-10.)), \
'Calculated value of force RMSE is wrong!'
diff = abs(calc.model.lossfunction.energy_maxresid -
ref_energy_maxresid)
assert (diff < 10.**(-10.)), \
'Calculated value of energy per atom max residual is wrong!'
diff = abs(calc.model.lossfunction.force_maxresid -
ref_force_maxresid)
assert (diff < 10 ** (-10.)), \
'Calculated value of force max residual is wrong!'
for _ in range(len(ref_dloss_dparameters)):
diff = abs(calc.model.lossfunction.dloss_dparameters[_] -
ref_dloss_dparameters[_])
assert(diff < 10 ** (-12.)), \
"Calculated value of loss function derivative is wrong!"
dblabel = label
secondlabel = '_' + label
calc = Amp(descriptor=Gaussian(
cutoff=6.5,
Gs=Gs,
fortran=fortran,
),
model=NeuralNetwork(
hiddenlayers=hiddenlayers,
weights=weights,
scalings=scalings,
activation='sigmoid',
regressor=regressor,
fortran=fortran,
),
label=secondlabel,
dblabel=dblabel,
cores=cores)
lossfunction = LossFunction(convergence=convergence)
calc.model.lossfunction = lossfunction
calc.train(images=images, )
diff = abs(calc.model.lossfunction.loss - ref_loss)
assert (diff < 10.**(-10.)), \
'Calculated value of loss function is wrong!'
diff = abs(calc.model.lossfunction.energy_loss - ref_energyloss)
assert (diff < 10.**(-10.)), \
'Calculated value of energy per atom RMSE is wrong!'
diff = abs(calc.model.lossfunction.force_loss - ref_forceloss)
assert (diff < 10 ** (-10.)), \
'Calculated value of force RMSE is wrong!'
diff = abs(calc.model.lossfunction.energy_maxresid -
ref_energy_maxresid)
assert (diff < 10.**(-10.)), \
'Calculated value of energy per atom max residual is wrong!'
diff = abs(calc.model.lossfunction.force_maxresid -
ref_force_maxresid)
assert (diff < 10 ** (-10.)), \
'Calculated value of force max residual is wrong!'
for _ in range(len(ref_dloss_dparameters)):
diff = abs(calc.model.lossfunction.dloss_dparameters[_] -
ref_dloss_dparameters[_])
assert(diff < 10 ** (-12.)), \
'Calculated value of loss function derivative is wrong!'
#!/usr/bin/env python
from amp import Amp
from amp.descriptor import Gaussian
from amp.regression import NeuralNetwork
from ase.db import connect
from amp import SimulatedAnnealing
db = connect('../../../database/master.db')
images = []
for d in db.select('train_set=True'):
atoms = db.get_atoms(d.id)
del atoms.constraints
images += [atoms]
for n in [2, 3]:
calc = Amp(label='./',
dblabel='../../',
descriptor=Gaussian(cutoff=6.5),
regression=NeuralNetwork(hiddenlayers=(2, n)))
calc.train(images=images,
data_format='db',
cores=4,
energy_goal=1e-2,
force_goal=1e-1,
global_search=SimulatedAnnealing(temperature=70, steps=50),
extend_variables=False)
G = {}
hiddenlayers = {}
for el in elements:
G[el] = Gf
hiddenlayers[el] = layer_config
if use_tensorflow:
from amp.model.tflow import NeuralNetwork
my_model = NeuralNetwork(hiddenlayers=hiddenlayers,
force_coefficient=None)
else:
from amp.model.neuralnetwork import NeuralNetwork
my_model = NeuralNetwork(hiddenlayers=hiddenlayers)
MLIP = Amp(descriptor=Gaussian(Gs=G, cutoff=cutoff), model=my_model)
MLIP.model.checkpoints = checkpoint_interval
MLIP.cores['localhost'] = cores
from amp.utilities import Logger, make_filename
MLIP._log = Logger(make_filename('', log_file))
############### parsing images ##########
from ase import io
from glob import glob
import time
struct_types = ['known', 'polymorphD3', 'random']
dyn_types = ['md', 'relax', 'sp']
image_list = []
