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_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)
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 create_calc(self, label, dblabel): amp_label = os.path.join(self.calc_dir, label) amp_dblabel = os.path.join(self.calc_dir, dblabel) amp_name = amp_label + ".amp" if not os.path.exists(amp_name): print("Creating calculator {}...".format(amp_name)) loss_function = LossFunction( convergence=self.convergence, energy_coefficient=self.energy_coefficient, force_coefficient=self.force_coefficient, overfit=self.overfit, ) model = NeuralNetwork( hiddenlayers=self.hidden_layers, activation=self.activation, lossfunction=loss_function, weights=None, scalings=None, prescale=True, ) descriptor = Gaussian(cutoff=self.cutoff, Gs=self.Gs, fortran=True) calc = Amp(descriptor=descriptor, model=model, label=amp_label, dblabel=amp_dblabel) return calc else: print("Calculator {} already exists!".format(amp_name)) calc = Amp.load(amp_name, label=amp_label, dblabel=amp_dblabel) return calc
def compute_population_performance(population, reference): for individual in population: # Construct NNP nnp = Amp(descriptor=Gaussian(), model=NeuralNetwork(hiddenlayers=(5, 5)), cores=8)
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 run_amp(fin): images = ase.io.read(fin, index=':') print(len(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)
def exe_train_images(images, HL, E_conv): Hidden_Layer = tuple(HL) print("Hidden Layer: {}".format(Hidden_Layer)) print("Energy convergence: {}".format(E_conv)) calc = Amp(descriptor=Gaussian(), model=NeuralNetwork(hiddenlayers=Hidden_Layer)) calc.model.lossfunction = LossFunction(convergence={'energy_rmse': E_conv}) #calc.model.lossfunction = LossFunction(convergence={'energy_rmse': E_conv},force_coefficient=-0.1) #calc.model.lossfunction = LossFunction(force_coefficient=-0.1) calc.train(images=images, overwrite=True) return
def calc_train_images(images, HL, E_conv, f_conv, f_coeff, ncore): Hidden_Layer=tuple(HL) print("Hidden Layer: {}".format(Hidden_Layer)) print("Energy convergence: {}".format(E_conv)) cores={'localhost':ncore} # 'localhost' depress SSH, communication between nodes calc = Amp(descriptor=Gaussian(), model=NeuralNetwork(hiddenlayers=Hidden_Layer), cores=cores) if f_conv <= 0.0: convergence={'energy_rmse': E_conv} else: convergence={'energy_rmse': E_conv, 'force_rmse':f_conv} calc.model.lossfunction = LossFunction(convergence=convergence, force_coefficient=f_coeff) #calc.model.lossfunction = LossFunction(force_coefficient=-0.1) calc.train(images=images, overwrite=True) return
def test(): "FingerprintPlot test.""" generate_data(2, filename='fpplot-training.traj') calc = Amp(descriptor=Gaussian(), model=NeuralNetwork(), label='fpplot-test' ) calc.model.lossfunction = LossFunction(convergence={'energy_rmse': 1.00, 'force_rmse': 1.00}) calc.train(images='fpplot-training.traj') images = ase.io.Trajectory('fpplot-training.traj') fpplot = FingerprintPlot(calc) fpplot(images) fpplot(images, overlay=images[0]) fpplot(images, overlay=[images[1][2], images[0][-1]])
def calc_train_images(images, HL, E_conv, f_conv, f_coeff, ncore, amp_pot=None): Hidden_Layer=tuple(HL) print("Hidden Layer: {}".format(Hidden_Layer)) print("Energy convergence: {}".format(E_conv)) cores={'localhost':ncore} # 'localhost' depress SSH, communication between nodes ### load "amp.amp" if amp_pot: calc = Amp.load(amp_pot) calc = Amp(descriptor=Gaussian(), model=NeuralNetwork(hiddenlayers=Hidden_Layer), cores=cores) ### Global Search in Param Space Annealer(calc=calc, images=images, Tmax=20, Tmin=1, steps=4000) if f_conv <= 0.0: E_maxresid = E_conv*3 #convergence={'energy_rmse': E_conv} convergence={'energy_rmse': E_conv, 'energy_maxresid': E_maxresid} else: convergence={'energy_rmse': E_conv, 'force_rmse':f_conv} calc.model.lossfunction = LossFunction(convergence=convergence, force_coefficient=f_coeff) # setting calc.train(images=images, overwrite=True) return
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)))
#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 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 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!")
# -*- 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(): """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
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)
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 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)
def periodic_test(): """Gaussian/Neural periodic standard. Checks that the answer matches that expected from previous Mathematica calculations. """ # 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(): """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 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!'
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!'
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')
def non_periodic_test(): """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), 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)