# -*- coding: utf-8 -*- setVerbosity(MinimalLog) # ------------------------------------------------------------- # Bulk Configuration # ------------------------------------------------------------- # Set up lattice vector_a = [9.497426971725758, 0.0, 0.0]*Angstrom vector_b = [0.0, 9.497426971725758, 0.0]*Angstrom vector_c = [0.0, 0.0, 9.497426971725758]*Angstrom lattice = UnitCell(vector_a, vector_b, vector_c) # Define elements elements = [Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Oxygen, Hafnium, Hafnium, Hafnium, Hafnium, Hafnium, Hafnium, Hafnium, Hafnium, Hafnium, Hafnium, Hafnium, Hafnium, Hafnium, Hafnium, Hafnium, Hafnium, Hafnium, Hafnium, Hafnium, Hafnium] # Define coordinates fractional_coordinates = [[ 0.076286602313, 0.484331164618, 0.381316169166], [ 0.766917781374, 0.494937841286, 0.291874574266], [ 0.731969980361, 0.943836312627, 0.57296061439 ], [ 0.554574027258, 0.461332853561, 0.886717927207], [ 0.250228263221, 0.890222307852, 0.388755307743], [ 0.929803223539, 0.676225151653, 0.795242624538], [ 0.39718592907 , 0.158335635766, 0.388556756402], [ 0.11645891105 , 0.71352269763 , 0.628339753261], [ 0.464081550737, 0.146998191259, 0.949948088773], [ 0.593809676382, 0.723661829469, 0.282815566712], [ 0.956144927607, 0.410082162955, 0.63118390115 ], [ 0.630261587996, 0.068949857119, 0.312897385008], [ 0.676614862447, 0.916136708317, 0.080737525515], [ 0.283280934016, 0.082466086602, 0.147414791521], [ 0.156534915042, 0.264983388403, 0.44504783418 ], [ 0.323709076563, 0.944373529376, 0.767758731255], [ 0.086974924396, 0.245291615829, 0.13994601509 ], [ 0.382753861034, 0.801331012658, 0.120736888779], [ 0.325331129964, 0.599980937095, 0.400551045963], [ 0.710664562389, 0.543883143178, 0.640949170099], [ 0.768632684085, 0.291833856211, 0.982008566834], [ 0.935781100858, 0.449313456644, 0.91777328168 ], [ 0.470152665574, 0.892151512961, 0.514373913142], [ 0.896162402168, 0.246679452251, 0.280748840967], [ 0.056111635294, 0.968087964348, 0.543755879799], [ 0.394873138661, 0.413695452858, 0.165606209471], [ 0.953268976235, 0.076126206638, 0.75940728911 ], [ 0.629651636858, 0.634630228571, 0.05858775273 ], [ 0.589855514666, 0.756388866923, 0.772396168952], [ 0.012660970567, 0.697385940088, 0.191689851382], [ 0.869214445599, 0.734481879712, 0.431281932903], [ 0.297163631424, 0.496592752664, 0.698151011982], [ 0.464653722521, 0.600528668785, 0.541204813482], [ 0.734874860709, 0.260514207603, 0.638705119014], [ 0.912475699997, 0.936927768911, 0.225749701655], [ 0.41838929944 , 0.18217514434 , 0.693574297042], [ 0.580921531053, 0.352165986484, 0.471443636145], [ 0.190302700851, 0.486222762835, 0.020574472234], [ 0.161258411077, 0.268084679045, 0.84395447297 ], [ 0.328992105963, 0.706695342299, 0.870286697912], [ 0.1214543959 , 0.707765332143, 0.395966412374], [ 0.324898608394, 0.402583945263, 0.38949737155 ], [ 0.466227728418, 0.908619926909, 0.935331908209], [ 0.302880914006, 0.24554211747 , 0.01438921806 ], [ 0.580163847321, 0.116820565433, 0.537604874893], [ 0.886135894107, 0.268791895681, 0.783965762093], [ 0.344730617223, 0.766690970039, 0.665638788583], [ 0.645121097015, 0.750009044287, 0.53235612777 ], [ 0.988059904634, 0.436680835838, 0.160021696006], [ 0.109670678906, 0.533225387478, 0.786010316405], [ 0.749014847635, 0.116815557743, 0.153472283775], [ 0.53286379991 , 0.405031432457, 0.674071349234], [ 0.437918433144, 0.923188119777, 0.277003037718], [ 0.810521688743, 0.424399343153, 0.479289133013], [ 0.800449713604, 0.735269463106, 0.219879631298], [ 0.093005373938, 0.069416769287, 0.30541397964 ], [ 0.204687273036, 0.142966757615, 0.642380807038], [ 0.403413430916, 0.591194801698, 0.041298434967], [ 0.924039151622, 0.8794440263 , 0.681366359624], [ 0.732190278544, 0.574240691674, 0.870477684012]] # Set up configuration bulk_configuration = BulkConfiguration( bravais_lattice=lattice, elements=elements, fractional_coordinates=fractional_coordinates, ) # ------------------------------------------------------------- # Calculator: DFT Reference calculator. # ------------------------------------------------------------- k_point_sampling = KpointDensity( density_a=7.0*Angstrom, ) numerical_accuracy_parameters = NumericalAccuracyParameters( density_mesh_cutoff=125.0*Hartree, k_point_sampling=k_point_sampling, ) iteration_control_parameters = IterationControlParameters( tolerance=5e-05, damping_factor=0.3, number_of_history_steps=12, ) reference_calculator = LCAOCalculator( numerical_accuracy_parameters=numerical_accuracy_parameters, iteration_control_parameters=iteration_control_parameters, ) # Load the training data. This can be one or several TrainingSet, MomentTensorPotentialTraining or # Trajectory objects, which contain energy, forces, stress calculated with the reference # calculator. initial_training_data = [nlread('HfO2_crystal_training.hdf5', TrainingSet)[0]] # Use the predefined small MTP basis. mtp_basis = PredefinedBasisSmall # Optimize the non-linear coefficients on the energy only. nl_parameters = NonLinearCoefficientsParameters( perform_optimization=True, energy_only=True, ) fitting_parameters = MomentTensorPotentialFittingParameters( basis_size=mtp_basis, outer_cutoff_radii=4.5*Ang, mtp_filename='HfO2_active_learning.mtp', non_linear_coefficients_parameters=nl_parameters, use_element_specific_coefficients=True, ) active_learning = ActiveLearningSimulation( fitting_parameters=fitting_parameters, initial_training_data=initial_training_data, mtp_study_filename='HfO2_mtp_study', mtp_study_object_id='HfO2', reference_calculator=reference_calculator, candidate_threshold=1.0, retrain_threshold=3.0, check_interval=20, max_forces_check=10.0*eV/Ang, use_stress=True, candidate_trajectory_filename='HfO2_am_active_learning_candidates.hdf5', restart_simulation=True, extrapolation_selection_parameters=ExtrapolationSelectionParameters( extrapolation_grade_algorithm=QueryByCommitteeForces, descriptor_cutoff=0.1, ), ) # Set up a high-temperature MD at 3000 K. initial_velocity = MaxwellBoltzmannDistribution( temperature=3000.0*Kelvin, remove_center_of_mass_momentum=True, random_seed=None, enforce_temperature=True, ) method = Langevin( time_step=1*femtoSecond, reservoir_temperature=3000*Kelvin, friction=0.01*femtoSecond**-1, initial_velocity=initial_velocity, ) constraints = [FixCenterOfMass()] # Run the MD simulation through the active learning object. md_trajectory = active_learning.runMolecularDynamics( bulk_configuration, constraints=constraints, trajectory_filename='HfO2_am_active_learning_3000K.hdf5', steps=100000, log_interval=100, method=method, domain_decomposition_pattern=[1, 1, 1], ) # Extract the additional training data that has been added during active learning as a TrainingSet # object, and save it to a file. additional_training_data = active_learning.additionalTrainingSet() nlsave('HfO2_active_learning_additional_training_data.hdf5', additional_training_data) # Extract a table with the initial and additional training data that has been added # during active learning. This table can be used as input to a final MTP fit. training_set_table = active_learning.trainingSetTable() # Test different basis sizes. fitting_parameters_list = [] for mtp_basis in [PredefinedBasisSmall, 400, 800]: # Optimize the non-linear coefficients on the energy only. nl_parameters = NonLinearCoefficientsParameters( perform_optimization=True, energy_only=True, ) fitting_parameters = MomentTensorPotentialFittingParameters( basis_size=mtp_basis, outer_cutoff_radii=4.5*Ang, mtp_filename=f'HfO2_active_learning_{mtp_basis}.mtp', # RJL: Not technically active learning. Maybe rename. non_linear_coefficients_parameters=nl_parameters, use_element_specific_coefficients=True, ) fitting_parameters_list.append(fitting_parameters) # Perform an MTP training using the list of fitting parameters. mtp_training = MomentTensorPotentialTraining( filename='Final_MTP_training_basis_size_scan.hdf5', object_id='mtp_training', training_sets=training_set_table, calculator=reference_calculator, calculate_stress=True, fitting_parameters_list=fitting_parameters_list, train_test_split=0.9, random_seed=13345, log_filename_prefix='mtp_basis_size_scan', ) mtp_training.update() nlprint(mtp_training)