# %% ForceFieldCalculator # This script uses a forcefield calculator for simply showing how to set up the workflow # but for real studies of defect dynamics and other complex phenomena a GGA DFT calculator # is advisable. potentialSet = ReaxFF_CHONSSiCaCsKSrNaMgAl_2014(strict_bondpairs=False) calculator = TremoloXCalculator(parameters=potentialSet) # %% Load TrainingSet # Load a premade training set. This simple training set has been created with # respectively pure silicon carbide cells of varying sizes and some simple # defect configurations created with the Defects object with an interstitial # magnesium atom included. training_set_data_results_training_set_0 = nlread( filename=r'C:/full/path/to/folder/TrainingSet_data_results.hdf5', object_id='TrainingSet_0', )[0] # %% Table if 'table' not in locals(): table = Table('AKMC-AL_SiC_Mg-interstitial.hdf5', object_id='table') table.addInstanceColumn(key='training_set', types=TrainingSet) table.setMetatext('Table') table.append(training_set_data_results_training_set_0) # %% NonLinearCoefficientsParameters non_linear_coefficients_parameters = NonLinearCoefficientsParameters(random_seed=1) # %% MomentTensorPotentialFittingParameters moment_tensor_potential_fitting_parameters = MomentTensorPotentialFittingParameters( basis_size=500, outer_cutoff_radii=4.5 * Angstrom, forces_cap=500.0 * eV / Angstrom, non_linear_coefficients_parameters=non_linear_coefficients_parameters, use_element_specific_coefficients=True, ) # %% ActiveLearningSimulation extrapolation_selection_parameters = ExtrapolationSelectionParameters( extrapolation_grade_algorithm=QueryByCommitteeForces, write_atomic_error_estimates=True, ) active_learning_simulation = ActiveLearningSimulation( fitting_parameters=moment_tensor_potential_fitting_parameters, initial_training_data=table, mtp_study_filename='AKMC-AL_SiC_Mg-interstitial.hdf5', mtp_study_object_id='mtp_active_learning', reference_calculator=calculator, retrain_threshold=2.0, candidate_trajectory_filename='AKMC-AL_SiC_Mg-interstitial.hdf5', candidate_trajectory_object_id='al_candidates', max_iterations=100, extrapolation_selection_parameters=extrapolation_selection_parameters, ) # %% ConfigurationTable(List) # %% Configuration # Set up lattice lattice = Hexagonal(9.24153*Angstrom, 10.0848*Angstrom) # Define elements elements = [Magnesium, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon, Silicon, Carbon] # Define coordinates fractional_coordinates = [[-0.000001796922, -0.000000562305, 0.596070368757], [-0.000000491118, -0.000001423676, 0.000842041026], [-0.000000205884, -0.000001383541, 0.188233014251], [ 0.110811058927, 0.221621814395, 0.249969311004], [ 0.109829059508, 0.2196583359 , 0.439589629801], [ 0.245293333655, 0.122645489506, 0.488142170214], [ 0.225402718954, 0.112700398204, 0.687221178349], [ 0.116653980832, 0.233307899771, 0.757331706657], [ 0.11146277856 , 0.222925272715, 0.940751317959], [-0.000182451102, 0.334335956139, 0.001401835037], [ 0.000126658693, 0.333610745075, 0.188919306969], [ 0.111713775738, 0.55585572834 , 0.250483969249], [ 0.111211131028, 0.555604200648, 0.437760793502], [ 0.222330915802, 0.444660951181, 0.500401792859], [ 0.223660337491, 0.447319977989, 0.688113927628], [ 0.110764068664, 0.555380822675, 0.75124517968 ], [ 0.111094766015, 0.555546249638, 0.938001717188], [-0.000182413478, 0.665479510391, 0.001401800378], [ 0.000126722726, 0.666513720997, 0.188919311512], [ 0.110811253577, 0.889186932759, 0.2499694597 ], [ 0.109829395363, 0.890168437617, 0.439589668067], [ 0.222330695434, 0.777667199888, 0.500401732683], [ 0.223660088609, 0.776337844776, 0.688113886822], [ 0.116654009306, 0.883343889406, 0.75733141284 ], [ 0.111462832867, 0.888535342786, 0.940751158024], [ 0.334518658582, 0.000180596666, 0.001401815154], [ 0.333484463299, -0.000128552864, 0.188919261249], [ 0.446812059424, 0.223404856537, 0.245228012251], [ 0.456543201939, 0.228270381015, 0.431675884813], [ 0.554798395266, 0.109596872961, 0.500145533664], [ 0.555556707125, 0.111113437972, 0.687570795315], [ 0.446612498081, 0.223305123252, 0.751441590001], [ 0.443672532677, 0.221835220216, 0.937622702914], [ 0.334518665183, 0.334335953788, 0.001401886277], [ 0.333484416638, 0.333610760408, 0.188919345063], [ 0.444142309997, 0.555855766271, 0.250483916962], [ 0.444393617909, 0.555604225616, 0.437760642442], [ 0.55479867684 , 0.445199205235, 0.500145623278], [ 0.555557011734, 0.444440938365, 0.687570890175], [ 0.444617255708, 0.55538068838 , 0.751245236818], [ 0.444451784801, 0.555546238995, 0.938001772576], [ 0.333332812795, 0.666665348345, 0.000498637507], [ 0.333332807161, 0.666665295376, 0.188195233904], [ 0.444142341554, 0.888284277919, 0.250483948497], [ 0.444393593349, 0.888786637092, 0.437760784015], [ 0.55533696638 , 0.777667004673, 0.500401762112], [ 0.552677949783, 0.776337601652, 0.68811393903 ], [ 0.444617087112, 0.8892338381 , 0.751245172959], [ 0.444451782942, 0.888903289278, 0.938001710532], [ 0.665662186868, 0.000180653452, 0.001401825274], [ 0.666387391661, -0.000128521614, 0.188919250589], [ 0.776593048989, 0.223404954227, 0.245227802701], [ 0.771726970503, 0.22827093797 , 0.431675605268], [ 0.877351901913, 0.122645867165, 0.488141315009], [ 0.887297100201, 0.112700891892, 0.687221327326], [ 0.776692560289, 0.223305456976, 0.75144168897 ], [ 0.778162860019, 0.221835322908, 0.937622793709], [ 0.666666109383, 0.333332111268, -0.002869186472], [ 0.666666128427, 0.333332135772, 0.182762928989], [ 0.776593232543, 0.553186114161, 0.2452280084 ], [ 0.771727165243, 0.543453991573, 0.431675654892], [ 0.89040092211 , 0.44519944496 , 0.500145529853], [ 0.888884545611, 0.444441257865, 0.687570775483], [ 0.776692914778, 0.553385577665, 0.751441571953], [ 0.778162915485, 0.556325532925, 0.937622740482], [ 0.665662181617, 0.6654794412 , 0.001401871408], [ 0.666387398713, 0.666513773324, 0.188919337018], [ 0.778376349524, 0.88918711857 , 0.249969284304], [ 0.780339644533, 0.890168850272, 0.439589560342], [ 0.877353113378, 0.754706111706, 0.488142521702], [ 0.887297635426, 0.774595359534, 0.68722101625 ], [ 0.766690016129, 0.883343927058, 0.757331621642], [ 0.777072777134, 0.888535332651, 0.940751367245]] # First AKMC simulation: Initial configuration setup for an AKMC simulation # with active learning that should be started from scratch # Set up configuration configuration_0 = BulkConfiguration( bravais_lattice=lattice, elements=elements, fractional_coordinates=fractional_coordinates ) # Add tags - constraints needed for AKMC configuration_0.addTags('fixed', [36, 37]) configuration_name_0 = "optimized_configuration_sic_mginterstitial" configuration_table = Table() configuration_table.addInstanceColumn(key='configuration', types=BulkConfiguration) configuration_table.append(configuration_0) configuration_names_table = Table() configuration_names_table.addStringColumn(key='configuration_name') configuration_names_table.append(configuration_name_0) # %% ActiveLearningOptimizeGeometry optimized_configuration_table = runActiveLearningOptimizeGeometry( active_learning_simulation=active_learning_simulation, configurations=configuration_table, max_forces=0.01 * eV / Angstrom, constraints=[BravaisLatticeConstraint()], trajectory_filename='AKMC-AL_SiC_Mg-interstitial.hdf5', trajectory_object_id='alopt', log_filename_prefix='AKMC-AL_pre-optimize_geometry', ) nlsave( 'AKMC-AL_SiC_Mg-interstitial.hdf5', optimized_configuration_table, object_id='alopt_1' ) training_set_table = active_learning_simulation.trainingSetTable() # Custom block for extraction the configuration from the table def custom(table): # This custom script supports atkpython syntax # and can perform almost any procedure. configuration = table[0, 0] configuration.update() return configuration configuration = custom(optimized_configuration_table) nlsave('AKMC-AL_SiC_Mg-interstitial.hdf5', configuration) # %% TotalEnergy total_energy = TotalEnergy(configuration=configuration) nlsave('AKMC-AL_SiC_Mg-interstitial.hdf5', total_energy) ## Restarting the AKMC simulation: Initial configuration setup for an AKMC simulation ## with active learning that should be restarted # %% Load BulkConfiguration # # configuration = nlread( # filename=r'C:/full/path/to/folder/AKMC-AL_SiC_Mg-interstitial.hdf5', # object_id='BulkConfiguration_0', # )[0] # # # %% Load TotalEnergy # # total_energy = nlread( # filename=r'C:/full/path/to/folder/AKMC-AL_SiC_Mg-interstitial.hdf5', # object_id='TotalEnergy_0', # )[0] # %% AdaptiveKineticMonteCarlo saddle_search_parameters = SaddleSearchParameters(neb_climbing_image=True) if os.path.isfile('akmc-al_markov_chain.nc'): markov_chain = nlread('akmc-al_markov_chain.nc')[-1] else: markov_chain = MarkovChain( configuration=configuration, configuration_energy=total_energy.evaluate() ) if os.path.isfile('akmc-al_kmc.nc'): kmc = nlread('akmc-al_kmc.nc')[-1] else: kmc = None saddle_search_method = MDSaddleSearch( temperature=2900.0 * Kelvin, saddle_search_parameters=saddle_search_parameters, active_learning=active_learning_simulation, constraints=configuration.indicesFromTags(['fixed']), ) akmc = AdaptiveKineticMonteCarlo( markov_chain=markov_chain, kmc_temperature=300.0 * Kelvin, calculator=configuration.calculator(), kmc=kmc, filename_prefix='akmc-al', saddle_search_method=saddle_search_method, ) akmc.run(max_searches=50, max_kmc_steps=50)