def Minimisation_Function(cluster, collection, cluster_name): cluster.pbc = False #################################################################################################################### # Perform the local optimisation method on the cluster. # Parameter sequence: [p, q, a, xi, r0] Gupta_parameters = {'Cu': [10.960, 2.2780, 0.0855, 1.224, 2.556]} cluster.set_calculator(Gupta(Gupta_parameters, cutoff=1000, debug=False)) dyn = FIRE(cluster, logfile=None) startTime = time.time() converged = False try: dyn.run(fmax=0.01, steps=5000) converged = dyn.converged() if not converged: errorMessage = 'The optimisation of cluster ' + str( cluster_name) + ' did not optimise completely.' print(errorMessage, file=sys.stderr) print(errorMessage) except: print('Local Optimiser Failed for some reason.') endTime = time.time() #################################################################################################################### # Write information about the algorithm Info = {} Info["INFO.txt"] = '' Info["INFO.txt"] += ("No of Force Calls: " + str(dyn.get_number_of_steps()) + '\n') Info["INFO.txt"] += ("Time (s): " + str(endTime - startTime) + '\n') #Info["INFO.txt"] += ("Cluster converged?: " + str(dyn.converged()) + '\n') #################################################################################################################### return cluster, converged, Info
def Minimisation_Function(cluster,collection,cluster_name): #################################################################################################################### cluster.pbc = False #################################################################################################################### # Perform the local optimisation method on the cluster. # Parameter sequence: [p, q, a, xi, r0] #Gupta_parameters = {'Au': [10.529999999999999, 4.2999999999999998, 0.21970000000000001, 1.855, 2.8779245994292486]} Gupta_parameters = {'Pd': [10.867, 3.742, 0.1746, 1.718, 2.7485], 'Au': [10.229, 4.036, 0.2061, 1.79, 2.884], ('Au','Pd'): [10.54, 3.89, 0.19, 1.75, 2.816]} cluster.set_calculator(Gupta(Gupta_parameters, cutoff=1000, debug=False)) dyn = FIRE(cluster,logfile=None) startTime = time.time(); converged = False try: dyn.run(fmax=0.01,steps=5000) converged = dyn.converged() if not converged: errorMessage = 'The optimisation of cluster ' + str(cluster_name) + ' did not optimise completely.' print(errorMessage, file=sys.stderr) print(errorMessage) except Exception: print('Local Optimiser Failed for some reason.') endTime = time.time() #################################################################################################################### # Write information about the algorithm Info = {} Info["INFO.txt"] = '' Info["INFO.txt"] += ("No of Force Calls: " + str(dyn.get_number_of_steps()) + '\n') Info["INFO.txt"] += ("Time (s): " + str(endTime - startTime) + '\n') #Info["INFO.txt"] += ("Cluster converged?: " + str(dyn.converged()) + '\n') #################################################################################################################### return cluster, converged, Info
def Minimisation_Function(cluster, collection, cluster_dir): cluster.pbc = False rCut = 1000 #sigma = 1; epsilon = 1; lj_calc = LennardJones(sigma=sigma, epsilon=epsilon,rc=rCut) elements = [atomic_numbers[cluster[0].symbol]] sigma = [1] epsilon = [1] lj_calc = LennardJones(elements, epsilon, sigma, rCut=rCut, modified=True) cluster.set_calculator(lj_calc) dyn = FIRE(cluster, logfile=None) startTime = time.time() converged = False try: dyn.run(fmax=0.01, steps=5000) converged = dyn.converged() if not converged: errorMessage = 'The optimisation of cluster ' + str( cluster_dir) + ' did not optimise completely.' print(errorMessage, file=sys.stderr) print(errorMessage) except Exception: print('Local Optimiser Failed for some reason.') endTime = time.time() # Write information about the algorithm Info = {} Info["INFO.txt"] = '' Info["INFO.txt"] += ("No of Force Calls: " + str(dyn.get_number_of_steps()) + '\n') Info["INFO.txt"] += ("Time (s): " + str(endTime - startTime) + '\n') #Info.write("Cluster converged?: " + str(dyn.converged()) + '\n') return cluster, converged, Info
def Minimisation_Function(cluster,collection,cluster_dir): #################################################################################################################### # Read the BeforeOpt file and record the elements, the # number of each element in the cluster and their positions #cluster = ase_read("BeforeOpt",format='vasp') cluster.pbc = False #################################################################################################################### #Construct atoms using the ASE class "Atoms". #################################################################################################################### # Perform the local optimisation method on the cluster. # Parameter sequence: [p, q, a, xi, r0] rCut = 1000 #sigma = 1; epsilon = 1; lj_calc = LennardJones(sigma=sigma, epsilon=epsilon,rc=rCut) elements = [atomic_numbers[cluster[0].symbol]]; sigma = [1]; epsilon = [1]; lj_calc = LennardJones(elements, epsilon, sigma, rCut=rCut, modified=True) cluster.set_calculator(lj_calc) dyn = FIRE(cluster,logfile=None) startTime = time.time(); converged = False try: dyn.run(fmax=0.01,steps=5000) converged = dyn.converged() if not converged: import os name = os.path.basename(os.getcwd()) errorMessage = 'The optimisation of cluster ' + name + ' did not optimise completely.' print(errorMessage, file=sys.stderr) print(errorMessage) except: print('Local Optimiser Failed for some reason.') endTime = time.time() #ase_write('AfterOpt.traj',cluster) #################################################################################################################### # Write information about the algorithm Info = {} Info["INFO.txt"] = '' Info["INFO.txt"] += ("No of Force Calls: " + str(dyn.get_number_of_steps()) + '\n') Info["INFO.txt"] += ("Time (s): " + str(endTime - startTime) + '\n') #Info.write("Cluster converged?: " + str(dyn.converged()) + '\n') #################################################################################################################### return cluster, converged, Info
def Minimisation_Function(cluster, collection, cluster_dir): #################################################################################################################### # Read the BeforeOpt file and record the elements, the # number of each element in the cluster and their positions #cluster = ase_read("BeforeOpt",format='vasp') cluster.pbc = False #################################################################################################################### #Construct atoms using the ASE class "Atoms". #################################################################################################################### # Perform the local optimisation method on the cluster. # Parameter sequence: [p, q, a, xi, r0] Gupta_parameters = {'Cu': [10.960, 2.2780, 0.0855, 1.224, 2.556]} cluster.set_calculator(Gupta(Gupta_parameters, cutoff=1000, debug=False)) dyn = FIRE(cluster, logfile=None) startTime = time.time() converged = False try: dyn.run(fmax=0.01, steps=5000) converged = dyn.converged() if not converged: import os name = os.path.basename(os.getcwd()) errorMessage = 'The optimisation of cluster ' + name + ' did not optimise completely.' #print sys.stderr >> errorMessage print(errorMessage) except: print('Local Optimiser Failed for some reason.') endTime = time.time() #ase_write('AfterOpt.traj',cluster) #################################################################################################################### # Write information about the algorithm Info = {} Info["INFO.txt"] = '' Info["INFO.txt"] += ("No of Force Calls: " + str(dyn.get_number_of_steps()) + '\n') Info["INFO.txt"] += ("Time (s): " + str(endTime - startTime) + '\n') #Info.write("Cluster converged?: " + str(dyn.converged()) + '\n') #################################################################################################################### return cluster, converged, Info
def Minimisation_Function(cluster, collection, cluster_name): ####################################################################################### cluster.pbc = False # make sure that the periodic boundry conditions are set off ####################################################################################### # Perform the local optimisation method on the cluster. # Parameter sequence: [p, q, a, xi, r0] # # RGL parameters below from: # Crossover among structural motifs in transition and noble-metal clusters # F. Baletto, R. Ferrando, A. Fortunelli, F. Montalenti and C. Mottet, J. Chem. Phys., 2002, 116, 3856–3863. # https://doi.org/10.1063/1.1448484 r0 = 4.07 / (2.0**0.5) Gupta_parameters = {'Au': [10.53, 4.30, 0.2197, 1.855, r0]} cluster.set_calculator(Gupta(Gupta_parameters, cutoff=1000, debug=False)) dyn = FIRE(cluster, logfile=None) startTime = time.time() converged = False try: dyn.run(fmax=0.01, steps=5000) converged = dyn.converged() if not converged: errorMessage = 'The optimisation of cluster ' + str( cluster_name) + ' did not optimise completely.' print(errorMessage, file=sys.stderr) print(errorMessage) except Exception: print('Local Optimiser Failed for some reason.') endTime = time.time() #################################################################################################################### # Write information about the algorithm Info = {} Info["INFO.txt"] = '' Info["INFO.txt"] += ("No of Force Calls: " + str(dyn.get_number_of_steps()) + '\n') Info["INFO.txt"] += ("Time (s): " + str(endTime - startTime) + '\n') #Info["INFO.txt"] += ("Cluster converged?: " + str(dyn.converged()) + '\n') #################################################################################################################### return cluster, converged, Info