def Minimisation_Function(cluster):
cluster.pbc = False
# Perform the local optimisation method on the cluster.
# Parameter sequence: [p, q, a, xi, r0]
#Au_parameters = {'Au': [10.229, 4.0360, 0.2061, 1.7900, 2.884]}
r0 = 4.07/(2.0 ** 0.5)
Au_parameters = {'Au': [10.53, 4.30, 0.2197, 1.855, r0]} # Baletto
Gupta_parameters = Au_parameters
cutoff = 1000
calculator = Gupta(Gupta_parameters, cutoff=cutoff, debug=False)
cluster.set_calculator(calculator)
original_cluster = cluster.copy()
dyn = FIRE(cluster,logfile=None)
converged = False
try:
dyn.run(fmax=0.01,steps=5000)
converged = dyn.converged()
if not converged:
cluster_name = 'issue_cluster.xyz'
errorMessage = 'The optimisation of cluster ' + str(original_cluster) + ' did not optimise completely.\n'
errorMessage += 'The cluster of issue before optimisation has been saved as: '+str(cluster_name)
write(cluster_name,original_cluster)
raise Exception(errorMessage)
except Exception as exception_message:
cluster_name = 'issue_cluster.xyz'
errorMessage = 'The optimisation of cluster ' + str(original_cluster) + ' did not optimise completely.\n'
errorMessage += 'The cluster of issue before optimisation has been saved as: '+str(cluster_name)+'\n'
errorMessage += exception_message
write(cluster_name,original_cluster)
raise Exception(errorMessage)
return cluster
def Minimisation_Function(cluster):
cluster.pbc = False
# Perform the local optimisation method on the cluster.
# Parameter sequence: [p, q, a, xi, r0]
Pt_parameters = {'Pt': [10.71, 3.845, 0.27443, 2.6209, 2.77]}
Gupta_parameters = Pt_parameters
cutoff = 1000
calculator = Gupta(Gupta_parameters, cutoff=cutoff, debug=False)
cluster.set_calculator(calculator)
original_cluster = cluster.copy()
dyn = FIRE(cluster, logfile=None)
#startTime = time.time();
converged = False
try:
dyn.run(fmax=0.01, steps=5000)
converged = dyn.converged()
if not converged:
cluster_name = 'issue_cluster.xyz'
errorMessage = 'The optimisation of cluster ' + str(
original_cluster) + ' did not optimise completely.\n'
errorMessage += 'The cluster of issue before optimisation has been saved as: ' + str(
cluster_name)
write(cluster_name, original_cluster)
raise Exception(errorMessage)
except Exception as exception_message:
cluster_name = 'issue_cluster.xyz'
errorMessage = 'The optimisation of cluster ' + str(
original_cluster) + ' did not optimise completely.\n'
errorMessage += 'The cluster of issue before optimisation has been saved as: ' + str(
cluster_name) + '\n'
errorMessage += exception_message
write(cluster_name, original_cluster)
raise Exception(errorMessage)
#endTime = time.time()
return cluster
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 optimse_Cu(cluster):
Cu_parameters = {'Cu': [10.960, 2.2780, 0.0855, 1.2240, 2.556]}
cluster.set_calculator(Gupta(Cu_parameters, cutoff=1000, debug=True))
dyn = FIRE(cluster)
try:
import time
startTime = time.time()
dyn.run(fmax=0.01, steps=5000)
endTime = time.time()
if not dyn.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.')
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
#plt.savefig('CNA_plot_over_rCut.eps')
plt.savefig('CNA_plot_over_rCut.svg')
####################################################################################################################
####################################################################################################################
####################################################################################################################
####################################################################################################################
####################################################################################################################
####################################################################################################################
####################################################################################################################
cluster_1 = get_cluster('1149.xyz')
cluster_2 = get_cluster('2619.xyz')
Gupta_parameters = {'Cu': [10.960, 2.2780, 0.0855, 1.224, 2.556]}
cluster_1.set_calculator(Gupta(Gupta_parameters, cutoff=1000, debug=False))
cluster_1.get_potential_energy()
cluster_2.set_calculator(Gupta(Gupta_parameters, cutoff=1000, debug=False))
cluster_2.get_potential_energy()
lattice_constant = 3.62
r_eq = lattice_constant / (2.0**0.5)
r_high = lattice_constant
r_low = r_eq
r_nums = 1000
rCuts = np.linspace(r_low, r_high, num=r_nums, endpoint=True)
name_1, cluster_1_CNA_profile = get_CNA_profile((cluster_1, rCuts))
name_2, cluster_2_CNA_profile = get_CNA_profile((cluster_2, rCuts))
