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
