def main():
from MAST.structopt import Optimizer
from MAST.structopt import tools
#Test for cluster structures
parameters = {
'structure': 'Cluster',
'optimizer_type': 'GA',
'atomlist': [('Au', 10, 0, 0), ('Cu', 10, 0, 0)]
}
A = Optimizer(parameters)
A.algorithm_initialize()
A.calc = tools.setup_calculator(A)
offspring = A.generation_set([])
indiv = offspring[0].duplicate()
ind1 = offspring[1].duplicate()
ind2 = offspring[2].duplicate()
test_mutations(indiv)
test_cx(ind1, ind2)
A.close_output()
#Test for defect structures
parameters = {
'structure': 'Defect',
'optimizer_type': 'GA',
'atomlist': [('Au', 3, 0, 0), ('Cu', 3, 0, 0)],
'supercell': (3, 3, 3),
'SolidFile': 'BFe.xyz',
'SolidCell': [8.61, 8.61, 8.61]
}
A = Optimizer(parameters)
A.algorithm_initialize()
A.calc = tools.setup_calculator(A)
offspring = A.generation_set([])
indiv = offspring[0].duplicate()
ind1 = offspring[1].duplicate()
ind2 = offspring[2].duplicate()
test_mutations(indiv)
test_cx(ind1, ind2)
A.close_output()
#Test for crystal structures
parameters = {
'structure': 'Crystal',
'optimizer_type': 'GA',
'atomlist': [('Au', 10, 0, 0), ('Cu', 10, 0, 0)]
}
A = Optimizer(parameters)
A.algorithm_initialize()
A.calc = tools.setup_calculator(A)
offspring = A.generation_set([])
indiv = offspring[0].duplicate()
ind1 = offspring[1].duplicate()
ind2 = offspring[2].duplicate()
test_mutations(indiv)
test_cx(ind1, ind2)
A.close_output()
def quench(indiv, Optimizer):
"""Move function to perform a molecular dynamics NVT quenching of structure
Inputs:
indiv = Individual class object to be altered
Optimizer = Optimizer class object with needed parameters
Outputs:
indiv = Altered Individual class object
"""
if "MU" in Optimizer.debug:
debug = True
else:
debug = False
olammps_min = Optimizer.lammps_min
ocalc = Optimizer.calc
mincomd = (
"1.0e-8 1.0e-8 1000 100000\nunfix fix_nve\nfix fix_nvt all nvt temp "
+ repr(Optimizer.quench_max_temp)
+ " "
+ repr(Optimizer.quench_min_temp)
+ " "
+ repr(Optimizer.quench_step_size)
)
mincomd += (
"\nrun "
+ repr(Optimizer.quench_n_steps_1)
+ "\nunfix fix_nvt\nfix fix_nve all nve\nminimize 1.0e-8 1.0e-8 1000 100000"
)
mincomd += (
"\nunfix fix_nve\nfix fix_nvt all nvt temp "
+ repr(Optimizer.quench_max_temp)
+ " "
+ repr(Optimizer.quench_min_temp)
+ " "
+ repr(Optimizer.quench_step_size)
)
mincomd += (
"\nrun "
+ repr(Optimizer.quench_n_steps_2)
+ "\nunfix fix_nvt\nfix fix_nve all nve\nminimize 1.0e-8 1.0e-8 1000 100000"
)
Optimizer.lammps_min = mincomd
Optimizer.calc = setup_calculator(Optimizer)
outs = fitness_switch([Optimizer, indiv])
indiv = outs[0]
Optimizer.output.write("Quench NVT Mutation performed on individual\n")
Optimizer.output.write("Index = " + repr(indiv.index) + "\n")
Optimizer.output.write(outs[1])
Optimizer.output.write("New fitness : " + repr(indiv.fitness) + "\n")
muttype = "Q"
Optimizer.lammps_min = olammps_min
Optimizer.calc = setup_calculator(Optimizer)
if indiv.energy == 0:
indiv.history_index = indiv.history_index + "m" + muttype
else:
indiv.history_index = repr(indiv.index) + "m" + muttype
return indiv
def quench(indiv, Optimizer):
"""Move function to perform a molecular dynamics NVT quenching of structure
Inputs:
indiv = Individual class object to be altered
Optimizer = Optimizer class object with needed parameters
Outputs:
indiv = Altered Individual class object
"""
if 'MU' in Optimizer.debug:
debug = True
else:
debug = False
olammps_min = Optimizer.lammps_min
ocalc = Optimizer.calc
mincomd = '1.0e-8 1.0e-8 1000 100000\nunfix fix_nve\nfix fix_nvt all nvt temp ' + repr(
Optimizer.quench_max_temp) + ' ' + repr(
Optimizer.quench_min_temp) + ' ' + repr(Optimizer.quench_step_size)
mincomd += '\nrun ' + repr(
Optimizer.quench_n_steps_1
) + '\nunfix fix_nvt\nfix fix_nve all nve\nminimize 1.0e-8 1.0e-8 1000 100000'
mincomd += '\nunfix fix_nve\nfix fix_nvt all nvt temp ' + repr(
Optimizer.quench_max_temp) + ' ' + repr(
Optimizer.quench_min_temp) + ' ' + repr(Optimizer.quench_step_size)
mincomd += '\nrun ' + repr(
Optimizer.quench_n_steps_2
) + '\nunfix fix_nvt\nfix fix_nve all nve\nminimize 1.0e-8 1.0e-8 1000 100000'
Optimizer.lammps_min = mincomd
Optimizer.calc = setup_calculator(Optimizer)
outs = fitness_switch([Optimizer, indiv])
indiv = outs[0]
Optimizer.output.write('Quench NVT Mutation performed on individual\n')
Optimizer.output.write('Index = ' + repr(indiv.index) + '\n')
Optimizer.output.write(outs[1])
Optimizer.output.write('New fitness : ' + repr(indiv.fitness) + '\n')
muttype = 'Q'
Optimizer.lammps_min = olammps_min
Optimizer.calc = setup_calculator(Optimizer)
if indiv.energy == 0:
indiv.history_index = indiv.history_index + 'm' + muttype
else:
indiv.history_index = repr(indiv.index) + 'm' + muttype
return indiv
def main():
from MAST.structopt import Optimizer
from MAST.structopt import tools
#Test for cluster structures
parameters = {'structure':'Cluster','optimizer_type': 'GA','atomlist':[('Au', 10, 0, 0), ('Cu', 10, 0, 0)]}
A = Optimizer(parameters)
A.algorithm_initialize()
A.calc = tools.setup_calculator(A)
offspring = A.generation_set([])
indiv = offspring[0].duplicate()
ind1 = offspring[1].duplicate()
ind2 = offspring[2].duplicate()
test_mutations(indiv)
test_cx(ind1,ind2)
A.close_output()
#Test for defect structures
parameters = {'structure':'Defect','optimizer_type': 'GA','atomlist':[('Au', 3, 0, 0), ('Cu', 3, 0, 0)],'supercell':(3,3,3),'SolidFile':'BFe.xyz','SolidCell':[8.61, 8.61, 8.61]}
A = Optimizer(parameters)
A.algorithm_initialize()
A.calc = tools.setup_calculator(A)
offspring = A.generation_set([])
indiv = offspring[0].duplicate()
ind1 = offspring[1].duplicate()
ind2 = offspring[2].duplicate()
test_mutations(indiv)
test_cx(ind1,ind2)
A.close_output()
#Test for crystal structures
parameters = {'structure':'Crystal','optimizer_type': 'GA','atomlist':[('Au', 10, 0, 0), ('Cu', 10, 0, 0)]}
A = Optimizer(parameters)
A.algorithm_initialize()
A.calc = tools.setup_calculator(A)
offspring = A.generation_set([])
indiv = offspring[0].duplicate()
ind1 = offspring[1].duplicate()
ind2 = offspring[2].duplicate()
test_mutations(indiv)
test_cx(ind1,ind2)
A.close_output()
def generation_set(self,pop):
global logger
self.calc = tools.setup_calculator(self) #Set up calculator for atomic structures
#Set up calculator for fixed region calculations
if self.fixed_region:
self.static_calc = self.calc #May need to copy this
self.calc = tools.setup_fixed_region_calculator(self)
self.output.write('\n-------- Generation '+repr(self.generation)+' --------\n')
self.files[self.nindiv].write('Generation '+str(self.generation)+'\n')
if len(pop) == 0:
logger.info('Initializing structures')
offspring = self.initialize_structures()
self.population = offspring
else:
for i in range(len(pop)):
# Reset History index
pop[i].history_index=repr(pop[i].index)
# Select the next generation individuals
offspring = switches.selection_switch(pop, self.nindiv,
self.selection_scheme, self)
# Clone the selected individuals
offspring=[off1.duplicate() for off1 in offspring]
# Apply crossover to the offspring
self.output.write('\n--Applying Crossover--\n')
cxattempts = 0
for child1, child2 in zip(offspring[::2], offspring[1::2]):
if random.random() < self.cxpb:
child1,child2 = switches.crossover_switch(child1, child2, self)
cxattempts+=2
self.cxattempts=cxattempts
#DEBUG: Write first child
if 'MA' in self.debug:
inp_out.write_xyz(self.debugfile,offspring[0][0],'First Child '+
repr(offspring[0].history_index))
# Apply mutation to the offspring
self.output.write('\n--Applying Mutation--\n')
mutattempts = []
muts = []
for mutant in offspring:
if random.random() < self.mutpb:
if self.mutant_add:
mutant = mutant.duplicate()
mutant, optsel = switches.moves_switch(mutant,self)
mutattempts.append([mutant.history_index,optsel])
if self.mutant_add:
muts.append(mutant)
if self.mutant_add:
offspring.extend(muts)
self.mutattempts=mutattempts
#DEBUG: Write first offspring
if 'MA' in self.debug:
inp_out.write_xyz(self.debugfile,muts[0][0],'First Mutant '+\
repr(muts[0].history_index))
if 'stem' in self.fitness_scheme:
if self.stem_coeff==None:
logger.info('Setting STEM coeff (alpha)')
ind = offspring.pop()
from MAST.structopt.tools.StemCalc import find_stem_coeff
outs = find_stem_coeff(self,ind)
ind = outs[1]
ind.fitness = 0
ind.energy = 0
self.stem_coeff = outs[0]
logger.info('STEM Coeff = {0}'.format(self.stem_coeff))
self.output.write('stem_coeff Calculated to be: '+repr(self.stem_coeff)+'\n')
offspring.append(ind)
return offspring
def algorithm_par_mp1(self):
"""Subprogram for running parallel version of GA
Requires MPI4PY"""
comm = MPI.COMM_WORLD
rank = MPI.COMM_WORLD.Get_rank()
if rank==0:
if 'MA' in self.debug:
debug = True
else:
debug = False
self.algorithm_initialize()
self.convergence = False
convergence = False
while not convergence:
if rank==0:
self.calc = tools.setup_calculator(self) #Set up calculator for atomic structures
#Set up calculator for fixed region calculations
if self.fixed_region:
self.static_calc = self.calc #May need to copy this
self.calc = tools.setup_fixed_region_calculator(self)
pop = self.population
offspring = self.generation_set(self,pop)
# Identify the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if ind.energy==0]
#Evaluate the individuals with invalid fitness
self.output.write('\n--Evaluate Structures--\n')
proc_dist = int(comm.Get_size()/self.n_proc_eval)
ntimes=int(math.ceil(float(len(invalid_ind))/float(proc_dist)))
nadd=int(ntimes*proc_dist-len(invalid_ind))
maplist=[[] for n in range(ntimes)]
strt=0
for i in range(len(maplist)):
maplist[i]=[[self,indi] for indi in invalid_ind[strt:proc_dist+strt]]
strt+=proc_dist
for i in range(nadd):
maplist[len(maplist)-1].append([None,None])
masterlist = [i*self.n_proc_eval for i in range(proc_dist)]
else:
ntimes=None
masterlist = None
ntimes = comm.bcast(ntimes,root=0)
outs=[]
for i in range(ntimes):
if rank==0:
one=maplist[i]
for j in range(len(one)):
comm.send(ind, dest=1, tag=11)
elif rank == 1:
ind = comm.recv(source=0, tag=11)
else:
one=None
ind =comm.scatter(one,root=0)
out = switches.fitness_switch(ind)
else:
invalid_ind=[]
poorlist = []
for i in range(len(invalid_ind)):
if self.fitness_scheme=='STEM_Cost':
if self.stem_coeff==None:
ind = invalid_ind.pop()
from MAST.structopt.tools.StemCalc import find_stem_coeff
outs = find_stem_coeff(self,ind)
ind = outs[1]
self.stem_coeff = outs[0]
self.output.write('stem_coeff Calculated to be: '+repr(self.stem_coeff)+'\n')
pop.append(ind)
ind=invalid_ind[i]
if 'MA' in self.debug: write_xyz(self.debugfile,ind[0],'Individual to fitness_switch')
outs = switches.fitness_switch([self,ind])
self.output.write(outs[1])
invalid_ind[i]=outs[0]
if invalid_ind[i].energy == float('inf'):
poorlist.append(i)
self.output.write('Removing infinite energy individual '+repr(ind.history_index)+'\n')
elif invalid_ind[i].energy == float('nan'):
poorlist.append(i)
self.output.write('Removing nan energy individual '+repr(ind.history_index)+'\n')
self.output.flush()
if len(poorlist) != 0:
poorlist.sort(reverse=True)
for one in poorlist:
del invalid_ind[one]
if rank==0:
pop.extend(invalid_ind)
pop = self.generation_eval(pop)
convergence = comm.bcast(self.convergence, root=0)
if rank==0:
end_signal = self.algorithm_stats(self.population)
else:
end_signal = None
end_signal = comm.bcast(end_signal, root=0)
return end_signal
