def mutation_dups_zp(pop, Optimizer):
"""Predator function that selects individuals that are too similar based fitness and
replaces them with a zero point rotation of the structure
"""
fitlist = [one.fitness for one in pop]
nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin)
STR = ''
newpop = []
if len(nfitlist) != len(fitlist):
STR += 'Predator: Removed total of ' + repr(
len(fitlist) - len(nfitlist)) + ' from population\n'
otherlist = []
for i in range(len(pop)):
if i not in nindices:
STR += 'Predator: Removed ' + repr(pop[i].history_index) + '\n'
otherlist.append(pop[i])
else:
newpop.append(pop[i])
while len(newpop) < Optimizer.nindiv:
indiv = random.choice(otherlist).duplicate()
mutopts = Optimizer.mutation_options
Optimizer.mutation_options = ['ZP_Rotation']
indiv = moves_switch(indiv, Optimizer)
Optimizer.mutation_options = mutopts
newpop.append(indiv)
nindices.append(indiv.index)
STR += 'Predator: Adding mutated duplicates to new pop history=' + indiv.history_index + '\n'
nindices.sort()
if Optimizer.natural_selection_scheme == 'FUSSF':
for ind in newpop:
if ind.fingerprint == 0:
ind.fingerprint = get_fingerprint(Optimizer, ind,
Optimizer.fpbin,
Optimizer.fpcutoff)
if 'lambda,mu' in Optimizer.algorithm_type:
try:
mark = [
index for index, n in enumerate(nindices)
if n > Optimizer.nindiv - 1
][0]
except:
mark = Optimizer.nindiv
Optimizer.mark = mark
pop, str1 = lambdacommamu.lambdacommamu(newpop, Optimizer)
STR += str1
else:
pop = selection_switch(newpop, Optimizer.nindiv,
Optimizer.natural_selection_scheme, Optimizer)
pop = get_best(pop, len(pop))
return pop, STR
def mutation_dups_energy(pop, Optimizer):
"""Predator function that removes duplicates based on energy and replaces with mutations
"""
fitlist = [one.energy for one in pop]
nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin)
STR = ''
newpop = []
if len(nfitlist) != len(fitlist):
STR += 'Predator: Removed total of ' + repr(
len(fitlist) - len(nfitlist)) + ' from population\n'
otherlist = []
for i in range(len(pop)):
if i not in nindices:
STR += 'Predator: Removed ' + repr(pop[i].history_index) + '\n'
otherlist.append(pop[i])
else:
newpop.append(pop[i])
while len(newpop) < Optimizer.nindiv:
indiv = random.choice(otherlist).duplicate()
indiv, scheme = moves_switch(indiv, Optimizer)
indiv.energy = 1000
indiv.fitness = 1000
newpop.append(indiv)
STR += 'Predator: Adding mutated duplicates to new pop history=' + indiv.history_index + '\n'
nindices.append(indiv.index)
nindices.sort()
if Optimizer.natural_selection_scheme == 'fussf':
for ind in newpop:
if ind.fingerprint == 0:
ind.fingerprint = get_fingerprint(Optimizer, ind,
Optimizer.fpbin,
Optimizer.fpcutoff)
if 'lambda,mu' in Optimizer.algorithm_type:
try:
mark = [
index for index, n in enumerate(nindices)
if n > Optimizer.nindiv - 1
][0]
except:
mark = Optimizer.nindiv
Optimizer.mark = mark
pop, str1 = lambdacommamu.lambdacommamu(newpop, Optimizer)
STR += str1
else:
pop = selection_switch(newpop, Optimizer.nindiv,
Optimizer.natural_selection_scheme, Optimizer)
pop = get_best(pop, len(pop))
return pop, STR
def mutation_dups_adapt_stem(pop, Optimizer):
"""Predator function that removes individuals based on fitness and mutates replacements
"""
fitlist = [one.fitness for one in pop]
nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin)
STR = ''
newpop = []
if len(nfitlist) != len(fitlist):
STR+='Predator: Removed total of '+repr(len(fitlist)-len(nfitlist))+' from population\n'
otherlist = []
for i in range(len(pop)):
if i not in nindices:
STR+='Predator: Removed '+repr(pop[i].history_index)+'\n'
otherlist.append(pop[i])
else:
newpop.append(pop[i])
while len(newpop) < Optimizer.nindiv:
indiv = random.choice(otherlist).duplicate()
indiv, scheme = moves_switch(indiv,Optimizer)
indiv.energy = 1000
indiv.fitness = 1000
newpop.append(indiv)
STR+='Predator: Adding mutated duplicates to new pop history='+indiv.history_index+'\n'
nindices.append(indiv.index)
nindices.sort()
if Optimizer.natural_selection_scheme=='fussf':
for ind in newpop:
if ind.fingerprint == 0:
ind.fingerprint = get_fingerprint(Optimizer,ind,Optimizer.fpbin,Optimizer.fpcutoff)
if 'lambda,mu' in Optimizer.algorithm_type:
try:
mark = [ index for index,n in enumerate(nindices) if n > Optimizer.nindiv-1][0]
except:
mark = Optimizer.nindiv
Optimizer.mark = mark
pop, str1 = lambdacommamu.lambdacommamu(newpop, Optimizer)
STR+=str1
else:
pop = selection_switch(newpop, Optimizer.nindiv, Optimizer.natural_selection_scheme, Optimizer)
pop = get_best(pop,len(pop))
indiv = pop[0]
if (indiv.fitness/indiv.energy <2.0):
from MAST.structopt_stem.tools.StemCalc import find_stem_coeff
outs = find_stem_coeff(Optimizer,indiv)
ind = outs[1]
Optimizer.stem_coeff = outs[0]
STR+='Readjusting STEM Coeff = {0}'.format(Optimizer.stem_coeff))
return pop, STR
def mutation_dups_zp(pop, Optimizer):
"""Predator function that selects individuals that are too similar based fitness and
replaces them with a zero point rotation of the structure
"""
fitlist = [one.fitness for one in pop]
nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin)
STR = ''
newpop = []
if len(nfitlist) != len(fitlist):
STR+='Predator: Removed total of '+repr(len(fitlist)-len(nfitlist))+' from population\n'
otherlist = []
for i in range(len(pop)):
if i not in nindices:
STR+='Predator: Removed '+repr(pop[i].history_index)+'\n'
otherlist.append(pop[i])
else:
newpop.append(pop[i])
while len(newpop) < Optimizer.nindiv:
indiv = random.choice(otherlist).duplicate()
mutopts = Optimizer.mutation_options
Optimizer.mutation_options = ['ZP_Rotation']
indiv = moves_switch(indiv, Optimizer)
Optimizer.mutation_options = mutopts
newpop.append(indiv)
nindices.append(indiv.index)
STR+='Predator: Adding mutated duplicates to new pop history='+indiv.history_index+'\n'
nindices.sort()
if Optimizer.natural_selection_scheme=='FUSSF':
for ind in newpop:
if ind.fingerprint == 0:
ind.fingerprint = get_fingerprint(Optimizer,ind,Optimizer.fpbin,Optimizer.fpcutoff)
if 'lambda,mu' in Optimizer.algorithm_type:
try:
mark = [ index for index,n in enumerate(nindices) if n > Optimizer.nindiv-1][0]
except:
mark = Optimizer.nindiv
Optimizer.mark = mark
pop, str1 = lambdacommamu.lambdacommamu(newpop, Optimizer)
STR+=str1
else:
pop = selection_switch(newpop, Optimizer.nindiv, Optimizer.natural_selection_scheme, Optimizer)
pop = get_best(pop,len(pop))
return pop, STR
def adapting(pop, Optimizer):
"""Function to provide an adapting fitness function for GA evaluation
Input:
pop = population consisting of list of Individual Class objects to be evaluated
Optimizer = Optimizer class object with fitness parameters
Output:
pop = new population updated based on fitness evaluation
*** needs work ***
"""
fitlist = [one.fitness for one in pop]
nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin)
STR = ''
newpop = []
if len(nfitlist) != len(fitlist):
STR+='Predator: Removed total of '+repr(len(fitlist)-len(nfitlist))+' from population\n'
otherlist = []
for i in range(len(pop)):
if i not in nindices:
STR+='Predator: Removed '+repr(pop[i].history_index)+'\n'
otherlist.append(pop[i])
else:
newpop.append(pop[i])
while len(newpop) < Optimizer.nindiv:
Optimizer.output.write('Predator: Adding duplicates back')
indiv = random.choice(otherlist)
newpop.append(indiv)
STR+='Predator: Adding mutated duplicates to new pop history='+indiv.history_index+'\n'
if Optimizer.natural_selection_scheme=='fussf':
for ind in newpop:
if ind.fingerprint == 0:
ind.fingerprint = get_fingerprint(Optimizer,ind,Optimizer.fpbin,Optimizer.fpcutoff)
if genrep >= Optimizer.reqrep*Optimizer.adaptbegin:
ofusslim = Optimizer.fusslimit
nfusslim = ofusslim*math.exp(-Optimizer.adaptmultiplier*float(Optimizer.genrep)/float(Optimizer.reqrep))
Optimizer.fusslimit = nfusslim
else:
ofusslim = Optimizer.fusslimit
pop = selection_switch(newpop, Optimizer.nindiv,Optimizer.natural_selection_scheme,Optimizer)
pop = get_best(pop,len(pop))
Optimizer.fusslimit=ofusslim
return pop, STR
def mutation_dups_energy(pop, Optimizer):
"""Predator function that removes duplicates based on energy and replaces with mutations
"""
fitlist = [one.energy for one in pop]
nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin)
STR = ''
newpop = []
if len(nfitlist) != len(fitlist):
STR+='Predator: Removed total of '+repr(len(fitlist)-len(nfitlist))+' from population\n'
otherlist = []
for i in range(len(pop)):
if i not in nindices:
STR+='Predator: Removed '+repr(pop[i].history_index)+'\n'
otherlist.append(pop[i])
else:
newpop.append(pop[i])
while len(newpop) < Optimizer.nindiv:
indiv = random.choice(otherlist).duplicate()
indiv, scheme = moves_switch(indiv,Optimizer)
indiv.energy = 1000
indiv.fitness = 1000
newpop.append(indiv)
STR+='Predator: Adding mutated duplicates to new pop history='+indiv.history_index+'\n'
nindices.append(indiv.index)
nindices.sort()
if Optimizer.natural_selection_scheme=='fussf':
for ind in newpop:
if ind.fingerprint == 0:
ind.fingerprint = get_fingerprint(Optimizer,ind,Optimizer.fpbin,Optimizer.fpcutoff)
if 'lambda,mu' in Optimizer.algorithm_type:
try:
mark = [ index for index,n in enumerate(nindices) if n > Optimizer.nindiv-1][0]
except:
mark = Optimizer.nindiv
Optimizer.mark = mark
pop, str1 = lambdacommamu.lambdacommamu(newpop, Optimizer)
STR+=str1
else:
pop = selection_switch(newpop, Optimizer.nindiv, Optimizer.natural_selection_scheme, Optimizer)
pop = get_best(pop,len(pop))
return pop, STR
# Add explosion prevention protection
# if 'prevent_explosions' in globals():
# dist=[10]*len(indiv)
# for i in range(len(indiv)):
# for j in range(len(indiv)):
# if i != j:
# dist[j]=indiv.get_distance(i,j,mic=True)
# if min(dist) > 3.5:
# energy+=10
#if Optimizer.structure=='Defect':
# individ.force=force
individ.energy=energy
individ.buli=bul
individ.pressure=pressure
individ.volume=volume
if Optimizer.structure=='Cluster':
indiv.translate([-250,-250,-250])
if Optimizer.fingerprinting:
individ.fingerprint=get_fingerprint(Optimizer,individ,Optimizer.fpbin,Optimizer.fpcutoff)
if Optimizer.parallel:
calc.clean()
signal = 'Evaluated individual '+repr(individ.index)+' on '+repr(rank)+'\n'
signal +=STR
else:
signal=STR
return energy, bul, individ, signal
def mutation_dups_quench(pop, Optimizer):
"""Predator function that removes individuals based on fitness and mutates replacements
Also quenches top individuals
"""
fitlist = [one.fitness for one in pop]
nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin)
STR = ''
newpop = []
if len(nfitlist) != len(fitlist):
STR += 'Predator: Removed total of ' + repr(
len(fitlist) - len(nfitlist)) + ' from population\n'
otherlist = []
for i in range(len(pop)):
if i not in nindices:
STR += 'Predator: Removed ' + repr(pop[i].history_index) + '\n'
otherlist.append(pop[i])
else:
newpop.append(pop[i])
while len(newpop) < Optimizer.nindiv:
indiv = random.choice(otherlist).duplicate()
indiv, scheme = moves_switch(indiv, Optimizer)
indiv.energy = 1000
indiv.fitness = 1000
newpop.append(indiv)
STR += 'Predator: Adding mutated duplicates to new pop history=' + indiv.history_index + '\n'
nindices.append(indiv.index)
nindices.sort()
if Optimizer.natural_selection_scheme == 'fussf':
for ind in newpop:
if ind.fingerprint == 0:
ind.fingerprint = get_fingerprint(Optimizer, ind,
Optimizer.fpbin,
Optimizer.fpcutoff)
if 'lambda,mu' in Optimizer.algorithm_type:
try:
mark = [
index for index, n in enumerate(nindices)
if n > Optimizer.nindiv - 1
][0]
except:
mark = Optimizer.nindiv
Optimizer.mark = mark
pop, str1 = lambdacommamu.lambdacommamu(newpop, Optimizer)
STR += str1
else:
pop = selection_switch(newpop, Optimizer.nindiv,
Optimizer.natural_selection_scheme, Optimizer)
pop = get_best(pop, len(pop))
if Optimizer.genrep > 10:
from MAST.structopt_stem.moves.quench import quench
import os
olammpsvar = os.environ['LAMMPS_COMMAND']
try:
from mpi4py import MPI
if '-n' in olammpsvar:
lcommand = olammpsvar.split('-n')
lcommand[1] = lcommand[1].split()
nproc = MPI.COMM_WORLD.Get_size()
os.environ['LAMMPS_COMMAND'] = '{0}-n {1} {2}'.format(
lcommand[0], nproc, lcommand[1][1])
except:
pass
oqns2 = Optimizer.quench_n_steps_2
Optimizer.quench_n_steps_2 = 100000
opar = Optimizer.parallel
Optimizer.parallel = False
for i in range(3):
pop[i] = quench(pop[i], Optimizer)
Optimizer.quench_n_steps_2 = oqns2
os.environ['LAMMPS_COMMAND'] = olammpsvar
Optimizer.parallel = opar
return pop, STR
def mutation_dups_quench(pop, Optimizer):
"""Predator function that removes individuals based on fitness and mutates replacements
Also quenches top individuals
"""
fitlist = [one.fitness for one in pop]
nfitlist, nindices = remove_duplicates(fitlist, Optimizer.demin)
STR = ''
newpop = []
if len(nfitlist) != len(fitlist):
STR+='Predator: Removed total of '+repr(len(fitlist)-len(nfitlist))+' from population\n'
otherlist = []
for i in range(len(pop)):
if i not in nindices:
STR+='Predator: Removed '+repr(pop[i].history_index)+'\n'
otherlist.append(pop[i])
else:
newpop.append(pop[i])
while len(newpop) < Optimizer.nindiv:
indiv = random.choice(otherlist).duplicate()
indiv, scheme = moves_switch(indiv,Optimizer)
indiv.energy = 1000
indiv.fitness = 1000
newpop.append(indiv)
STR+='Predator: Adding mutated duplicates to new pop history='+indiv.history_index+'\n'
nindices.append(indiv.index)
nindices.sort()
if Optimizer.natural_selection_scheme=='fussf':
for ind in newpop:
if ind.fingerprint == 0:
ind.fingerprint = get_fingerprint(Optimizer,ind,Optimizer.fpbin,Optimizer.fpcutoff)
if 'lambda,mu' in Optimizer.algorithm_type:
try:
mark = [ index for index,n in enumerate(nindices) if n > Optimizer.nindiv-1][0]
except:
mark = Optimizer.nindiv
Optimizer.mark = mark
pop, str1 = lambdacommamu.lambdacommamu(newpop, Optimizer)
STR+=str1
else:
pop = selection_switch(newpop, Optimizer.nindiv, Optimizer.natural_selection_scheme, Optimizer)
pop = get_best(pop,len(pop))
if Optimizer.genrep >10:
from MAST.structopt_stem.moves.quench import quench
import os
olammpsvar = os.environ['LAMMPS_COMMAND']
try:
from mpi4py import MPI
if '-n' in olammpsvar:
lcommand = olammpsvar.split('-n')
lcommand[1]=lcommand[1].split()
nproc = MPI.COMM_WORLD.Get_size()
os.environ['LAMMPS_COMMAND'] = '{0}-n {1} {2}'.format(lcommand[0],nproc,lcommand[1][1])
except:
pass
oqns2 = Optimizer.quench_n_steps_2
Optimizer.quench_n_steps_2 = 100000
opar = Optimizer.parallel
Optimizer.parallel = False
for i in range(3):
pop[i] = quench(pop[i],Optimizer)
Optimizer.quench_n_steps_2 = oqns2
os.environ['LAMMPS_COMMAND'] = olammpsvar
Optimizer.parallel = opar
return pop, STR
def eval_energy(Optimizer, individ):
"""Function to evaluate energy of an individual
Inputs:
input = [Optimizer class object with parameters, Individual class structure to be evaluated]
Outputs:
energy, bul, individ, signal
energy = energy of Individual evaluated
bul = bulk structure of Individual if simulation structure is Defect
individ = Individual class structure evaluated
signal = string of information about evaluation
"""
#logger = initialize_logger(Optimizer.loggername)
logger = logging.getLogger(Optimizer.loggername)
if 'MAST' in Optimizer.calc_method:
energy = individ.energy
bul = individ.bulki
signal = 'Received MAST structure\n'
logger.info('Received individual index = {0} from MAST with energy {1}. Returning with no evaluation'.format(
individ.index, individ.energy))
else:
if Optimizer.parallel:
rank = MPI.COMM_WORLD.Get_rank()
logger.info('Received individual HI = {0} with energy {1} for energy evaluation'.format(
individ.history_index, individ.energy))
STR='----Individual ' + str(individ.history_index)+ ' Optimization----\n'
indiv=individ[0]
if 'EE' in Optimizer.debug:
debug = True
else:
debug = False
if debug:
write_xyz(Optimizer.debugfile,indiv,'Received by eval_energy')
Optimizer.debugfile.flush()
logger.debug('Writing recieved individual to debug file')
# Establish individual structure for evaluation. Piece together regions when necessary.
if Optimizer.structure=='Defect':
indi=indiv.copy()
bulk=individ.bulki
nat=indi.get_number_of_atoms()
if debug:
logger.info('Extending defect structure to include bulk len(r1+r2)={0} len(bulk)={1}'.format(nat,len(bulk)))
csize=bulk.get_cell()
totalsol=Atoms(cell=csize, pbc=True)
totalsol.extend(indi)
totalsol.extend(bulk)
for sym,c,m,u in Optimizer.atomlist:
nc=len([atm for atm in totalsol if atm.symbol==sym])
STR+='Defect configuration contains '+repr(nc)+' '+repr(sym)+' atoms\n'
elif Optimizer.structure=='Surface':
totalsol=Atoms()
totalsol.extend(indiv)
nat=indiv.get_number_of_atoms()
totalsol.extend(individ.bulki)
if debug:
logger.info('Extending surface structure to include bulk len(r1+r2)={0} len(bulk)={1}'.format(nat,len(individ.bulki)))
for sym,c,m,u in Optimizer.atomlist:
nc=len([atm for atm in totalsol if atm.symbol==sym])
STR+='Surface-Bulk configuration contains '+repr(nc)+' '+repr(sym)+' atoms\n'
cell=numpy.maximum.reduce(indiv.get_cell())
totalsol.set_cell([cell[0],cell[1],500])
totalsol.set_pbc([True,True,False])
elif Optimizer.structure=='Cluster':
# logger.info('M:')
totalsol = indiv.copy()
nat = len(totalsol)
if debug:
logger.info('Extending cluster with {0} atoms to center of evaluation box of size {1}'.format(nat,Optimizer.large_box_size))
origcell = indiv.get_cell()
#print 'rank, eval_energy.cell',rank,origcell
if Optimizer.forcing != 'RelaxBox':
totalsol.set_cell([Optimizer.large_box_size,Optimizer.large_box_size,Optimizer.large_box_size])
totalsol.translate([Optimizer.large_box_size/2.0,Optimizer.large_box_size/2.0,Optimizer.large_box_size/2.0])
# logger.info('M: set cell')
elif Optimizer.structure=='Crystal':
totalsol = indiv.copy()
nat = len(totalsol)
else:
print 'WARNING: In EvalEnergy. Optimizer.structure not recognized'
logger.warning('Optimizer.structure not recognized')
# Check for atoms that are too close or out of constrained location
if Optimizer.constrain_position:
if Optimizer.structure=='Defect':
if debug:
logger.info('Constraining positions of defect')
totalsol, stro = constrain_positions(totalsol, Optimizer.solidbulk, Optimizer.sf)
if debug:
logger.info(stro)
STR+=str0
min_len=0.7
if not Optimizer.fixed_region:
if debug:
logger.info('Running check minimum distance')
# logger.info('M:check dist')
totalsol, STR = check_min_dist(Optimizer, totalsol, Optimizer.structure, nat, min_len, STR)
if debug:
write_xyz(Optimizer.debugfile,totalsol,'After minlength check')
Optimizer.debugfile.flush()
logger.debug('Writing individual after checking minimum length')
# Set calculator to use to get forces/energies
if Optimizer.parallel:
# logger.info('M:start calculator')
calc = setup_calculator(Optimizer)
if Optimizer.fixed_region:
if debug:
logger.info('Setting up fixed region calculator')
pms=copy.deepcopy(calc.parameters)
try:
pms['mass'][len(pms['mass'])-1] += '\ngroup RO id >= {0}\nfix freeze RO setforce 0.0 0.0 0.0\n'.format(nat)
except KeyError:
pms['pair_coeff'][0] += '\ngroup RO id >= {0}\nfix freeze RO setforce 0.0 0.0 0.0\n'.format(nat)
calc = LAMMPS(parameters=pms, files=calc.files, keep_tmp_files=calc.keep_tmp_files, tmp_dir=calc.tmp_dir)
lmin = copy.copy(Optimizer.lammps_min)
if debug:
logger.info('Setting up no local minimization calculator')
Optimizer.lammps_min = None
Optimizer.static_calc = setup_calculator(Optimizer)
Optimizer.lammps_min = lmin
else:
calc=Optimizer.calc
totalsol.set_calculator(calc)
totalsol.set_pbc(True)
# Perform Energy Minimization
if not Optimizer.parallel:
if debug:
write_xyz(Optimizer.debugfile,totalsol,'Individual sent to Energy Minimizer')
logger.debug('Writing structure sent to energy minimizer')
try:
cwd = os.getcwd()
if Optimizer.ase_min == True:
if debug:
logger.info('Running ASE minimizer')
if Optimizer.calc_method=='LennardJones':
logger.warn('Must run ase LJ calculator with pbc=False')
totalsol.set_pbc(False)
totalsol, energy, pressure, volume, STR = run_ase_min(totalsol, Optimizer.ase_min_fmax, Optimizer.ase_min_maxsteps, Optimizer.fitness_scheme, STR)
else:
if debug:
logger.info('Running local energy calculator')
if Optimizer.fixed_region:
totalsol, pea, energy, pressure, volume, STR = run_energy_eval(totalsol, Optimizer.calc_method, Optimizer.fixed_region, Optimizer.fitness_scheme, STR, Optimizer.static_calc)
else:
# logger.info('M:start run_energy_eval')
totalsol, pea, energy, pressure, volume, STR = run_energy_eval(totalsol, Optimizer.calc_method, False, Optimizer.fitness_scheme, STR)
logger.info('M:finish run_energy_eval, energy = {0} @ rank ={1}'.format(energy,rank))
except Exception, e:
logger.critical('Error in energy evaluation: {0}'.format(e), exc_info=True)
path = os.path.join(cwd,'TroubledLammps')
if not os.path.exists(path):
os.mkdir(path)
#Copy files over
shutil.copyfile(calc.trajfile,os.path.join(path,os.path.basename(calc.trajfile)))
shutil.copyfile(calc.infile,os.path.join(path,os.path.basename(calc.infile)))
shutil.copyfile(calc.logfile,os.path.join(path,os.path.basename(calc.logfile)))
shutil.copyfile(calc.datafile,os.path.join(path,os.path.basename(calc.datafile)))
raise RuntimeError('{0}:{1}'.format(Exception,e))
if not Optimizer.parallel:
if debug:
write_xyz(Optimizer.debugfile,totalsol,'Individual after Energy Minimization')
Optimizer.debugfile.flush()
logger.debug('Writing structure recieved from energy minimizer')
# Separate structures into distinct pieces
if Optimizer.structure=='Defect':
if Optimizer.fixed_region==True or Optimizer.finddefects==False:
if debug:
logger.info('Identifying atoms in defect structure based on ID')
individ[0]=totalsol[0:nat]
bul=totalsol[(nat):len(totalsol)]
individ[0].set_cell(csize)
else:
if debug:
logger.info('Applying find defects scheme to identify R1 and R2 for Defect')
if 'FD' in Optimizer.debug:
outt=find_defects(totalsol,Optimizer.solidbulk,Optimizer.sf,atomlistcheck=Optimizer.atomlist,trackvacs=Optimizer.trackvacs,trackswaps=Optimizer.trackswaps,debug=Optimizer.debugfile)
else:
outt=find_defects(totalsol,Optimizer.solidbulk,Optimizer.sf,atomlistcheck=Optimizer.atomlist,trackvacs=Optimizer.trackvacs,trackswaps=Optimizer.trackswaps,debug=False)
individ[0]=outt[0]
bul=outt[1]
individ.vacancies = outt[2]
individ.swaps = outt[3]
STR += outt[4]
indiv=individ[0]
elif Optimizer.structure=='Surface':
if debug:
logger.info('Finding surface top layer')
top,bul=find_top_layer(totalsol,Optimizer.surftopthick)
indiv=top.copy()
individ[0]=top.copy()
bul = Atoms()
elif Optimizer.structure=='Crystal':
if debug:
logger.info('Checking crystal cell type')
celltype = check_cell_type(totalsol)
STR+='Cell structure = {0}\n'.format(celltype)
bul = Atoms()
individ[0] = totalsol.copy()
elif Optimizer.structure=='Cluster':
volume = get_cluster_volume(totalsol)
bul = Atoms()
if debug:
logger.info('Translating cluster back to smaller box size location')
if Optimizer.forcing != 'RelaxBox':
totalsol.translate([-Optimizer.large_box_size/2.0,-Optimizer.large_box_size/2.0,-Optimizer.large_box_size/2.0])
totalsol.set_cell(origcell)
individ[0] = totalsol.copy()
# Add concentration energy dependence
if Optimizer.forcing=='energy_bias':
if debug:
logger.info('Applying energy bias for atoms with different number of atoms of type than in atomlist')
n=[0]*len(Optimizer.atomlist)
for i in range(len(Optimizer.atomlist)):
n[i]=len([inds for inds in totalsol if inds.symbol==Optimizer.atomlist[i][0]])
n[i]=abs(n[i]-Optimizer.atomlist[i][1])
factor=sum(n)**3
energy=(energy+factor)/totalsol.get_number_of_atoms()
STR+='Energy with Bias = {0}\n'.format(energy)
elif Optimizer.forcing=='chem_pot':
if debug:
logger.info('Applying chemical potential bias for atoms with different number of atoms of type than in atomlist')
n=[0]*len(Optimizer.atomlist)
for i in range(len(Optimizer.atomlist)):
n[i]=len([inds for inds in totalsol if inds.symbol==Optimizer.atomlist[i][0]])
n[i]=n[i]*Optimizer.atomlist[i][3]
factor=sum(n)
energy=(energy+factor)/totalsol.get_number_of_atoms()
STR+='Energy with Chemical Potential = {0}\n'.format(energy)
individ.energy=energy
individ.buli=bul
individ.pressure=pressure
individ.volume=volume
#Add pealist to include atom index based on sorted PE.
logger.info('before sort{0}'.format(individ.energy))
sort_pealist(Optimizer,individ,pea)
energy = individ.energy
logger.info('after sort {0}'.format(individ.energy))
if Optimizer.fingerprinting:
if debug:
logger.info('Identifying fingerprint of new structure')
individ.fingerprint=get_fingerprint(Optimizer,individ,Optimizer.fpbin,Optimizer.fpcutoff)
if Optimizer.parallel:
calc.clean()
signal = 'Evaluated individual {0} on {1}\n'.format(individ.index,rank)
signal +=STR
else:
signal=STR
# dist=[10]*len(indiv)
# for i in range(len(indiv)):
# for j in range(len(indiv)):
# if i != j:
# dist[j]=indiv.get_distance(i,j,mic=True)
# if min(dist) > 3.5:
# energy+=10
#if Optimizer.structure=='Defect':
# individ.force=force
individ.energy = energy
individ.buli = bul
individ.pressure = pressure
individ.volume = volume
if Optimizer.structure == 'Cluster':
indiv.translate([-250, -250, -250])
if Optimizer.fingerprinting:
individ.fingerprint = get_fingerprint(Optimizer, individ,
Optimizer.fpbin,
Optimizer.fpcutoff)
if Optimizer.parallel:
calc.clean()
signal = 'Evaluated individual ' + repr(
individ.index) + ' on ' + repr(rank) + '\n'
signal += STR
else:
signal = STR
return energy, bul, individ, signal