def run_task(self,fw_spec):
global nIter
nIter = 0
jobID = fw_spec['jobID']
job = db2BulkJob(jobID)
setupEnv(job)
job.printFields() # confirm job details
existsPrecalc = job.precalcxc != 'None'
t0 = time.time()
def getBulkEnergy(latticeParams):
"""For a given set of bravais lattice parameters, optimize atomic coordinates and return minimum energy"""
global nIter
nIter += 1
atoms = job.fromParams(latticeParams)
calc = job.calc(restart=True) if existsPrecalc else job.calc()
if existsPrecalc:
preCalc = job.calc(precalc=True)
job.optimizePos(atoms,preCalc,saveWF = True)
if job.dftcode =='gpaw':
atoms,calc = job.gpawRestart()
job.optimizePos(atoms,calc)
energy = atoms.get_potential_energy()
with paropen('lattice_opt.log','a') as logfile:
logfile.write('%s\t%s\n' %(energy,latticeParams))
parprint('%s\t%s\n' %(energy,latticeParams))
if job.dftcode=='gpaw':
atoms.calc.write('inp.gpw', mode='all') #for use in getXCContribs
io.write('out.traj',atoms)
return energy
optimizedLatticeParams = fmin(getBulkEnergy,job.iGuess(),ftol=1,xtol=job.xtol)
avgtime = (time.time()-t0)/float(nIter)/60.0 #min
### Get info from final state
optAtoms = io.read('out.traj')
optCell = optAtoms.get_cell()
pos = optAtoms.get_scaled_positions()
e0 = optAtoms.get_potential_energy()
f0 = optAtoms.get_forces()
magmom = optAtoms.get_magnetic_moments() if any([x>0 for x in job.magmoms]) else np.zeros(len(optAtoms))
resultDict = {'latticeParams': optimizedLatticeParams,'avgtime':avgtime,'optCell':optCell,'pos':pos,'e0':e0,'f0':f0,'magmom':magmom,'niter':nIter}
return FWAction( stored_data=resultDict,mod_spec=[{'_push': resultDict}])
def initialize(jobID, latticeParams):
jobObject = db2BulkJob(jobID)
bulkObject = db2Bulk(jobObject.bulkpth)
calcObject = db2Calc(jobObject.calcid)
dft = jobObject.dftCode
preCalcObject = deepcopy(calcObject)
preCalcObject.xc = jobObject.precalcxc
existsPrecalc = str(preCalcObject.xc) != 'None'
atoms = bulkObject.fromParams(latticeParams)
magmoms = bulkObject.magInit(calcObject.magmom)
return (jobObject, bulkObject, calcObject, dft, preCalcObject,
preCalcObject, existsPrecalc, atoms, magmoms)
def getXCcontribs(jobID):
jobObject = db2BulkJob(jobID)
if jobObject.dftCode == 'gpaw':
from gpaw import restart
from gpaw.xc.bee import BEEFEnsemble
atoms, calc = restart('inp.gpw',
setups='sg15',
xc='mBEEF',
convergence={'energy': 5e-4},
txt='mbeef.txt')
atoms.get_potential_energy()
beef = BEEFEnsemble(calc)
return beef.mbeef_exchange_energy_contribs()
else:
return np.zeros((8, 8))
def run_task(self,fw_spec):
jobID,latticeParams = fw_spec['jobID'],fw_spec['latticeParams'] # WHY IS LATTICEPARAMS [[FLOAT]] INSTEAD OF [FLOAT]?
job = db2BulkJob(jobID)
setupEnv(job)
existsPrecalc = job.precalcxc != 'None'
optAtoms = job.fromParams(latticeParams[0])
optCell = optAtoms.get_cell()
strains = np.linspace(1-job.strain,1+job.strain,9)
calc = job.calc(restart=True) if existsPrecalc else job.calc()
vol,eng = [],[]
for i, strain in enumerate(strains):
atoms = deepcopy(optAtoms)
atoms.set_cell(optCell*strain,scale_atoms=True)
if existsPrecalc:
preCalc = job.calc(precalc=True)
job.optimizePos(atoms,preCalc,saveWF = True)
if job.dftcode =='gpaw':
atoms,calc = job.gpawRestart()
job.optimizePos(atoms,calc)
energy = atoms.get_potential_energy()
volume = atoms.get_volume()
vol.append(deepcopy(volume))
eng.append(deepcopy(energy))
parprint('%f %f'%(strain,energy))
aHat,quadR2 = quadFit(np.array(deepcopy(vol)),np.array(deepcopy(eng)))
try:
eos = EquationOfState(vol,eng)
v0, e0, b = eos.fit()
eos.plot(filename='bulk-eos.png',show=False)
b0= b/kJ*1e24 #GPa use this value if EOS doesn't fail
except ValueError: # too bad of a fit for ASE to handle
b0 = aHat*2*vol[4]*160.2 # units: eV/A^6 * A^3 * 1, where 1 === 160.2 GPa*A^3/eV
return FWAction( stored_data= {'b0':b0,'quadR2':quadR2}
,mod_spec=[{'_push': {'b0':b0,'quadR2':quadR2}}])
def run_task(self,fw_spec):
jobID = fw_spec['jobID']
job = db2BulkJob(jobID)
resultNames = ['optCell','pos','magmom','e0','f0','b0','quadR2','xcContribs','avgtime','niter']
results = [fw_spec[x][0] for x in resultNames]
resultDict = {n:fw_spec[n] for n in resultNames}
result = BulkResult(*([jobID]+results))
result.addToASEdb(job)
insertObject(result) # add result to bulkresult table
updateDB('bulkjob','status','id',jobID,'complete')
return FWAction(stored_data= resultDict)
def run_task(self,fw_spec):
jobID = fw_spec['jobID']
job = db2BulkJob(jobID)
setupEnv(job)
if job.dftcode == 'gpaw':
from gpaw import restart
from gpaw.xc.bee import BEEFEnsemble
atoms,calc = restart('inp.gpw', setups='sg15', xc='mBEEF', convergence={'energy': 5e-4}, txt='mbeef.txt')
atoms.get_potential_energy()
beef = BEEFEnsemble(calc)
xcContribs = beef.mbeef_exchange_energy_contribs()
else:
xcContribs = np.zeros((8,8))
return FWAction( stored_data={'xcContribs': xcContribs}
,mod_spec=[{'_push': {'xcContribs': xcContribs}}])
def main():
try:
deleteTable('tentative') #WHAT IS GOING ON HERE?
except:
pass
try:
createTentativeBulkTable()
except:
pass
domains = [
filteredTrajDomain, filteredXCDomain, filteredPSPDomain,
filteredPWDomain, filteredKPTDomain, filteredPreCalcXCDomain,
filteredDFTDomain
]
combos = product(*domains)
ncombo = reduce(mul, [len(x) for x in domains])
for i, c in enumerate(combos):
print '%f%' % (i / float(ncombo) * 100)
tentativeInput = c[:5] + tuple(defaults) + c[5:]
tentativeBulkjob = BulkJob(*tentativeInput)
if tentativeBulkjob.xc == 'mBEEF': #defaults slightly different for mBEEF
tentativeBulkjob.sigma += 0.1
tentativeBulkjob.sigma -= 0.05
ID, status = insertObject(tentativeBulkjob, tentative=True)
print "%d tentative jobs" % countDB('tentative')
valid = [
db2BulkJob(x[0], tentative=True)
for x in plotQuery('tentative', ['tentative.id'], constraints)
]
new = 0
for z in range(len(valid)):
ID, status = insertObject(valid[z])
if status == 'inserted': new += 1
print "%d valid jobs, %d are new" % (len(valid), new)
deleteTable('tentative')