def eos(self, atoms, name):
opts = self.opts
traj = PickleTrajectory(self.get_filename(name, 'traj'), 'w', atoms)
eps = 0.01
strains = np.linspace(1 - eps, 1 + eps, 5)
v1 = atoms.get_volume()
volumes = strains**3 * v1
energies = []
cell1 = atoms.cell
for s in strains:
atoms.set_cell(cell1 * s, scale_atoms=True)
energies.append(atoms.get_potential_energy())
traj.write(atoms)
traj.close()
eos = EquationOfState(volumes, energies, opts.eos_type)
v0, e0, B = eos.fit()
atoms.set_cell(cell1 * (v0 / v1)**(1 / 3), scale_atoms=True)
data = {'volumes': volumes,
'energies': energies,
'fitted_energy': e0,
'fitted_volume': v0,
'bulk_modulus': B,
'eos_type': opts.eos_type}
return data
def breed(particles,objects,gen):
sortedIndices = numpy.argsort(numpy.asarray(objects))
particlestobreed = []
explambda = numpy.log(10) / (NPsPerGen - 1)
for i in range(len(particles)):
trialvar = numpy.random.random()
if (trialvar <= numpy.exp(-1 * explambda * i)):
particlestobreed.append(particles[sortedIndices[i]])
NewGen = []
for i in range(NPsPerGen):
first = numpy.random.randint(len(particlestobreed))
second = numpy.random.randint(len(particlestobreed))
gene1 = numpy.random.randint(2)
gene2 = numpy.random.randint(2)
if (gene1 == 0):
type1 = particlestobreed[first].gene[0]
else:
type1 = particlestobreed[second].gene[0]
if (gene2 == 0):
type2 = particlestobreed[first].gene[1]
else:
type2 = particlestobreed[second].gene[1]
if (particlestobreed[first].gene[2] < particlestobreed[second].gene[2]):
minval = particlestobreed[first].gene[2]
maxval = particlestobreed[second].gene[2]
else:
minval = particlestobreed[second].gene[2]
maxval = particlestobreed[first].gene[2]
numbertype1 = numpy.random.randint(minval, maxval + 1)
NewGen.append(createparticle(type1,type2,numbertype1))
traj = PickleTrajectory('generation'+str(gen)+ '_' + str(i) + '.traj','w')
NewGen[i].gene = (type1,type2,numbertype1)
traj.write(ParticleList[i])
traj.close()
return NewGen
def fit_bond_length(self, name, atoms, data=None):
N, x = self.fit
d0 = atoms.get_distance(0, 1)
distances = np.linspace(d0 * (1 - x), d0 * (1 + x), N)
energies = []
traj = PickleTrajectory(self.get_filename(name, 'fit.traj'), 'w')
for d in distances:
atoms.set_distance(0, 1, d)
energies.append(atoms.get_potential_energy())
self.check_occupation_numbers(atoms)
traj.write(atoms)
traj.close()
if data is not None:
data['distances'] = distances
data['energies'] = energies
else:
assert N % 2 == 1
data = {
'energy': energies[N // 2],
'distances': distances,
'energies': energies
}
return data
def fit_volume(self, name, atoms, data=None):
N, x = self.fit
cell0 = atoms.get_cell()
v = atoms.get_volume()
if x > 0:
strains = np.linspace(1 - x, 1 + x, N)
else:
strains = np.linspace(1 + x / v, 1 - x / v, N)**(1. / 3)
energies = []
traj = PickleTrajectory(self.get_filename(name, 'fit.traj'), 'w')
for s in strains:
atoms.set_cell(cell0 * s, scale_atoms=True)
energies.append(atoms.get_potential_energy())
traj.write(atoms)
traj.close()
if data is not None:
data['strains'] = strains
data['energies'] = energies
else:
assert N % 2 == 1
data = {
'energy': energies[N // 2],
'strains': strains,
'energies': energies
}
return data
def accept_move(self):
e = self.atoms.get_potential_energy()
self.lists['energy'].append(e)
self.lists['counts'].append(1)
self.lists['coordinations'].append(self.sumcoordinations(self.surfmove.coordinations))
self.lists['types'].append(self.sumcoordinations(self.surfmove.types)) #See sumcoordinations
self.lists['vac_coordinations'].append(self.sumcoordinations(self.surfmove.vacant_coordinations))
self.lists['vac_types'].append(self.sumcoordinations(self.surfmove.vacant_types))
if self.mc_step>=self.total_steps/2:
if self.id_relax == None:
self.id_relax = self.n_accept
unrelax_energy, relax_energy = self.relax()
self.lists['unrelaxed_energies'].append(unrelax_energy)
self.lists['relaxed_energies'].append(relax_energy)
e += relax_energy - unrelax_energy
if e < self.ground_state_energy:
#now store .amc in tmpfn[1] and 0000* in tmpfn[0]
tmpfn = self.filename.split(".")
traj = PickleTrajectory(tmpfn[0]+".traj", 'w', self.atoms, master=True, backup=False)
traj.write()
traj.close()
#write(filename=tmpfn[0]+".traj",images=self.atoms)
self.ground_state_energy = e
else:
self.lists['unrelaxed_energies'].append(np.nan)
self.lists['relaxed_energies'].append(np.nan)
self.mc_step += 1
self.n_accept += 1
def fit_volume(self, name, atoms, data=None):
N, x = self.fit
cell0 = atoms.get_cell()
v = atoms.get_volume()
if x > 0:
strains = np.linspace(1 - x, 1 + x, N)
else:
strains = np.linspace(1 + x / v, 1 - x / v, N)**(1./3)
energies = []
traj = PickleTrajectory(self.get_filename(name, 'fit.traj'), 'w')
for s in strains:
atoms.set_cell(cell0 * s, scale_atoms=True)
energies.append(atoms.get_potential_energy())
traj.write(atoms)
traj.close()
if data is not None:
data['strains'] = strains
data['energies'] = energies
else:
assert N % 2 == 1
data = {'energy': energies[N // 2],
'strains': strains,
'energies': energies}
return data
def _test_timestepping(self, t):
#XXX DEBUG START
if debug and os.path.isfile('%s_%d.gpw' % (self.tdname, t)):
return
#XXX DEBUG END
timestep = self.timesteps[t]
self.assertAlmostEqual(self.duration % timestep, 0.0, 12)
niter = int(self.duration / timestep)
ndiv = 1 #XXX
traj = PickleTrajectory('%s_%d.traj' % (self.tdname, t),
'w', self.tdcalc.get_atoms())
t0 = time.time()
f = paropen('%s_%d.log' % (self.tdname, t), 'w')
print >>f, 'propagator: %s, duration: %6.1f as, timestep: %5.2f as, ' \
'niter: %d' % (self.propagator, self.duration, timestep, niter)
for i in range(1, niter+1):
# XXX bare bones propagation without all the nonsense
self.tdcalc.propagator.propagate(self.tdcalc.time,
timestep * attosec_to_autime)
self.tdcalc.time += timestep * attosec_to_autime
self.tdcalc.niter += 1
if i % ndiv == 0:
rate = 60 * ndiv / (time.time()-t0)
ekin = self.tdcalc.atoms.get_kinetic_energy()
epot = self.tdcalc.get_td_energy() * Hartree
F_av = np.zeros((len(self.tdcalc.atoms), 3))
print >>f, 'i=%06d, time=%6.1f as, rate=%6.2f min^-1, ' \
'ekin=%13.9f eV, epot=%13.9f eV, etot=%13.9f eV' \
% (i, timestep * i, rate, ekin, epot, ekin + epot)
t0 = time.time()
# Hack to prevent calls to GPAW::get_potential_energy when saving
spa = self.tdcalc.get_atoms()
spc = SinglePointCalculator(epot, F_av, None, None, spa)
spa.set_calculator(spc)
traj.write(spa)
f.close()
traj.close()
self.tdcalc.write('%s_%d.gpw' % (self.tdname, t), mode='all')
# Save density and wavefunctions to binary
gd, finegd = self.tdcalc.wfs.gd, self.tdcalc.density.finegd
if world.rank == 0:
big_nt_g = finegd.collect(self.tdcalc.density.nt_g)
np.save('%s_%d_nt.npy' % (self.tdname, t), big_nt_g)
del big_nt_g
big_psit_nG = gd.collect(self.tdcalc.wfs.kpt_u[0].psit_nG)
np.save('%s_%d_psit.npy' % (self.tdname, t), big_psit_nG)
del big_psit_nG
else:
finegd.collect(self.tdcalc.density.nt_g)
gd.collect(self.tdcalc.wfs.kpt_u[0].psit_nG)
world.barrier()
def _test_timestepping(self, t):
#XXX DEBUG START
if debug and os.path.isfile('%s_%d.gpw' % (self.tdname, t)):
return
#XXX DEBUG END
timestep = self.timesteps[t]
self.assertAlmostEqual(self.duration % timestep, 0.0, 12)
niter = int(self.duration / timestep)
ndiv = 1 #XXX
traj = PickleTrajectory('%s_%d.traj' % (self.tdname, t), 'w',
self.tdcalc.get_atoms())
t0 = time.time()
f = paropen('%s_%d.log' % (self.tdname, t), 'w')
print('propagator: %s, duration: %6.1f as, timestep: %5.2f as, ' \
'niter: %d' % (self.propagator, self.duration, timestep, niter), file=f)
for i in range(1, niter + 1):
# XXX bare bones propagation without all the nonsense
self.tdcalc.propagator.propagate(self.tdcalc.time,
timestep * attosec_to_autime)
self.tdcalc.time += timestep * attosec_to_autime
self.tdcalc.niter += 1
if i % ndiv == 0:
rate = 60 * ndiv / (time.time() - t0)
ekin = self.tdcalc.atoms.get_kinetic_energy()
epot = self.tdcalc.get_td_energy() * Hartree
F_av = np.zeros((len(self.tdcalc.atoms), 3))
print('i=%06d, time=%6.1f as, rate=%6.2f min^-1, ' \
'ekin=%13.9f eV, epot=%13.9f eV, etot=%13.9f eV' \
% (i, timestep * i, rate, ekin, epot, ekin + epot), file=f)
t0 = time.time()
# Hack to prevent calls to GPAW::get_potential_energy when saving
spa = self.tdcalc.get_atoms()
spc = SinglePointCalculator(epot, F_av, None, None, spa)
spa.set_calculator(spc)
traj.write(spa)
f.close()
traj.close()
self.tdcalc.write('%s_%d.gpw' % (self.tdname, t), mode='all')
# Save density and wavefunctions to binary
gd, finegd = self.tdcalc.wfs.gd, self.tdcalc.density.finegd
if world.rank == 0:
big_nt_g = finegd.collect(self.tdcalc.density.nt_g)
np.save('%s_%d_nt.npy' % (self.tdname, t), big_nt_g)
del big_nt_g
big_psit_nG = gd.collect(self.tdcalc.wfs.kpt_u[0].psit_nG)
np.save('%s_%d_psit.npy' % (self.tdname, t), big_psit_nG)
del big_psit_nG
else:
finegd.collect(self.tdcalc.density.nt_g)
gd.collect(self.tdcalc.wfs.kpt_u[0].psit_nG)
world.barrier()
def mainBasinLoop(symbol1, symbol2, elementN1, elementN2, numberOfType1, numberOfType2, radius, percentType1):
"""symbol1 and symbol2 should be STRINGS.
elementN1 and elementN2 should be ATOMIC NUMBERS of those elements.
numberOfType1 and numberOfType2 are the actual # of those atoms in the molecule
radius can be a decimal
percent should be expressed as a number between 0 and 1.
After that, it will create five new sets of 400 molecules,
each set being the result of that first set having one of our small perturbations
performed on it, which are also then minimized, perturbed, etc., until 50,000
total original orientations have been minimized."""
bigKickResults, round1PE = [], []
global finalList
creationString = symbol1 + str(numberOfType1) + symbol2 + str(numberOfType2)
creationString2 = creationString + '.traj'
baseAtom = nearlySphericalAtom(str(creationString),radius,numberOfType1+numberOfType2)
baseAtom.set_pbc((1,1,1))
baseAtom.set_cell((100,100,100))
calc = QSC()
baseAtom.set_calculator(calc)
baseAtom = makeBimetallic(baseAtom,numberOfType1+numberOfType2,elementN1,elementN2,percentType1)
baseAtom = preventExplosions(baseAtom)
baseAtom = preventExplosions(baseAtom)
for x in range(listLength):
bigKickResults.append(baseAtom.copy())
for x in range(listLength/2):
bigKickResults[x] = shake(bigKickResults[x])
for x in range(listLength/2, listLength):
bigKickResults[x] = switchAtoms(bigKickResults[x])
for x in range(len(bigKickResults)):
bigKickResults[x] = optimizeMolecule(bigKickResults[x],FIREMoves,creationString2)
round1PE.append(bigKickResults[x].get_potential_energy())
minimumPE = min(round1PE)
minimumIndex = round1PE.index(minimumPE)
bestAtom = bigKickResults[minimumIndex]
minimaList = PickleTrajectory(str(creationString2),mode='a')
minimaList.write(bestAtom)
minimaList.close()
finalList.append(bestAtom.copy())
smallKicks(bigKickResults,0,creationString2)
veryBestAtom = returnFinalBestAtom(str(creationString2), str(creationString))
return veryBestAtom
def write_optimized(self, atoms, energy):
magmoms = None # XXX we could do better ...
forces = np.zeros((len(atoms), 3))
calc = SinglePointCalculator(energy, forces, np.zeros((3, 3)),
magmoms, atoms)
atoms.set_calculator(calc)
filename = '%s%s-optimized.traj' % (self.name, self.tag)
traj = PickleTrajectory(filename, 'w', backup=False)
traj.write(atoms)
traj.close()
def write_mode(self, n, kT=units.kB * 300, nimages=30):
"""Write mode to trajectory file."""
mode = self.get_mode(n) * sqrt(kT / self.hnu[n])
p = self.atoms.positions.copy()
n %= 3 * len(self.indices)
traj = PickleTrajectory('%s.%d.traj' % (self.name, n), 'w')
calc = self.atoms.get_calculator()
self.atoms.set_calculator()
for x in np.linspace(0, 2 * pi, nimages, endpoint=False):
self.atoms.set_positions(p + sin(x) * mode)
traj.write(self.atoms)
self.atoms.set_positions(p)
self.atoms.set_calculator(calc)
traj.close()
def write_mode(self, n, kT=units.kB * 300, nimages=30):
"""Write mode to trajectory file."""
mode = self.get_mode(n) * sqrt(kT / self.hnu[n])
p = self.atoms.positions.copy()
n %= 3 * len(self.indices)
traj = PickleTrajectory('%s.%d.traj' % (self.name, n), 'w')
calc = self.atoms.get_calculator()
self.atoms.set_calculator()
for x in np.linspace(0, 2 * pi, nimages, endpoint=False):
self.atoms.set_positions(p + sin(x) * mode)
traj.write(self.atoms)
self.atoms.set_positions(p)
self.atoms.set_calculator(calc)
traj.close()
def MD(self):
"""Molecular Dynamic"""
from ase.md.velocitydistribution import MaxwellBoltzmannDistribution
from ase import units
from ase.md import MDLogger
from ase.io.trajectory import PickleTrajectory
from ase.md.langevin import Langevin
from ase.md.verlet import VelocityVerlet
dyndrivers = {
'Langevin': Langevin,
'None': VelocityVerlet,
}
useAsap = False
mol = self.mol
temperature = self.definedParams['temperature']
init_temperature = self.definedParams['init_temperature']
time_step = self.definedParams['time_step']
nstep = self.definedParams['nstep']
nprint = self.definedParams['nprint']
thermostat = self.definedParams['thermostat']
prop_file = os.path.join(self.definedParams['workdir'],
self.definedParams['output_prefix'] + '.out')
traj_file = os.path.join(self.definedParams['workdir'],
self.definedParams['output_prefix'] + '.traj')
MaxwellBoltzmannDistribution(mol, init_temperature * units.kB)
if thermostat == 'None':
dyn = VelocityVerlet(mol, time_step * units.fs)
elif thermostat == 'Langevin':
dyn = Langevin(mol, time_step * units.fs, temperature * units.kB,
0.01)
else:
raise ImplementationError(
method, 'Thermostat is not implemented in the MD function')
#Function to print the potential, kinetic and total energy
traj = PickleTrajectory(traj_file, "a", mol)
dyn.attach(MDLogger(dyn, mol, prop_file), interval=nprint)
dyn.attach(traj.write, interval=nprint)
dyn.run(nstep)
traj.close()
def fit_bond_length(self, name, atoms, data):
N, x = self.fit
assert N % 2 == 1
d0 = atoms.get_distance(0, 1)
distances = np.linspace(d0 * (1 - x), d0 * (1 + x), N)
energies = []
traj = PickleTrajectory(self.get_filename(name, 'fit.traj'), 'w')
for d in distances:
atoms.set_distance(0, 1, d)
energies.append(atoms.get_potential_energy())
self.check_occupation_numbers(atoms)
traj.write(atoms)
traj.close()
data['distances'] = distances
data['energies'] = energies
def fit_bond_length(self, name, atoms, data):
N, x = self.fit
assert N % 2 == 1
d0 = atoms.get_distance(0, 1)
distances = np.linspace(d0 * (1 - x), d0 * (1 + x), N)
energies = []
traj = PickleTrajectory(self.get_filename(name, 'fit.traj'), 'w')
for d in distances:
atoms.set_distance(0, 1, d)
energies.append(atoms.get_potential_energy())
self.check_occupation_numbers(atoms)
traj.write(atoms)
traj.close()
data['distances'] = distances
data['energies'] = energies
def append_equilibrium_trajectory(self,
weight,
calc,
traj,
comment=None,
label=None,
color=None):
"""
Calculates the V'rep(r) from a given equilibrium trajectory.
The trajectory is set of three (or more, albeit not necessary) frames
where atoms move near their equilibrium structure. To first approximation,
the energies of these frames ARE THE SAME. This method is then
equivalent to append_energy_curve method for given trajectory, with a flat
energy curve.
* Atoms should move as parallel to the fitted bonds as possible.
* Amplitude should be small enough (say, 0.01 Angstroms)
parameters:
===========
weight: fitting weight
calc: Hotbit calculator (remember charge and k-points)
traj: filename for ASE trajectory (energies need not be defined)
comment: fitting comment for par-file (replaced by comment if None)
label: plotting label (replaced by comment if None)
color: plotting color
"""
traj1 = PickleTrajectory(traj)
atoms2 = traj1[0].copy()
calc2 = NullCalculator()
atoms2.set_calculator(calc2)
tmpfile = '_tmp.traj'
traj2 = PickleTrajectory(tmpfile, 'w', atoms2)
for atoms1 in traj1:
atoms2.set_positions(atoms1.get_positions())
atoms2.set_cell(atoms1.get_cell())
atoms2.get_potential_energy()
traj2.write()
traj2.close()
self.append_energy_curve(weight, calc, tmpfile, comment, label, color)
os.remove(tmpfile)
if os.path.isfile(tmpfile + '.bak'):
os.remove(tmpfile + '.bak')
def MD(self):
"""Molecular Dynamic"""
from ase.md.velocitydistribution import MaxwellBoltzmannDistribution
from ase import units
from ase.md import MDLogger
from ase.io.trajectory import PickleTrajectory
from ase.md.langevin import Langevin
from ase.md.verlet import VelocityVerlet
dyndrivers = {
'Langevin': Langevin,
'None': VelocityVerlet,
}
useAsap = False
mol = self.mol
temperature = self.definedParams['temperature']
init_temperature = self.definedParams['init_temperature']
time_step = self.definedParams['time_step']
nstep = self.definedParams['nstep']
nprint = self.definedParams['nprint']
thermostat = self.definedParams['thermostat']
prop_file = os.path.join(self.definedParams['workdir'],
self.definedParams['output_prefix']+'.out')
traj_file = os.path.join(self.definedParams['workdir'],
self.definedParams['output_prefix']+'.traj')
MaxwellBoltzmannDistribution(mol,init_temperature*units.kB)
if thermostat == 'None':
dyn = VelocityVerlet(mol, time_step*units.fs)
elif thermostat == 'Langevin':
dyn = Langevin(mol, time_step*units.fs, temperature*units.kB, 0.01 )
else:
raise ImplementationError(method,'Thermostat is not implemented in the MD function')
#Function to print the potential, kinetic and total energy
traj = PickleTrajectory(traj_file,"a",mol)
dyn.attach(MDLogger(dyn,mol,prop_file),interval=nprint)
dyn.attach(traj.write, interval = nprint)
dyn.run(nstep)
traj.close()
def fit_volume(self, name, atoms):
N, x = self.fit
cell0 = atoms.get_cell()
strains = np.linspace(1 - x, 1 + x, N)
energies = []
traj = PickleTrajectory(self.get_filename(name, 'fit.traj'), 'w')
for s in strains:
atoms.set_cell(cell0 * s, scale_atoms=True)
energies.append(atoms.get_potential_energy())
traj.write(atoms)
traj.close()
assert N % 2 == 1
data = {'energy': energies[N // 2],
'strains': strains,
'energies': energies}
return data
def write_mode(self, n=None, kT=units.kB * 300, nimages=30):
"""Write mode number n to trajectory file. If n is not specified,
writes all non-zero modes."""
if n == None:
for index, energy in enumerate(self.get_energies()):
if abs(energy) > 1e-5:
self.write_mode(n=index, kT=kT, nimages=nimages)
return
mode = self.get_mode(n) * sqrt(kT / abs(self.hnu[n]))
p = self.atoms.positions.copy()
n %= 3 * len(self.indices)
traj = PickleTrajectory('%s.%d.traj' % (self.name, n), 'w')
calc = self.atoms.get_calculator()
self.atoms.set_calculator()
for x in np.linspace(0, 2 * pi, nimages, endpoint=False):
self.atoms.set_positions(p + sin(x) * mode)
traj.write(self.atoms)
self.atoms.set_positions(p)
self.atoms.set_calculator(calc)
traj.close()
def write_mode(self, n=None, kT=units.kB * 300, nimages=30):
"""Write mode number n to trajectory file. If n is not specified,
writes all non-zero modes."""
if n == None:
for index, energy in enumerate(self.get_energies()):
if abs(energy) > 1e-5:
self.write_mode(n=index, kT=kT, nimages=nimages)
return
mode = self.get_mode(n) * sqrt(kT / abs(self.hnu[n]))
p = self.atoms.positions.copy()
n %= 3 * len(self.indices)
traj = PickleTrajectory('%s.%d.traj' % (self.name, n), 'w')
calc = self.atoms.get_calculator()
self.atoms.set_calculator()
for x in np.linspace(0, 2 * pi, nimages, endpoint=False):
self.atoms.set_positions(p + sin(x) * mode)
traj.write(self.atoms)
self.atoms.set_positions(p)
self.atoms.set_calculator(calc)
traj.close()
def append_equilibrium_trajectory(self,weight,calc,traj,comment=None,label=None,color=None):
"""
Calculates the V'rep(r) from a given equilibrium trajectory.
The trajectory is set of three (or more, albeit not necessary) frames
where atoms move near their equilibrium structure. To first approximation,
the energies of these frames ARE THE SAME. This method is then
equivalent to append_energy_curve method for given trajectory, with a flat
energy curve.
* Atoms should move as parallel to the fitted bonds as possible.
* Amplitude should be small enough (say, 0.01 Angstroms)
parameters:
===========
weight: fitting weight
calc: Hotbit calculator (remember charge and k-points)
traj: filename for ASE trajectory (energies need not be defined)
comment: fitting comment for par-file (replaced by comment if None)
label: plotting label (replaced by comment if None)
color: plotting color
"""
traj1 = PickleTrajectory(traj)
atoms2 = traj1[0].copy()
calc2 = NullCalculator()
atoms2.set_calculator(calc2)
tmpfile = '_tmp.traj'
traj2 = PickleTrajectory(tmpfile,'w',atoms2)
for atoms1 in traj1:
atoms2.set_positions(atoms1.get_positions())
atoms2.set_cell( atoms1.get_cell() )
atoms2.get_potential_energy()
traj2.write()
traj2.close()
self.append_energy_curve(weight,calc,tmpfile,comment,label,color)
os.remove(tmpfile)
if os.path.isfile(tmpfile+'.bak'):
os.remove(tmpfile+'.bak')
def accept_move(self):
e = self.atoms.get_potential_energy()
self.lists['energy'].append(e)
self.lists['counts'].append(1)
self.lists['coordinations'].append(
self.sumcoordinations(self.surfmove.coordinations))
self.lists['types'].append(self.sumcoordinations(
self.surfmove.types)) #See sumcoordinations
self.lists['vac_coordinations'].append(
self.sumcoordinations(self.surfmove.vacant_coordinations))
self.lists['vac_types'].append(
self.sumcoordinations(self.surfmove.vacant_types))
if self.mc_step >= self.total_steps / 2:
if self.id_relax == None:
self.id_relax = self.n_accept
unrelax_energy, relax_energy = self.relax()
self.lists['unrelaxed_energies'].append(unrelax_energy)
self.lists['relaxed_energies'].append(relax_energy)
e += relax_energy - unrelax_energy
if e < self.ground_state_energy:
#now store .amc in tmpfn[1] and 0000* in tmpfn[0]
tmpfn = self.filename.split(".")
traj = PickleTrajectory(tmpfn[0] + ".traj",
'w',
self.atoms,
master=True,
backup=False)
traj.write()
traj.close()
#write(filename=tmpfn[0]+".traj",images=self.atoms)
self.ground_state_energy = e
else:
self.lists['unrelaxed_energies'].append(np.nan)
self.lists['relaxed_energies'].append(np.nan)
self.mc_step += 1
self.n_accept += 1
def breed(particles, objects, gen):
sortedIndices = numpy.argsort(numpy.asarray(objects))
particlestobreed = []
explambda = numpy.log(10) / (NPsPerGen - 1)
for i in range(len(particles)):
trialvar = numpy.random.random()
if (trialvar <= numpy.exp(-1 * explambda * i)):
particlestobreed.append(particles[sortedIndices[i]])
NewGen = []
for i in range(NPsPerGen):
first = numpy.random.randint(len(particlestobreed))
second = numpy.random.randint(len(particlestobreed))
gene1 = numpy.random.randint(2)
gene2 = numpy.random.randint(2)
if (gene1 == 0):
type1 = particlestobreed[first].gene[0]
else:
type1 = particlestobreed[second].gene[0]
if (gene2 == 0):
type2 = particlestobreed[first].gene[1]
else:
type2 = particlestobreed[second].gene[1]
if (particlestobreed[first].gene[2] <
particlestobreed[second].gene[2]):
minval = particlestobreed[first].gene[2]
maxval = particlestobreed[second].gene[2]
else:
minval = particlestobreed[second].gene[2]
maxval = particlestobreed[first].gene[2]
numbertype1 = numpy.random.randint(minval, maxval + 1)
NewGen.append(createparticle(type1, type2, numbertype1))
traj = PickleTrajectory(
'generation' + str(gen) + '_' + str(i) + '.traj', 'w')
NewGen[i].gene = (type1, type2, numbertype1)
traj.write(ParticleList[i])
traj.close()
return NewGen
def fit_bond_length(self, name, atoms, data=None):
N, x = self.fit
d0 = atoms.get_distance(0, 1)
distances = np.linspace(d0 * (1 - x), d0 * (1 + x), N)
energies = []
traj = PickleTrajectory(self.get_filename(name, 'fit.traj'), 'w')
for d in distances:
atoms.set_distance(0, 1, d)
energies.append(atoms.get_potential_energy())
self.check_occupation_numbers(atoms)
traj.write(atoms)
traj.close()
if data is not None:
data['distances'] = distances
data['energies'] = energies
else:
assert N % 2 == 1
data = {'energy': energies[N // 2],
'distances': distances,
'energies': energies}
return data
def optimizeMolecule(molecule,NMoves,creationString):
bestEnergy = 0.
totalMinimaFound = 0
sinceLastFind = 0
calc = QSC()
molecule.set_calculator(calc)
for i in range(NMoves):
molecule = newMove(molecule)
molecule = preventExplosions(molecule)
molecule.center()
sinceLastFind += 1
# do a last optimization of the structure
dyn = FIRE(molecule)
dyn.run(steps = 2000)
newEnergy = molecule.get_potential_energy()
if (newEnergy < bestEnergy):
optimizedMolecule = molecule
bestEnergy = newEnergy
line = str(totalMinimaFound) + " " + str(molecule.get_potential_energy()) + " " + str(i) +"\n"
print line
f = open('EnergyList.txt','a')
f.write(line)
f.close()
minimaList = PickleTrajectory(str(creationString),mode='a')
minimaList.write(optimizedMolecule)
totalMinimaFound += 1
minimaList.close()
sinceLastFind = 0
elif (sinceLastFind < 200):
break
minimaList.close()
minimaList = PickleTrajectory(str(creationString),mode='r')
atomslist = [atom for atom in minimaList]
ase.io.write('movie.xyz',atomslist,format='xyz') # write a movie file of our dynamics
minimaList.close()
return optimizedMolecule
through the other. Such atoms have coordinates outside the box.
This facilitates analysis of e.g. diffusion, but can be problematic
when plotting. This script reads through a trajectory file, and
write a new one where all atoms are mapped into the computational box.
If there are axes with free boundary conditions, the corresponding
coordinate is left unchanged.
SIDE EFFECT: All energies, forces and stresses are removed (yes, this
can be considered as a bug!)
"""
from __future__ import print_function
import sys
from ase.io.trajectory import PickleTrajectory
if len(sys.argv) != 3:
print(__doc__)
sys.exit(1)
infile = PickleTrajectory(sys.argv[1])
outfile = None
for atoms in infile:
atoms.set_scaled_positions(atoms.get_scaled_positions())
atoms.set_calculator(None) # or the singlepointcalculator fails!
if outfile is None:
outfile = PickleTrajectory(sys.argv[2], 'w')
outfile.write(atoms)
outfile.close()
train1_pos = np.column_stack((t1_x, t1_y, t1_z))
train2_pos = np.column_stack((t2_x, t2_y, t2_z))
train1_vel = np.column_stack((v1_x, v1_y, v1_z))
train2_vel = np.column_stack((v2_x, v2_y, v2_z))
train_pos = np.copy(train1_pos)
train_vel = np.copy(train1_vel)
# second train commented out to avoid collisions that screw GPAW
# calculation
#train_pos = np.append(train_pos, train2_pos, axis=0)
#train_vel = np.append(train_vel, train2_vel, axis=0)
methane_trains = Atoms()
for i in range(len(train_pos)):
m = molecule("CH4")
m.set_positions(m.get_positions() + train_pos[i])
m.set_velocities(np.zeros((5, 3)) + train_vel[i])
methane_trains.extend(m)
# trains are now ready, append to atoms
#print(methane_trains.get_positions())
atoms.extend(methane_trains)
# now write to .traj file
pt = PickleTrajectory(filename="ch4_train.traj", mode="w", backup=False)
pt.write(atoms)
pt.close()
def run(self, steps=1000, temp=1000, txt=None, amc=None, trajectory=None):
#Open the output textfile
if txt is not None:
if not isinstance(txt, str):
raise Warning('You must specify a valid filename.')
f = open(txt, 'w')
else:
f = None
#Initialize the dataobject
if amc is not None:
amcdata = AtomMonteCarloData(amc, self.cluster.atomic_number,
self.cluster.symmetry,
self.cluster.lattice_constant,
self.size, temp)
amcdata.set_dimensions(self.type_count + 1,
self.neighbor_count + 1)
else:
amcdata = None
#Open the trajectoryfile
if trajectory is not None:
traj = PickleTrajectory(trajectory, 'w', self.cluster, None, False)
#traj.write_energy = False
traj.write_forces = False
traj.write_stress = False
traj.write_momenta = False
traj.write()
#Print header to outputfile
if isinstance(f, file):
print >> f, (
'Monte Carlo Sumulation on Cluster Surface Atoms \n' +
'Date: %s\n' % datetime.now().strftime("%A, %d %b %Y %H:%M:%S")
+ 'Material: %s\n' %
data.chemical_symbols[self.cluster.atomic_number] +
'Lattice constant: %s\n' % self.cluster.lattice_constant +
'Cluster size: %i\n' % self.size +
'Multiplicity: %i\n' % self.cluster.multiplicity +
'Temperature: %i K\n' % temp + 'Thermal energy: %.5f eV\n' %
(data.kB * temp) +
'Iteration Atom Position ' +
'Desination Energy Accept Probability')
#Set initial values for the simulation
old_energy = self.cluster.get_potential_energy()
acceptances = 0
if amcdata is not None:
amcdata.append((
old_energy,
0.0, #Releaxed energy not found yet
self.cluster.get_diameter(),
self.cluster.multiplicity,
1,
np.bincount(self.get_types()),
np.bincount(self.get_coordinations()),
np.bincount(self.get_vacant_types()),
np.bincount(self.get_vacant_coordinations()),
))
#Print initial energy to outputfile
if isinstance(f, file):
print >> f, '%6i %70.4f' % (0, old_energy)
# Check for ASAP calculator, and enable optimization
#use_asap = 'asap' in str(self.cluster.get_calculator())
if self.use_asap:
auxcluster = self.cluster.copy()
auxcluster.set_calculator(self.asap_calc)
auxcluster.get_potential_energy()
#Monte Carlo simulering
for i in range(1, steps + 1):
#Chose a move
surface_i, vacant_i = self.chose_move_old()
if self.use_asap:
new_atom = auxcluster[surface_i]
new_atom.set_position(self.get_vacant_positions()[vacant_i])
new_energy = auxcluster.get_potential_energy()
# Move atom back. It is moved forward again if accepted.
#new_atom.set_position(self.cluster[surface_i].position)
else:
#Make new cluster with change and calculate energy
new_cluster = self.cluster.copy()
new_cluster.calc = self.cluster.calc
new_atom = ClusterAtom(new_cluster.atomic_number)
new_atom.position = self.get_vacant_positions()[vacant_i]
new_atom.neighbors = self.get_vacant_neighbors()[vacant_i]
new_atom.type = self.get_vacant_types()[vacant_i]
new_atom.coordination = self.get_vacant_coordinations(
)[vacant_i]
new_cluster.move_atom(surface_i, new_atom)
new_energy = new_cluster.get_potential_energy()
#Calculate the energy change and accept or reject the change
energy_change = new_energy - old_energy
accept = False
if energy_change < 0:
p = 1.0
accept = True
else:
p = math.exp(-energy_change / (data.kB * temp))
pc = random.random()
if pc < p: accept = True
#Print info on MC move til outputfile
if isinstance(f, file):
old_position = self.cluster[surface_i].position
new_position = new_atom.position
print >> f, (
'%6i %8i (%6.2f%7.2f%7.2f) (%6.2f%7.2f%7.2f) %11.4f' %
(i, surface_i, old_position[0], old_position[1],
old_position[2], new_position[0], new_position[1],
new_position[2], new_energy)),
if accept:
old_energy = new_energy
acceptances += 1
self.move_atom(surface_i, vacant_i)
if isinstance(f, file):
print >> f, '%7s %10.2f' % ('Yes', p)
if amcdata is not None:
amcdata.append((
old_energy,
0.0, #Releaxed energy not found yet
self.cluster.get_diameter(),
self.cluster.multiplicity,
1,
np.bincount(self.get_types()),
np.bincount(self.get_coordinations()),
np.bincount(self.get_vacant_types()),
np.bincount(self.get_vacant_coordinations()),
))
if trajectory is not None:
traj.write(self.cluster)
else:
if self.use_asap:
# Move atom back
new_atom.set_position(self.cluster[surface_i].position)
if isinstance(f, file):
print >> f, '%7s %10.2f' % ('No', p)
if amcdata is not None:
amcdata.add_count(-1, 1)
#Print footer to the outputfile
if isinstance(f, file):
print >> f, '\nDate: %s' % datetime.now().strftime(
"%A, %d %b %Y %H:%M:%S")
print >> f, 'Acceptances ratio: %.2f' % (acceptances * 1.0 / steps)
#Save the data object
if amcdata is not None:
amcdata.write()
#Close the trajectory
if trajectory is not None:
traj.close()
def write_modes(self, q_c, branches=0, kT=units.kB*300, repeat=(1, 1, 1),
nimages=30, acoustic=True):
"""Write mode to trajectory file.
The classical equipartioning theorem states that each normal mode has
an average energy::
<E> = 1/2 * k_B * T = 1/2 * omega^2 * Q^2
=>
Q = sqrt(k_B*T) / omega
at temperature T. Here, Q denotes the normal coordinate of the mode.
Parameters
----------
q_c: ndarray
q-vector of the modes.
branches: int or list
Branch index of calculated modes.
kT: float
Temperature in units of eV. Determines the amplitude of the atomic
displacements in the modes.
repeat: tuple
Repeat atoms (l, m, n) times in the directions of the lattice
vectors. Displacements of atoms in repeated cells carry a Bloch
phase factor given by the q-vector and the cell lattice vector R_m.
nimages: int
Number of images in an oscillation.
"""
if isinstance(branches, int):
branch_n = [branches]
else:
branch_n = list(branches)
# Calculate modes
omega_n, u_n = self.band_structure([q_c], modes=True, acoustic=acoustic)
# Repeat atoms
atoms = self.atoms * repeat
pos_mav = atoms.positions.copy()
# Total number of unit cells
M = np.prod(repeat)
# Corresponding lattice vectors R_m
R_cm = np.indices(repeat[::-1]).reshape(3, -1)[::-1]
# Bloch phase
phase_m = np.exp(2.j * pi * np.dot(q_c, R_cm))
phase_ma = phase_m.repeat(len(self.atoms))
for n in branch_n:
omega = omega_n[0, n]
u_av = u_n[0, n] # .reshape((-1, 3))
# Mean displacement at high T ?
u_av *= sqrt(kT / abs(omega))
mode_av = np.zeros((len(self.atoms), 3), dtype=self.dtype)
indices = self.dyn.get_indices()
mode_av[indices] = u_av
mode_mav = (np.vstack([mode_av]*M) * phase_ma[:, np.newaxis]).real
traj = PickleTrajectory('%s.mode.%d.traj' % (self.name, n), 'w')
for x in np.linspace(0, 2*pi, nimages, endpoint=False):
# XXX Is it correct to take out the sine component here ?
atoms.set_positions(pos_mav + sin(x) * mode_mav)
traj.write(atoms)
traj.close()
train1_pos = np.column_stack((t1_x, t1_y, t1_z))
train2_pos = np.column_stack((t2_x, t2_y, t2_z))
train1_vel = np.column_stack((v1_x, v1_y, v1_z))
train2_vel = np.column_stack((v2_x, v2_y, v2_z))
train_pos = np.copy(train1_pos)
train_vel = np.copy(train1_vel)
# second train commented out to avoid collisions that screw GPAW
# calculation
#train_pos = np.append(train_pos, train2_pos, axis=0)
#train_vel = np.append(train_vel, train2_vel, axis=0)
methane_trains = Atoms()
for i in range(len(train_pos)):
m = molecule("CH4")
m.set_positions(m.get_positions() + train_pos[i])
m.set_velocities(np.zeros((5, 3)) + train_vel[i])
methane_trains.extend(m)
# trains are now ready, append to atoms
#print(methane_trains.get_positions())
atoms.extend(methane_trains)
# now write to .traj file
pt = PickleTrajectory(filename="ch4_train.traj", mode="w", backup=False)
pt.write(atoms)
pt.close()
def write_modes(self,
q_c,
branches=0,
kT=units.kB * 300,
repeat=(1, 1, 1),
nimages=30,
acoustic=True):
"""Write mode to trajectory file.
The classical equipartioning theorem states that each normal mode has
an average energy::
<E> = 1/2 * k_B * T = 1/2 * omega^2 * Q^2
=>
Q = sqrt(k_B*T) / omega
at temperature T. Here, Q denotes the normal coordinate of the mode.
Parameters
----------
q_c: ndarray
q-vector of the modes.
branches: int or list
Branch index of calculated modes.
kT: float
Temperature in units of eV. Determines the amplitude of the atomic
displacements in the modes.
repeat: tuple
Repeat atoms (l, m, n) times in the directions of the lattice
vectors. Displacements of atoms in repeated cells carry a Bloch
phase factor given by the q-vector and the cell lattice vector R_m.
nimages: int
Number of images in an oscillation.
"""
if isinstance(branches, int):
branch_n = [branches]
else:
branch_n = list(branches)
# Calculate modes
omega_n, u_n = self.band_structure([q_c],
modes=True,
acoustic=acoustic)
# Repeat atoms
atoms = self.atoms * repeat
pos_mav = atoms.positions.copy()
# Total number of unit cells
M = np.prod(repeat)
# Corresponding lattice vectors R_m
R_cm = np.indices(repeat[::-1]).reshape(3, -1)[::-1]
# Bloch phase
phase_m = np.exp(2.j * pi * np.dot(q_c, R_cm))
phase_ma = phase_m.repeat(len(self.atoms))
for n in branch_n:
omega = omega_n[0, n]
u_av = u_n[0, n] # .reshape((-1, 3))
# Mean displacement at high T ?
u_av *= sqrt(kT / abs(omega))
mode_av = np.zeros((len(self.atoms), 3), dtype=self.dtype)
indices = self.dyn.get_indices()
mode_av[indices] = u_av
mode_mav = (np.vstack([mode_av] * M) *
phase_ma[:, np.newaxis]).real
traj = PickleTrajectory('%s.mode.%d.traj' % (self.name, n), 'w')
for x in np.linspace(0, 2 * pi, nimages, endpoint=False):
# XXX Is it correct to take out the sine component here ?
atoms.set_positions(pos_mav + sin(x) * mode_mav)
traj.write(atoms)
traj.close()
randomNewRadius = random.gauss( (ninetyNinthRadius+ninetyFifthRadius)/2 , (ninetyNinthRadius - ninetyFifthRadius)/2 )
xFromCenter = random.uniform(0,randomNewRadius)
randomNewRadius = ((randomNewRadius**2) - (xFromCenter**2))**0.5
yFromCenter = random.uniform(0,randomNewRadius)
zFromCenter = ((randomNewRadius**2) - (yFromCenter**2))**0.5
newXPosition = inCOM[0] + random.choice([-1,1])*xFromCenter
newYPosition = inCOM[1] + random.choice([-1,1])*yFromCenter
newZPosition = inCOM[2] + random.choice([-1,1])*zFromCenter
positionArray = inAtom.get_positions()
positionArray[atomIndex] = (newXPosition,newYPosition,newZPosition)
inAtom.set_positions(positionArray)
return inAtom
except IndexError:
print "The index of the atom you wanted to move is too high or too low."
print "Please check your function call of shell_move(a,b)"
print "-Jeff"
minimaList.close()
minimaList = PickleTrajectory('Pt75Au25.traj',mode='r')
atomslist = [atom for atom in minimaList]
ase.io.write('movie.xyz',atomslist,format='xyz') # write a movie file of our dynamics
minimaList.close()
through the other. Such atoms have coordinates outside the box.
This facilitates analysis of e.g. diffusion, but can be problematic
when plotting. This script reads through a trajectory file, and
write a new one where all atoms are mapped into the computational box.
If there are axes with free boundary conditions, the corresponding
coordinate is left unchanged.
SIDE EFFECT: All energies, forces and stresses are removed (yes, this
can be considered as a bug!)
"""
from __future__ import print_function
import sys
from ase.io.trajectory import PickleTrajectory
if len(sys.argv) != 3:
print(__doc__)
sys.exit(1)
infile = PickleTrajectory(sys.argv[1])
outfile = None
for atoms in infile:
atoms.set_scaled_positions(atoms.get_scaled_positions())
atoms.set_calculator(None) # or the singlepointcalculator fails!
if outfile is None:
outfile = PickleTrajectory(sys.argv[2], 'w')
outfile.write(atoms)
outfile.close()
tdcalc = TDDFT(name + '_esx.gpw',
txt=name + '_td.txt',
propagator='EFSICN')
ehrenfest = EhrenfestVelocityVerlet(tdcalc)
traj = PickleTrajectory(name + '_td.traj', 'w', tdcalc.get_atoms())
t0 = time.time()
f = paropen(name + '_td.log', 'w')
for i in range(1, niter + 1):
ehrenfest.propagate(timestep)
if i % ndiv == 0:
rate = 60 * ndiv / (time.time() - t0)
ekin = tdcalc.atoms.get_kinetic_energy()
epot = tdcalc.get_td_energy() * Hartree
F_av = ehrenfest.F * Hartree / Bohr
print(
'i=%06d (%6.2f min^-1), ekin=%13.9f, epot=%13.9f, etot=%13.9f'
% (i, rate, ekin, epot, ekin + epot),
file=f)
t0 = time.time()
# Hack to prevent calls to GPAW::get_potential_energy when saving
spa = tdcalc.get_atoms()
spc = SinglePointCalculator(epot, F_av, None, None, spa)
spa.set_calculator(spc)
traj.write(spa)
f.close()
traj.close()
def smallKicks(moleculeList, inTreeLevel, creationString):
inTreeLevel += 1
global finalList
if inTreeLevel == treeDepth:
return None
else:
list1, list2, list3, list4, list5 = [],[],[],[],[]
p1, p2, p3, p4, p5 = [],[],[],[],[]
nAtoms = moleculeList[0].get_number_of_atoms() #they should all have the same number
for x in range(len(moleculeList)):
list1.append(moleculeList[x].copy())
list2.append(moleculeList[x].copy())
list3.append(moleculeList[x].copy())
list4.append(moleculeList[x].copy())
list5.append(moleculeList[x].copy())
for x in range(len(list1)): #but they should all be the same length
list1[x] = shell_move(list1[x],random.randint(0,nAtoms))
list2[x] = HighEnergyMove(list2[x])
list3[x] = ball_move(list3[x],random.randint(0,nAtoms))
list4[x] = smallSwitchAtoms(list4[x])
list5[x] = moveAtoms(2,list5[x])
for x in range(len(list1)): #again, should all be the same length
list1[x] = optimizeMolecule(list1[x],FIREMoves,creationString)
list2[x] = optimizeMolecule(list2[x],FIREMoves,creationString)
list3[x] = optimizeMolecule(list3[x],FIREMoves,creationString)
list4[x] = optimizeMolecule(list4[x],FIREMoves,creationString)
list5[x] = optimizeMolecule(list5[x],FIREMoves,creationString)
del moleculeList[:]
del moleculeList
#This is also decidedly unpythonic
for x in range(len(list1)):
p1.append(list1[x].get_potential_energy())
p2.append(list2[x].get_potential_energy())
p3.append(list3[x].get_potential_energy())
p4.append(list4[x].get_potential_energy())
p5.append(list5[x].get_potential_energy())
#p1min = (MIN)imum PE. p1mi = (M)inimum (I)ndex. p1b = (B)est.
p1min, p2min, p3min, p4min, p5min = min(p1),min(p2),min(p3),min(p4),min(p5)
p1mi, p2mi, p3mi, p4mi, p5mi = p1.index(p1min), p2.index(p2min), p3.index(p3min), p4.index(p4min), p5.index(p5min)
p1b, p2b, p3b, p4b, p5b = list1[p1mi],list2[p2mi],list3[p3mi],list4[p4mi],list5[p5mi]
minimaList = PickleTrajectory(str(creationString),mode='a')
minimaList.write(p1b)
minimaList.write(p2b)
minimaList.write(p3b)
minimaList.write(p4b)
minimaList.write(p5b)
minimaList.close()
if p1min < finalList[len(finalList)-1]:
finalList.append(p1b.copy())
if p2min < finalList[len(finalList)-1]:
finalList.append(p2b.copy())
if p3min < finalList[len(finalList)-1]:
finalList.append(p3b.copy())
if p4min < finalList[len(finalList)-1]:
finalList.append(p4b.copy())
if p5min < finalList[len(finalList)-1]:
finalList.append(p5b.copy())
smallKicks(list1,inTreeLevel,creationString)
smallKicks(list2,inTreeLevel,creationString)
smallKicks(list3,inTreeLevel,creationString)
smallKicks(list4,inTreeLevel,creationString)
smallKicks(list5,inTreeLevel,creationString)
dyn.run(1)
# do a last optimization of the structure
dyn = FIRE(atoms)
dyn.run()
newEnergy = atoms.get_potential_energy()
if (newEnergy < bestEnergy):
bestEnergy = newEnergy
line = str(totalMinimaFound) + " " + str(
atoms.get_potential_energy()) + " " + str(i) + "\n"
print line
f = open('EnergyList.txt', 'a')
f.write(line)
f.close()
minimaList = PickleTrajectory(filename, mode='a')
minimaList.write(atoms)
minimaList.close()
totalMinimaFound += 1
timesincelast = 0
else:
timesincelast += 1
if (timesincelast > 25):
minimalist = PickleTrajectory(filename, mode='r')
atomslist = [atom for atom in minimalist]
minimalist.close()
atoms.set_positions(atomslist[len(atomslist) - 1].get_positions())
atoms.get_potential_energy()
timessincelast = 0
minimaList = PickleTrajectory(filename, mode='r')
atomslist = [atom for atom in minimaList]
ase.io.write('movie.xyz', atomslist,
def write_modes(self, q_c, branches=0, kT=units.kB*300, born=False,
repeat=(1, 1, 1), nimages=30, center=False):
"""Write modes to trajectory file.
Parameters
----------
q_c: ndarray
q-vector of modes.
branches: int or list
Branch index of modes.
kT: float
Temperature in units of eV. Determines the amplitude of the atomic
displacements in the modes.
born: bool
Include non-analytic contribution to the force constants at q -> 0.
repeat: tuple
Repeat atoms (l, m, n) times in the directions of the lattice
vectors. Displacements of atoms in repeated cells carry a Bloch
phase factor given by the q-vector and the cell lattice vector R_m.
nimages: int
Number of images in an oscillation.
center: bool
Center atoms in unit cell if True (default: False).
"""
if isinstance(branches, int):
branch_n = [branches]
else:
branch_n = list(branches)
# Calculate modes
omega_n, u_n = self.band_structure([q_c], modes=True, born=born)
print omega_n
# Repeat atoms
atoms = self.atoms * repeat
# Center
if center:
atoms.center()
# Here ma refers to a composite unit cell/atom dimension
pos_mav = atoms.get_positions()
# Total number of unit cells
M = np.prod(repeat)
# Corresponding lattice vectors R_m
R_cm = np.indices(repeat).reshape(3, -1)
# Bloch phase
phase_m = np.exp(2.j * pi * np.dot(q_c, R_cm))
phase_ma = phase_m.repeat(len(self.atoms))
for n in branch_n:
omega = omega_n[0, n]
u_av = u_n[0, n]
# Mean displacement of a classical oscillator at temperature T
u_av *= sqrt(kT) / abs(omega)
mode_av = np.zeros((len(self.atoms), 3), dtype=complex)
# Insert slice with atomic displacements for the included atoms
mode_av[self.indices] = u_av
# Repeat and multiply by Bloch phase factor
mode_mav = (np.vstack([mode_av]*M) * phase_ma[:, np.newaxis]).real
traj = PickleTrajectory('%s.mode.%d.traj' % (self.name, n), 'w')
for x in np.linspace(0, 2*pi, nimages, endpoint=False):
atoms.set_positions(pos_mav + sin(x) * mode_mav)
traj.write(atoms)
traj.close()
def write(self, filename):
from ase.io.trajectory import PickleTrajectory
traj = PickleTrajectory(filename, 'w', self)
traj.write()
traj.close()
while not os.path.isfile(name + '_gs.gpw'):
print('Node %d waiting for file...' % world.rank)
time.sleep(10)
world.barrier()
tdcalc = TDDFT(name + '_gs.gpw', txt=name + '_td.txt', propagator='EFSICN')
ehrenfest = EhrenfestVelocityVerlet(tdcalc)
traj = PickleTrajectory(name + '_td.traj', 'w', tdcalc.get_atoms())
t0 = time.time()
f = paropen(name + '_td.log', 'w')
for i in range(1, niter+1):
ehrenfest.propagate(timestep)
if i % ndiv == 0:
rate = 60 * ndiv / (time.time()-t0)
ekin = tdcalc.atoms.get_kinetic_energy()
epot = tdcalc.get_td_energy() * Hartree
F_av = ehrenfest.F * Hartree / Bohr
print('i=%06d (%6.2f min^-1), ekin=%13.9f, epot=%13.9f, etot=%13.9f' % (i, rate, ekin, epot, ekin+epot), file=f)
t0 = time.time()
# Hack to prevent calls to GPAW::get_potential_energy when saving
spa = tdcalc.get_atoms()
spc = SinglePointCalculator(epot, F_av, None, None, spa)
spa.set_calculator(spc)
traj.write(spa)
f.close()
traj.close()
def write(self, filename):
from ase.io.trajectory import PickleTrajectory
traj = PickleTrajectory(filename, 'w', self)
traj.write()
traj.close()
def write_modes(self, q_c, branches=0, kT=units.kB*300, born=False,
repeat=(1, 1, 1), nimages=30, center=False):
"""Write modes to trajectory file.
Parameters
----------
q_c: ndarray
q-vector of the modes.
branches: int or list
Branch index of modes.
kT: float
Temperature in units of eV. Determines the amplitude of the atomic
displacements in the modes.
born: bool
Include non-analytic contribution to the force constants at q -> 0.
repeat: tuple
Repeat atoms (l, m, n) times in the directions of the lattice
vectors. Displacements of atoms in repeated cells carry a Bloch
phase factor given by the q-vector and the cell lattice vector R_m.
nimages: int
Number of images in an oscillation.
center: bool
Center atoms in unit cell if True (default: False).
"""
if isinstance(branches, int):
branch_l = [branches]
else:
branch_l = list(branches)
# Calculate modes
omega_l, u_l = self.band_structure([q_c], modes=True, born=born)
# Repeat atoms
atoms = self.atoms * repeat
# Center
if center:
atoms.center()
# Here ``Na`` refers to a composite unit cell/atom dimension
pos_Nav = atoms.get_positions()
# Total number of unit cells
N = np.prod(repeat)
# Corresponding lattice vectors R_m
R_cN = np.indices(repeat).reshape(3, -1)
# Bloch phase
phase_N = np.exp(2.j * pi * np.dot(q_c, R_cN))
phase_Na = phase_N.repeat(len(self.atoms))
for l in branch_l:
omega = omega_l[0, l]
u_av = u_l[0, l]
# Mean displacement of a classical oscillator at temperature T
u_av *= sqrt(kT) / abs(omega)
mode_av = np.zeros((len(self.atoms), 3), dtype=complex)
# Insert slice with atomic displacements for the included atoms
mode_av[self.indices] = u_av
# Repeat and multiply by Bloch phase factor
mode_Nav = np.vstack(N * [mode_av]) * phase_Na[:, np.newaxis]
traj = PickleTrajectory('%s.mode.%d.traj' % (self.name, l), 'w')
for x in np.linspace(0, 2*pi, nimages, endpoint=False):
atoms.set_positions((pos_Nav + np.exp(1.j * x) * mode_Nav).real)
traj.write(atoms)
traj.close()
