def _molecular_dynamics(self, resume=None):
"""Performs a molecular dynamics simulation, until mdmin is
exceeded. If resuming, the file number (md%05i) is expected."""
self._log('msg', 'Molecular dynamics: md%05i' % self._counter)
mincount = 0
energies, oldpositions = [], []
thermalized = False
if resume:
self._log('msg', 'Resuming MD from md%05i.traj' % resume)
if os.path.getsize('md%05i.traj' % resume) == 0:
self._log(
'msg', 'md%05i.traj is empty. Resuming from '
'qn%05i.traj.' % (resume, resume - 1))
atoms = io.read('qn%05i.traj' % (resume - 1), index=-1)
else:
images = io.PickleTrajectory('md%05i.traj' % resume, 'r')
for atoms in images:
energies.append(atoms.get_potential_energy())
oldpositions.append(atoms.positions.copy())
passedmin = self._passedminimum(energies)
if passedmin:
mincount += 1
self._atoms.set_momenta(atoms.get_momenta())
thermalized = True
self._atoms.positions = atoms.get_positions()
self._log('msg',
'Starting MD with %i existing energies.' % len(energies))
if not thermalized:
MaxwellBoltzmannDistribution(self._atoms,
temp=self._temperature * units.kB,
force_temp=True)
traj = io.PickleTrajectory('md%05i.traj' % self._counter, 'a',
self._atoms)
dyn = VelocityVerlet(self._atoms, dt=self._timestep * units.fs)
log = MDLogger(dyn,
self._atoms,
'md%05i.log' % self._counter,
header=True,
stress=False,
peratom=False)
dyn.attach(log, interval=1)
dyn.attach(traj, interval=1)
while mincount < self._mdmin:
dyn.run(1)
energies.append(self._atoms.get_potential_energy())
passedmin = self._passedminimum(energies)
if passedmin:
mincount += 1
oldpositions.append(self._atoms.positions.copy())
# Reset atoms to minimum point.
self._atoms.positions = oldpositions[passedmin[0]]
def _read_minima(self):
"""Reads in the list of minima from the minima file."""
exists = os.path.exists(self._minima_traj)
if exists:
empty = os.path.getsize(self._minima_traj) == 0
if os.path.exists(self._minima_traj):
if not empty:
traj = io.PickleTrajectory(self._minima_traj, 'r')
self._minima = [atoms for atoms in traj]
else:
self._minima = []
return True
else:
self._minima = []
return False
def _plot_qn(self, index, line):
"""Plots a dashed vertical line for the optimization."""
if line[1] == 'performing MD':
return
file = os.path.join(self._rundirectory, 'qn%05i.traj' % index)
if os.path.getsize(file) == 0:
return
traj = io.PickleTrajectory(file, 'r')
energies = [traj[0].get_potential_energy(),
traj[-1].get_potential_energy()]
if index > 0:
file = os.path.join(self._rundirectory, 'md%05i.traj' % index)
atoms = io.read(file, index=-3)
energies[0] = atoms.get_potential_energy()
self._ax.plot([index + 0.25] * 2, energies, ':k')
def _plot_md(self, step, line):
"""Adds a curved plot of molecular dynamics trajectory."""
if step == 0:
return
energies = [self._data[step - 1][0]]
file = os.path.join(self._rundirectory, 'md%05i.traj' % step)
traj = io.PickleTrajectory(file, 'r')
for atoms in traj:
energies.append(atoms.get_potential_energy())
xi = step - 1 + .5
if len(energies) > 2:
xf = xi + (step + 0.25 - xi) * len(energies) / (len(energies) - 2.)
else:
xf = step
if xf > (step + .75):
xf = step
self._ax.plot(np.linspace(xi, xf, num=len(energies)), energies, '-k')
images.append(initial.copy())
if restart:
for j in range(1,intermediate_images+1):
nebimage=io.read('neb%d.traj' % j)
nebimage.set_initial_magnetic_moments(mag)
images.append(nebimage)
images.append(final)
if not climb:
neb = NEB(images,k=k)
if climb:
neb = NEB(images,climb=True,k=k)
m.set_neb(neb)
#only for first step, linear interpolation here.
if not restart:
neb.interpolate()
if not climb:
qn = FIRE(neb, logfile='qn.log')
if climb:
qn = FIRE(neb, logfile='qn.log')
for j in range(1,intermediate_images+1):
traj = io.PickleTrajectory('neb%d.traj' % j, 'a', images[j])
qn.attach(traj)
qn.run(fmax=0.05)
def _record_minimum(self):
"""Adds the current atoms configuration to the minima list."""
traj = io.PickleTrajectory(self._minima_traj, 'a')
traj.write(self._atoms)
self._read_minima()
self._log('msg', 'Recorded minima #%i.' % (len(self._minima) - 1))
box = 5. # box dimension
h = 0.25 # grid spacing
width = 0.01 # Fermi width
nbands = 6 # bands in GS calculation
nconv = 4 # bands in GS calculation to converge
R = 2.99 # starting distance
iex = 1 # excited state index
d = 0.01 # step for numerical force evaluation
exc = 'LDA' # xc for the linear response TDDFT kernel
s = Cluster([Atom('Na'), Atom('Na', [0, 0, R])])
s.minimal_box(box, h=h)
c = GPAW(h=h, nbands=nbands, eigensolver='cg',
occupations=FermiDirac(width=width),
setups={'Na': '1'},
convergence={'bands':nconv})
c.calculate(s)
lr = LrTDDFT(c, xc=exc, eps=0.1, jend=nconv-1)
ex = ExcitedState(lr, iex, d=d)
s.set_calculator(ex)
ftraj='relax_ex' + str(iex)
ftraj += '_box' + str(box) + '_h' + str(h)
ftraj += '_d' + str(d) + '.traj'
traj = io.PickleTrajectory(ftraj, 'w', s)
dyn = optimize.FIRE(s)
dyn.attach(traj.write)
dyn.run(fmax=0.05)