def write_mode(self, n, kT=units.kB * 300, nimages=30):
"""Write mode to trajectory file."""
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(self, filename):
from ase_ext.io.trajectory import PickleTrajectory
traj = PickleTrajectory(filename, 'w', self)
traj.write()
traj.close()
class BasinHopping(Dynamics):
"""Basin hopping algorythm.
After Wales and Doye, J. Phys. Chem. A, vol 101 (1997) 5111-5116"""
def __init__(self, atoms,
temperature=100 * kB,
optimizer=FIRE,
fmax=0.1,
dr=.1,
logfile='-',
trajectory='lowest.traj',
optimizer_logfile='-',
local_minima_trajectory='local_minima.traj',
adjust_cm=True):
Dynamics.__init__(self, atoms, logfile, trajectory)
self.kT = temperature
self.optimizer = optimizer
self.fmax = fmax
self.dr = dr
if adjust_cm:
self.cm = atoms.get_center_of_mass()
else:
self.cm = None
self.optimizer_logfile = optimizer_logfile
self.lm_trajectory = local_minima_trajectory
if isinstance(local_minima_trajectory, str):
self.lm_trajectory = PickleTrajectory(local_minima_trajectory,
'w', atoms)
self.initialize()
def initialize(self):
self.positions = 0. * self.atoms.get_positions()
self.Emin = self.get_energy(self.atoms.get_positions())
self.rmin = self.atoms.get_positions()
self.positions = self.atoms.get_positions()
self.call_observers()
self.log(-1, self.Emin, self.Emin)
def run(self, steps):
"""Hop the basins for defined number of steps."""
ro = self.positions
Eo = self.get_energy(ro)
for step in range(steps):
rn = self.move(ro)
En = self.get_energy(rn)
if En < self.Emin:
# new minimum found
self.Emin = En
self.rmin = self.atoms.get_positions()
self.call_observers()
rn = self.rmin
self.log(step, En, self.Emin)
accept = np.exp((Eo - En) / self.kT) > np.random.uniform()
if accept:
ro = rn
Eo = En
def log(self, step, En, Emin):
if self.logfile is None:
return
name = self.__class__.__name__
self.logfile.write('%s: step %d, energy %15.6f, emin %15.6f\n'
% (name, step, En, self.Emin))
self.logfile.flush()
def move(self, ro):
atoms = self.atoms
# displace coordinates
disp = np.random.uniform(-1., 1., (len(atoms), 3))
rn = ro + self.dr * disp
atoms.set_positions(rn)
if self.cm is not None:
cm = atoms.get_center_of_mass()
atoms.translate(self.cm - cm)
rn = atoms.get_positions()
if world is not None:
world.broadcast(rn, 0)
atoms.set_positions(rn)
return atoms.get_positions()
def get_minimum(self):
atoms = self.atoms.copy()
atoms.set_positions(self.rmin)
return self.Emin, atoms
def get_energy(self, positions):
"""Return the energy of the nearest local minimum."""
if np.sometrue(self.positions != positions):
self.positions = positions
self.atoms.set_positions(positions)
try:
opt = self.optimizer(self.atoms, logfile=self.optimizer_logfile)
opt.run(fmax=self.fmax)
if self.lm_trajectory is not None:
self.lm_trajectory.write(self.atoms)
self.energy = self.atoms.get_potential_energy()
except:
# the atoms are probably to near to each other
self.energy = 1.e32
return self.energy