def __init__(self, atoms, restart=None, logfile='-', trajectory=None,
dt=0.1,
maxmove=0.2,dtmax=1.0,Nmin=5,finc=1.1,fdec=0.5,
astart=0.1,fa=0.99,a=0.1):
Optimizer.__init__(self, atoms, restart, logfile, trajectory)
# if dt is not None:
self.dt = dt
self.Nsteps = 0
self.maxmove = maxmove
self.dtmax = dtmax
self.Nmin = Nmin
self.finc = finc
self.fdec = fdec
self.astart = astart
self.fa = fa
self.a = a
def __init__(self, atoms, restart=None, logfile='-', trajectory=None,
maxstep=0.2, dR=0.1,
memory=25, alpha=0.05, method=None, damping=1.):
Optimizer.__init__(self, atoms, restart, logfile, trajectory)
if maxstep > 1.0:
raise ValueError(
'Wanna fly? I know the calculation is too slow. ' +
'But you have to follow the rules.\n'+
' The maximum step size %.1f' % maxstep
+' is too big! \n'+
' Try to set the maximum step size'+
' below 0.2.')
self.maxstep = maxstep
self.dR = dR
self.memory = memory
self.alpha = alpha
self.method = method
self.damping = damping
def __init__(self, atoms, restart=None, logfile='-', trajectory=None,
fmax=None, converged=None,
hessianupdate='BFGS',hessian=None,forcemin=True,
verbosity=None,maxradius=None,
diagonal=20.,radius=None,
transitionstate = False):
Optimizer.__init__(self, atoms, restart, logfile, trajectory)
self.eps = 1e-12
self.hessianupdate = hessianupdate
self.forcemin = forcemin
self.verbosity = verbosity
self.diagonal = diagonal
self.atoms = atoms
n = len(self.atoms) * 3
if radius is None:
self.radius = 0.05*np.sqrt(n)/10.0
else:
self.radius = radius
if maxradius is None:
self.maxradius = 0.5*np.sqrt(n)
else:
self.maxradius = maxradius
# 0.01 < radius < maxradius
self.radius = max(min( self.radius, self.maxradius ), 0.0001)
self.transitionstate = transitionstate
# check if this is a nudged elastic band calculation
if hasattr(atoms,'springconstant'):
self.forcemin=False
self.t0 = time.time()
def __init__(self, atoms, restart=None, logfile='-', trajectory=None,
maxstep=None):
"""Quasi-Newton optimizer.
Parameters:
restart: string
Pickle file used to store hessian matrix. If set, file with
such a name will be searched and hessian matrix stored will
be used, if the file exists.
trajectory: string
Pickle file used to store trajectory of atomic movement.
maxstep: float
Used to set the maximum distance an atom can move per
iteration (default value is 0.04 Å).
"""
Optimizer.__init__(self, atoms, restart, logfile, trajectory)
if maxstep is not None:
if maxstep > 1.0:
raise ValueError('You are using a much too large value for ' +
'the maximum step size: %.1f Å' % maxstep)
self.maxstep = maxstep
def __init__(self, atoms, restart=None, logfile='-', trajectory=None,
dt=None):
Optimizer.__init__(self, atoms, restart, logfile, trajectory)
if dt is not None:
self.dt = dt
c = KeepInPlane(i['DIFFATOM'], (i['CONSATOM1'], i['CONSATOM2']))
atoms.wrap(atoms.get_scaled_positions()[i['DIFFATOM']])
atoms.set_calculator(Vasp())
i = Incar.from_file('INCAR')
if i['EDIFFG'] < 0:
i['NSW'] = 5000
i['NELM'] = 25
i['NELMIN'] = 3
i['IBRION'] = 3
i['POTIM'] = 0
i['PC_EDIFFG'] = i['EDIFFG']
i['EDIFFG'] = 1e-5
if 'IOPT' in i:
del i['IOPT']
i.write_file('INCAR')
atoms.set_constraint(c)
Optimizer.converged = converged_fmax_or_emax
dyn = Optimizer(atoms, trajectory='run.traj', restart='history.pckl', logfile='ase.out')
dyn.run(fmax=-i['PC_EDIFFG'])
if iterate:
print('Converged')
atoms = read('POSCAR') # type: Atoms
dyn = Optimizer(atoms, trajectory='run.traj', restart='history.pckl')
dyn.run(fmax=-i['PC_EDIFFG'])
c = iterate()
print('Done')
{% endblock python %}