atoms=atoms,
kpts_scf=kpts,
reference_level='vbm',
nsemicore=0,
weight=1.,
distribution={
'type': 'Boltzmann',
'kBT': 1.5
})
# Setting up and running the actual optimizer
# (the keyword arguments are known from hotcent.slako.SlaterKosterTable)
confopt = ConfinementOptimizer(atom,
N=500,
rmin=0.4,
dr=0.02,
stride=4,
superposition='density',
xc=xc)
# The initial confinement parameters are the same as in Tutorial #1.
# The additional 'adjustable' keyword argument serves to indicate
# which parameters are allowed to vary. Here we keep the quadratic
# form (s=2) and treat the confinement radii r0 as variable.
rcov = covalent_radii[atomic_numbers[element]] / Bohr
if len(valence) == 1:
initial_guess = {
element + '_' + valence[0]:
PowerConfinement(r0=2 * rcov, s=2, adjustable=['r0']),
element + '_n':
atom.info['hubbardvalues'] = {'s': U_p}
atom.info['occupations'] = {'2s': 2, '2p': occ_2p}
atoms.append(atom)
sk_kwargs = {
'N': {
'default': 380,
'O-O': 420
},
'rmin': 0.4,
'dr': 0.02,
'stride': 5,
'superposition': 'density',
'xc': 'LDA'
}
confopt = ConfinementOptimizer(*atoms, **sk_kwargs)
initial_guess = {}
for element in elements:
rcov = covalent_radii[atomic_numbers[element]] / Bohr
initial_guess['%s_2s,%s_2p' % (element, element)] = \
PowerConfinement(r0=2 * rcov, s=2, adjustable=['r0'])
initial_guess['%s_n' % element] = PowerConfinement(r0=3 * rcov,
s=2,
adjustable=['r0'])
vconf = confopt.run(dpbs.get_residual,
initial_guess=initial_guess,
tol=1e-2,
method='COBYLA',
options={