def minimize_energy(traj_file):
"""
Run a BFGS energy minimization of the smamp molecule in vacuum.
Args:
traj_file: the path to a trajectory file (all atom format)
Returns:
struc: an internal molecular structure object
calc: internal calculation object
"""
# Read in the trajectory file
struc = read(traj_file)
# Set up the box
struc.set_cell([25,25,25])
struc.set_pbc([0,0,0])
struc.center()
# Define gpaw convergence&simulation parameters
calc = GPAW(xc='PBE', h=0.2, charge=0,
spinpol=True, convergence={'energy': 0.001})
struc.set_calculator(calc)
dyn = BFGSLineSearch(struc, trajectory='molecule.traj',
restart='bfgs_ls.pckl', logfile='BFGSLinSearch.log')
# run the simulation
dyn.run(fmax=0.05)
# Maybe this is useful? Does not seem to be needed.
# Epot = struc.get_potential_energy()
# Save everything into a restart file
calc.write('restart.gpw', mode='all')
return struc, calc
def minimise (self,cluster):
'''Minimise a cluster
parameters:
cluster- a Cluster object from bcga.cluster
Using this method will overwrite the coordinates and energy of the
supplied Cluster object.'''
# Fix overlapping atoms to avoid NWChem errors
cluster.fix_overlaps(1.5)
# Set up element labels for NWChem
atom_string=""
for i in range(0,len(cluster.labels)):
atom_string+=cluster.labels[i]+str(get_composition(cluster.atom_types)[i])
print(atom_string)
mol = Atoms(atom_string,
positions=cluster._coords,
cell=(6.0,6.0,6.0))
mol.center()
# Run GPAW calculation
calc = GPAW(**self.GPAWargs)
mol.set_calculator(calc)
opt = BFGSLineSearch(mol)
try:
opt.run(fmax=0.25)
except:
sys.exit()
# Get back cluster properties from GPAW
cluster.energy=mol.get_potential_energy()
cluster.quenched=True
return cluster
def minimise(self, cluster):
'''Minimise a cluster
parameters:
cluster- a Cluster object from bcga.cluster
Using this method will overwrite the coordinates and energy of the
supplied Cluster object.'''
# Fix overlapping atoms to avoid NWChem errors
cluster.fix_overlaps(1.5)
# Set up element labels for NWChem
atom_string = ""
for i in range(0, len(cluster.labels)):
atom_string += cluster.labels[i] + str(
get_composition(cluster.atom_types)[i])
print(atom_string)
mol = Atoms(atom_string,
positions=cluster._coords,
cell=(6.0, 6.0, 6.0))
mol.center()
# Run GPAW calculation
calc = GPAW(**self.GPAWargs)
mol.set_calculator(calc)
opt = BFGSLineSearch(mol)
try:
opt.run(fmax=0.25)
except:
sys.exit()
# Get back cluster properties from GPAW
cluster.energy = mol.get_potential_energy()
cluster.quenched = True
return cluster
def __init__(self,
atoms,
restart=None,
logfile='-',
maxstep=.2,
trajectory=None,
c1=0.23,
c2=0.46,
alpha=10.0,
stpmax=50.0,
master=None,
force_consistent=None,
zlim=None):
"""
add zlim to BFGSLineSearch:
zlim = [z_min, z_max]
"""
self.zlim = zlim
BFGSLineSearch.__init__(self,
atoms,
restart=restart,
logfile=logfile,
maxstep=maxstep,
trajectory=trajectory,
c1=c1,
c2=c2,
alpha=alpha,
stpmax=stpmax,
master=master,
force_consistent=force_consistent)
def createparticle(type1, type2, numbertype1):
calc = QSC()
atoms = ase.io.read('POSCAR_genetic', format='vasp')
shell = deepcopy(atoms)
core = deepcopy(atoms)
for i in range(85, 225):
core.pop(85)
for i in range(85):
shell.pop(0)
dummyarray = core.get_chemical_symbols()
for i in range(numbertype1):
dummyarray[i] = type1
for i in range(numbertype1, 85):
dummyarray[i] = type2
random.shuffle(dummyarray)
core.set_chemical_symbols(dummyarray)
for i in range(len(shell)):
core.append(shell.pop(0))
core.set_calculator(calc)
opt = BFGSLineSearch(core)
opt.run(steps=8)
opt = FIRE(core)
opt.run(steps=2000)
return core
def createparticle(type1, type2, numbertype1):
calc = QSC()
atoms = ase.io.read('POSCAR_genetic',format='vasp')
shell = deepcopy(atoms)
core = deepcopy(atoms)
for i in range(85,225):
core.pop(85)
for i in range(85):
shell.pop(0)
dummyarray = core.get_chemical_symbols()
for i in range(numbertype1):
dummyarray[i] = type1
for i in range(numbertype1,85):
dummyarray[i] = type2
random.shuffle(dummyarray)
core.set_chemical_symbols(dummyarray)
for i in range(len(shell)):
core.append(shell.pop(0))
core.set_calculator(calc)
opt = BFGSLineSearch(core)
opt.run(steps=8)
opt = FIRE(core)
opt.run(steps=2000)
return core
#!/usr/bin/env python
#PBS -m ae
#PBS -q verylong
#PBS -l nodes=1:ppn=8
#!/usr/bin/env python
from ase.io import read
from gpaw import GPAW
from ase.optimize.bfgslinesearch import BFGSLineSearch
slab = read('CH4Au532.xyz')
slab.set_cell([[7.309254, 0., 0.], [4.872836, 7.509545, 0.], [0., 0., 20.]],
scale_atoms=False)
slab.set_pbc((1, 1, 1))
calc = GPAW(h=0.18, kpts=(4, 4, 1))
slab.set_calculator(calc)
dyn = BFGSLineSearch(slab, trajectory='relax.traj')
dyn.run(fmax=0.02)
parser.add_argument('outfile_pckl',
nargs='?',
metavar='bfgs_ls.pckl',
default='bfgs_ls.pckl',
help="Restart file, default 'bfgs_ls.pckl'")
parser.add_argument('logfile',
nargs='?',
metavar='BFGSLinSearch.log',
default='BFGSLinSearch.log',
help="Optimization's log, default 'BFGSLinSearch.log'")
args = parser.parse_args()
struc = molecule('H2O')
struc.set_pbc([0, 0, 0])
struc.set_cell([5, 5, 5])
struc.center()
calc = GPAW(xc='PBE',
h=0.2,
charge=0,
spinpol=True,
convergence={'energy': 0.001})
struc.set_calculator(calc)
dyn = BFGSLineSearch(struc,
trajectory=args.outfile_traj,
restart=args.outfile_pckl,
logfile=args.logfile)
dyn.run(fmax=0.05)
traj_file = 'benzene.traj'
if not os.path.isfile(traj_file):
struc.set_cell([15, 15, 15])
struc.set_pbc([0, 0, 0])
struc.center()
calc = GPAW(xc='PBE',
h=0.2,
charge=0,
spinpol=True,
convergence={'energy': 0.001})
struc.set_calculator(calc)
#opt = FIRE(struc, trajectory='benzene.traj', logfile='fire.log')
dyn = BFGSLineSearch(struc,
trajectory=traj_file,
restart='bfgs_ls.pckl',
logfile='BFGSLinSearch.log')
dyn.run(fmax=0.05)
# Did not find any documentation on how to directly continue with results
struc = read(traj_file)
calc = GPAW(xc='PBE',
h=0.2,
charge=0,
spinpol=True,
convergence={'energy': 0.001})
struc.set_calculator(calc) # is it necessary?
struc.get_potential_energy()
vHtg = struc.calc.get_electrostatic_potential()
rho4 = struc.calc.get_all_electron_density(gridrefinement=4)
cluster = read('Au55.xyz')
cell = [(17.79365715,0,0),
(0,19.60846479,0),
(0,0, 19.84025464)]
cluster.set_cell(cell,scale_atoms=False)
cluster.center()
kwargs_lcao = dict(mode='lcao',
#basis='dzp',
convergence={'density':0.1, 'energy':0.1}
)#poissonsolver=poissonsolver)
calc = GPAW(h=0.18,txt=None,**kwargs_lcao)
cluster.set_calculator(calc)
dyn1 = BFGSLineSearch(cluster, trajectory='Au_cluster_lcao.traj')
dyn1.run(fmax=0.02)
e_cluster_lcao = cluster.get_potential_energy()
#cluster.set_calculator(GPAW(h=0.18,tex=None))
dyn2 = BFGSLineSearch(cluster, trajectory='Au_cluster_paw.traj')
dyn2.run(fmax=0.02)
e_cluster_paw = cluster.get_potential_energy()
#Relax CO molecule with both lcao and paw modes
CO = Atoms([Atom('C',(1.0,1.0,1.0)),
Atom('O',(1.0,1.0,2.3))],
cell=(12,12.5,14.5))
CO.set_calculator(calc)
CO.center()
dyn3 = BFGSLineSearch(CO)
#Relax the Au 55 atoms cluster with both lcao and paw modes
cluster = read('Au55.xyz')
cell = [(17.79365715,0,0),
(0,19.60846479,0),
(0,0, 19.84025464)]
cluster.set_cell(cell,scale_atoms=False)
cluster.center()
kwargs_lcao = dict(mode='lcao',
#basis='dzp',
convergence={'density':0.1, 'energy':0.1})
calc = GPAW(h=0.18,txt=None,**kwargs_lcao)
cluster.set_calculator(calc)
dyn1 = BFGSLineSearch(cluster, trajectory='Au_cluster_lcao.traj')
dyn1.run(fmax=0.02)
e_cluster_lcao = cluster.get_potential_energy()
#cluster.set_calculator(GPAW(h=0.18,tex=None))
dyn2 = BFGSLineSearch(cluster, trajectory='Au_cluster_paw.traj')
dyn2.run(fmax=0.02)
e_cluster_paw = cluster.get_potential_energy()
#Relax CO molecule with both lcao and paw modes
CO = Atoms([Atom('C',(1.0,1.0,1.0)),
Atom('O',(1.0,1.0,2.3))],
cell=(12,12.5,14.5))
CO.set_calculator(calc)
CO.center()
dyn3 = BFGSLineSearch(CO)
from ase.io import read
from gpaw import GPAW
from ase.optimize.bfgslinesearch import BFGSLineSearch
slab = read('CH4Au532.xyz')
slab.set_cell([[7.309254, 0., 0.], [4.872836, 7.509545, 0.], [0., 0., 20.]],scale_atoms=False)
slab.set_pbc((1,1,1))
calc = GPAW(h=0.18, kpts=(4, 4, 1))
slab.set_calculator(calc)
dyn = BFGSLineSearch(slab,trajectory='relax.traj')
dyn.run(fmax=0.02)
r_OH2 = 1.90792059
theta = 114.4996469200000 * np.pi / 180.0
r_o, r_h1, r_h2 = get_cart_coords_h2o(r_OH1, r_OH2, theta, [0.0,0.0, 0.0])
atoms = Atoms('OHH', positions=[r_o, r_h1, r_h2])
calc = H2o_pjt2_calculator()
atoms.set_calculator(calc)
E = atoms.get_potential_energy()
print(E)
# optimize geometry
dyn = BFGSLineSearch(atoms, logfile='h2o_opt.log')
dyn.run(fmax=0.00001)
pos = atoms.get_positions()
q1,q2,theta = get_internal_coords_h2o(pos[0], pos[1], pos[2])
coords = [q1, q2, theta * 180/np.pi]
ref=np.array([0.957920759532, 0.957920773357, 104.499646202])
error = np.sqrt(np.sum((coords - ref)**2))
print('coords')
print(coords)
print('diff from reference:')
print(error)
