def get_optimal_h(atoms, natoms, dyn = False, show = False):
# This find the optimal h - when the top is sliding:
if not dyn:
from scipy.optimize import fmin
pos_init = atoms.positions.copy()
def get_epot(z):
new_pos = pos_init
new_pos[:,2] = z
atoms.positions = new_pos
e = atoms.get_potential_energy()/natoms
#print z, e
return e
hmin = fmin(get_epot, 3.4, disp = 0)
emin = get_epot(hmin)
atoms.positions = pos_init
if show: print 'optimal height= %.2f and e=%.2f' %(hmin, emin)
return emin, hmin
else:
dyn = BFGS(atoms)
dyn.run(fmax=0.03)
e = atoms.get_potential_energy()/natoms
hmin = np.average(atoms.positions[:,2])
return e, hmin
def get_optimal_h(atoms, bottom, top, natoms, dyn = False):
# This find the optimal h - when the top is sliding:
if not dyn:
from scipy.optimize import fmin
pos_init = atoms.positions.copy()
zmax = np.amax(atoms.positions[bottom][:,2])
def get_epot(z):
new_pos = pos_init
for iat in range(len(atoms)):
if top[iat]:
new_pos[iat][2] = z + zmax
atoms.positions = new_pos
e = atoms.get_potential_energy()/natoms
print z, e
return e
hmin = fmin(get_epot, 3.34)
emin = get_epot(hmin)
atoms.positions = pos_init
print 'optimal height= %.2f and e=%.2f' %(hmin, emin)
return emin, hmin
else:
dyn = BFGS(atoms)
dyn.run(fmax=0.03)
e = atoms.get_potential_energy()/natoms
layers = find_layers(atoms.positions)[0]
hmin = layers[1] - layers[0]
return e, hmin
def test_geopt():
calc = CP2K(label='test_geopt')
h2 = molecule('H2', calculator=calc)
h2.center(vacuum=2.0)
dyn = BFGS(h2)
dyn.run(fmax=0.05)
dist = h2.get_distance(0, 1)
diff = abs((dist - 1.36733746519) / dist)
assert(diff < 1e-10)
print('passed test "geopt"')
def run(sigma, atoms):
calc = CP2K(label = 'molecules/co-relax/sigma{0}'.format(sigma),
xc='PBE')
atoms.set_calculator(calc)
gopt = BFGS(atoms, logfile=None)
gopt.run(fmax=1e-2)
e = atoms.get_potential_energy()
pos = atoms.get_positions()
d = ((pos[0] - pos[1])**2).sum()**0.5
print('{0:1.2f} {1:1.4f} {2:1.4f}'.format(sigma, e, d))
def idpp_interpolate(self, traj='idpp.traj', log='idpp.log', fmax=0.1,
optimizer=BFGS):
d1 = self.images[0].get_all_distances()
d2 = self.images[-1].get_all_distances()
d = (d2 - d1) / (self.nimages - 1)
old = []
for i, image in enumerate(self.images):
old.append(image.calc)
image.calc = IDPP(d1 + i * d)
opt = BFGS(self, trajectory=traj, logfile=log)
opt.run(fmax=0.1)
for image, calc in zip(self.images, old):
image.calc = calc
def ase_vol_relax():
Al = bulk('Al', 'fcc', a=4.5, cubic=True)
calc = Vasp(xc='LDA')
Al.set_calculator(calc)
from ase.constraints import StrainFilter
sf = StrainFilter(Al)
qn = QuasiNewton(sf, logfile='relaxation.log')
qn.run(fmax=0.1, steps=5)
print 'Stress:\n', calc.read_stress()
print 'Al post ASE volume relaxation\n', calc.get_atoms().get_cell()
return Al
def ase_vol_relax():
Al = bulk("Al", "fcc", a=4.5, cubic=True)
calc = Vasp(xc="LDA")
Al.set_calculator(calc)
from ase.constraints import StrainFilter
sf = StrainFilter(Al)
qn = QuasiNewton(sf, logfile="relaxation.log")
qn.run(fmax=0.1, steps=5)
print("Stress:\n", calc.read_stress())
print("Al post ASE volume relaxation\n", calc.get_atoms().get_cell())
return Al
def run_neb_calculation(cpu):
images = [PickleTrajectory('H.traj')[-1]]
for i in range(nimages):
images.append(images[0].copy())
images[-1].positions[6, 1] = 2 - images[0].positions[6, 1]
neb = NEB(images, parallel=True, world=cpu)
neb.interpolate()
images[cpu.rank + 1].set_calculator(Calculator())
dyn = BFGS(neb)
dyn.run(fmax=fmax)
if cpu.rank == 1:
results.append(images[2].get_potential_energy())
def test_pwscf_calculator():
if not have_ase():
skip("no ASE found, skipping test")
elif not have_pwx():
skip("no pw.x found, skipping test")
else:
pseudo_dir = pj(testdir, prefix, 'pseudo')
print common.backtick("mkdir -pv {p}; cp files/qe_pseudos/*.gz {p}/; \
gunzip {p}/*".format(p=pseudo_dir))
at = get_atoms_with_calc_pwscf(pseudo_dir)
print "scf"
# trigger calculation here
forces = at.get_forces()
etot = at.get_potential_energy()
stress = at.get_stress(voigt=False) # 3x3
st = io.read_pw_scf(at.calc.label + '.out')
assert np.allclose(forces, st.forces)
assert np.allclose(etot, st.etot)
assert np.allclose(st.stress, -stress * constants.eV_by_Ang3_to_GPa)
# files/ase/pw.scf.out.start is a norm-conserving LDA struct,
# calculated with pz-vbc.UPF, so the PBE vc-relax will make the cell
# a bit bigger
print "vc-relax"
from ase.optimize import BFGS
from ase.constraints import UnitCellFilter
opt = BFGS(UnitCellFilter(at))
cell = parse.arr2d_from_txt("""
-1.97281509 0. 1.97281509
0. 1.97281509 1.97281509
-1.97281509 1.97281509 0.""")
assert np.allclose(cell, at.get_cell())
opt.run(fmax=0.05) # run only 2 steps
cell = parse.arr2d_from_txt("""
-2.01837531 0. 2.01837531
0. 2.01837531 2.01837531
-2.01837531 2.01837531 0""")
assert np.allclose(cell, at.get_cell())
# at least 1 backup files must exist: pw.*.0 is the SCF run, backed up
# in the first iter of the vc-relax
assert os.path.exists(at.calc.infile + '.0')
def relaxBend(bend, left_idxs, right_idxs, edge, bond, mdrelax):
constraints = []
constraints.append(FixAtoms(indices = left_idxs))
constraints.append(FixAtoms(indices = right_idxs))
#twist = twistConst_Rod(bend, 1, edge, bond ,F = 20)
#twist.set_angle(np.pi/3 + 2./180*np.pi)
#constraints.append(twist)
add_pot = LJ_potential_smooth(bend, bond)
constraints.append(add_pot)
calc = LAMMPS(parameters=get_lammps_params())
bend.set_calculator(calc)
# END CALCULATOR
# RELAX
bend.set_constraint(constraints)
dyn = BFGS(bend, trajectory = mdrelax)
dyn.run(fmax=0.05)
def test_lammps_calculator():
if not have_ase():
skip("no ASE found, skipping test")
elif not have_lmp():
skip("no lammps found, skipping test")
else:
at = get_atoms_with_calc_lammps()
at.rattle(stdev=0.001, seed=int(time.time()))
common.makedirs(at.calc.directory)
print common.backtick("cp -v utils/lammps/AlN.tersoff {p}/".format(
p=at.calc.directory))
print "scf"
forces = at.get_forces()
etot = at.get_potential_energy()
stress = at.get_stress(voigt=False) # 3x3
st = io.read_lammps_md_txt(at.calc.label + '.out')[0]
assert np.allclose(forces, st.forces)
assert np.allclose(etot, st.etot)
assert np.allclose(st.stress, -stress * constants.eV_by_Ang3_to_GPa,
atol=1e-10)
print "relax"
from ase.optimize import BFGS
opt = BFGS(at, maxstep=0.04)
opt.run(fmax=0.001, steps=10)
coords_frac = parse.arr2d_from_txt("""
3.3333341909920072e-01 6.6666683819841532e-01 4.4325467247779138e-03
6.6666681184103216e-01 3.3333362368205072e-01 5.0443254824788963e-01
3.3333341909918301e-01 6.6666683819838046e-01 3.8356759709402671e-01
6.6666681184101539e-01 3.3333362368201563e-01 8.8356759861713752e-01
""")
assert np.allclose(coords_frac, at.get_scaled_positions(), atol=1e-2)
# at least 1 backup files must exist
assert os.path.exists(at.calc.infile + '.0')
assert os.path.exists(at.calc.outfile + '.0')
assert os.path.exists(at.calc.dumpfile + '.0')
assert os.path.exists(at.calc.structfile + '.0')
def main():
if "ASE_CP2K_COMMAND" not in os.environ:
raise NotAvailable('$ASE_CP2K_COMMAND not defined')
calc = CP2K(label='test_H2_GOPT')
atoms = molecule('H2', calculator=calc)
atoms.center(vacuum=2.0)
# Run Geo-Opt
gopt = BFGS(atoms, logfile=None)
gopt.run(fmax=1e-6)
# check distance
dist = atoms.get_distance(0, 1)
dist_ref = 0.7245595
assert (dist - dist_ref) / dist_ref < 1e-7
# check energy
energy_ref = -30.7025616943
energy = atoms.get_potential_energy()
assert (energy - energy_ref) / energy_ref < 1e-10
print('passed test "H2_GEO_OPT"')
def test(size, R, nk):
atoms_flat = get_square_uCell(size)
view(atoms_flat)
# CALCULATOR FLAT
calc_f = Hotbit(SCC=False, kpts=(nk,nk,1), \
txt= path + 'test_consistency/optimization_flat.cal')
atoms_flat.set_calculator(calc_f)
opt_f = BFGS(atoms_flat)
opt_f.run(fmax = 0.05)
e_flat = atoms_flat.get_potential_energy()
atoms_c = atoms_flat.copy()
L = atoms_c.get_cell().diagonal()
atoms_c.set_cell(L)
angle = L[1]/R
atoms_c.rotate('y', np.pi/2)
atoms_c.translate((-atoms_c[0].x, 0, 0) )
for a in atoms_c:
r0 = a.position
phi = r0[1]/L[1]*angle
a.position[0] = R*np.cos(phi)
a.position[1] = R*np.sin(phi)
atoms_c = Atoms(atoms = atoms_c, container = 'Wedge')
atoms_c.set_container(angle = angle, height = L[0], physical = False, pbcz = True)
if R < 100:
view(atoms_c.extended_copy((8,1,3)))
# CALCULATOR Cyl
calc_c = Hotbit(SCC=False, kpts=(nk,1, nk), physical_k = False, \
txt= path + 'test_consistency/optimization_cyl.cal')
atoms_c.set_calculator(calc_c)
opt_c = BFGS(atoms_c)
opt_c.run(fmax = 0.05)
e_cyl = atoms_c.get_potential_energy()
print 'R = %.2f' %R
print 'energy flat = %.6f' %e_flat
print 'energy cylinder = %.6f' %e_cyl
print 'energy dif (e_cylinder - eflat)/nAtoms = %.6f \n' %(-(e_flat - e_cyl)/len(atoms_flat))
return e_flat, e_cyl, len(atoms_flat)
def ase_minimization(indiv, Optimizer):
"""Function to use built in ASE minimizers to minimize atomic positions in structure.
Input:
indiv = Individual class object to be optimized
Optimizer = Optimizer class object with needed parameters
Output:
indiv = Optimized Individual class object.
"""
if 'MU' in Optimizer.debug:
debug = True
else:
debug = False
cwd1=os.getcwd()
olammpsmin = Optimizer.lammps_min
if Optimizer.lammps_min:
Optimizer.lammps_min = None
calc2 = setup_calculator(Optimizer)
# if 'mass' in indiv[0].get_calculator():
# mass2 = ['1 '+ str(Optimizer.atomlist[0][2])]
# if len(Optimizer.atomlist) > 1:
# for i in range(len(Optimizer.atomlist)-1):
# mass2.append(str(i+2) + ' ' + str(Optimizer.atomlist[i+1][2]))
# calc2=LAMMPS(parameters={ 'pair_style' : Optimizer.pair_style, 'pair_coeff' : Optimizer.pair_coeff , 'mass' : mass2 },files=[ Optimizer.pot_file ])
# else:
# calc2=LAMMPS(parameters={ 'pair_style' : Optimizer.pair_style, 'pair_coeff' : Optimizer.pair_coeff},files=[ Optimizer.pot_file ])
if Optimizer.structure==Defect:
nat=indiv[0].get_number_of_atoms
sol=Atoms()
sol.extend(indiv[0])
sol.extend(indiv.bulko)
sol.set_calculator(calc2)
sol.set_cell(indiv.bulko.get_cell())
sol.set_pbc(True)
dyn=BFGS(sol)
dyn.run(fmax=0.001, steps=2500)
positions=sol[0:nat].get_positions()
indiv[0].set_positions(positions)
else:
atomsdup=indiv[0].copy()
atomsdup.set_calculator(calc2)
dyn=BFGS(indiv[0])
dyn.run(fmax=0.001, steps=2500)
positions=atomsdup.get_positions()
indiv[0].set_positions(positions)
os.chdir(cwd1)
calc2.clean()
Optimizer.lammps_min = olammpsmin
Optimizer.output.write('ASE Minimization mutation performed on individual = '+repr(indiv.index)+'\n')
muttype='ASEM'
if indiv.energy==0:
indiv.history_index=indiv.history_index+'m'+muttype
else:
indiv.history_index=repr(indiv.index)+'m'+muttype
return indiv
def run_ase_min(totalsol, fmax=0.01, mxstep=1000, fitscheme='totalenfit', STR=''):
try:
dyn=BFGS(totalsol)
dyn.run(fmax=fmax, steps=mxstep)
except OverflowError:
STR+='--- Error: Infinite Energy Calculated - Implement Random shake---\n'
totalsol.rattle(stdev=0.3)
dyn=BFGS(totalsol)
dyn.run(fmax=fmax, steps=mxstep)
except numpy.linalg.linalg.LinAlgError:
STR+='--- Error: Singular Matrix - Implement Random shake ---\n'
totalsol.rattle(stdev=0.2)
dyn=BFGS(totalsol)
dyn.run(fmax=fmax, steps=mxstep)
# Get Energy of Minimized Structure
en=totalsol.get_potential_energy()
if fitscheme == 'enthalpyfit':
pressure=totalsol.get_isotropic_pressure(totalsol.get_stress())
else:
pressure=0
volume = totalsol.get_volume()
energy=en
return totalsol, energy, pressure, volume, STR
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jun 15 00:45:20 2020
@author: macenrola
"""
from ase import Atoms, io, calculators
import ase.calculators.cp2k
from gpaw import GPAW, PW
from ase.optimize import BFGS
from ase.vibrations import Vibrations
mol = io.read(
"/home/macenrola/Documents/cb8_MV_electrochemistry_JIA/apbs_ref/benzene.pdb"
)
mol.center(vacuum=3.5)
print(mol.cell)
print(mol.positions)
c = ase.calculators.cp2k.CP2K()
CP2K.command = "env OMP_NUM_THREADS=2 mpirun -np 4 cp2k"
print(mol.set_calculator(c))
BFGS(mol).run(fmax=0.01)
vib = Vibrations(mol)
vib.run()
print(vib.summary())
# Regardless of the dtype you use in your model, when converting it to ASE
# calculator, it always automatically the dtype to ``torch.float64``. The
# reason for this behavior is, at many cases, the rounding error is too
# large for structure minimization. If you insist on using
# ``torch.float32``, do the following instead:
#
# .. code-block:: python
#
# calculator = torchani.models.ANI1ccx().ase(dtype=torch.float32)
# Now let's set the calculator for ``atoms``:
atoms.set_calculator(calculator)
# Now let's minimize the structure:
print("Begin minimizing...")
opt = BFGS(atoms)
opt.run(fmax=0.001)
print()
# Now create a callback function that print interesting physical quantities:
def printenergy(a=atoms):
"""Function to print the potential, kinetic and total energy."""
epot = a.get_potential_energy() / len(a)
ekin = a.get_kinetic_energy() / len(a)
print('Energy per atom: Epot = %.3feV Ekin = %.3feV (T=%3.0fK) '
'Etot = %.3feV' % (epot, ekin, ekin / (1.5 * units.kB), epot + ekin))
# We want to run MD with constant energy using the Langevin algorithm
# with a time step of 1 fs, the temperature 300K and the friction
import os
from ase.io import read
from ase.neb import NEB
from ase.calculators.turbomole import Turbomole
from ase.optimize import BFGS
initial = read('initial.coord')
final = read('final.coord')
os.system('rm -f coord; cp initial.coord coord')
# Make a band consisting of 5 configs:
configs = [initial]
configs += [initial.copy() for i in range(3)]
configs += [final]
band = NEB(configs, climb=True)
# Interpolate linearly the positions of the not-endpoint-configs:
band.interpolate()
#Set calculators
for config in configs:
config.set_calculator(Turbomole())
# Optimize the Path:
relax = BFGS(band, trajectory='neb.traj')
relax.run(fmax=0.05)
from ase import Atoms
from ase.optimize import BFGS
from gpaw import GPAW
atoms = Atoms('HOH', positions=[[0, 0, -1], [0, 1, 0], [0, 0, 1]])
atoms.center(vacuum=3.0)
calc = GPAW(mode='lcao', basis='dzp', txt='gpaw.txt')
atoms.calc = calc
opt = BFGS(atoms, trajectory='opt.traj')
opt.run(fmax=0.05)
#neb = NEB(images, dynamic_relaxation=True, scale_fmax=1.)
# Interpolate the interiorimages
#neb.interpolate('idpp',mic=True)
# Give each image its respective directory and assign a calculator
home = os.getcwd()
for i in range(nimages + 2):
imgdir = '%02d' % i
if imgdir not in os.listdir(home):
os.mkdir(imgdir)
write(imgdir + '/POSCAR', images[i])
if i not in [0, nimages + 1]:
images[i].set_calculator(calculator(imgdir))
# Run without climbing
#opt = BFGS(neb, trajectory='neb_noclimb.traj', logfile='neb_noclimb.log')
#opt.run(fmax=0.05)
#write('Final_NEB_non_climbed.traj', images)
# Run with climbing and tighter convergence
neb = NEB(images,
dynamic_relaxation=True,
scale_fmax=2.,
fmax=0.03,
climb=True)
opt = BFGS(neb, trajectory='neb_climb.traj', logfile='neb_climb.log')
opt.run(fmax=0.03)
write('Final_NEB_climbed.traj', images)
from ase.io import read
from ase.constraints import FixAtoms
from ase.neb import NEB
from JDFTx import JDFTx
from ase.optimize import BFGS
from ase.parallel import rank, size
initial = read('initial.traj')
final = read('final.traj')
constraint = FixAtoms(mask=[atom.tag > 1 for atom in initial])
images = [initial]
for i in range(3):
image = initial.copy()
image.set_calculator(JDFTx(executable='mpirun -n 1 -N 1 -c 12 --exclude=node[1001-1032] /home/jfm343/JDFTXDIR/build/jdftx', pseudoDir='/home/jfm343/JDFTXDIR/build/pseudopotentials',pseudoSet='GBRV-pbe',commands={'elec-cutoff' : '20 100','kpoint-folding' : '2 2 1'}))
image.set_constraint(constraint)
images.append(image)
images.append(final)
neb = NEB(images, parallel=True)
neb.interpolate()
qn = BFGS(neb, trajectory='neb.traj')
qn.run(fmax=0.05)
# Make a mask of zeros and ones that select fixed atoms (the
# two bottom layers):
mask = initial.positions[:, 2] - min(initial.positions[:, 2]) < 1.5 * h
constraint = FixAtoms(mask=mask)
print mask
for image in images:
# Let all images use an EMT calculator:
image.set_calculator(EMT())
image.set_constraint(constraint)
# Relax the initial and final states:
QuasiNewton(initial).run(fmax=0.05)
QuasiNewton(final).run(fmax=0.05)
# Create a Nudged Elastic Band:
neb = NEB(images)
# Make a starting guess for the minimum energy path (a straight line
# from the initial to the final state):
neb.interpolate()
# Relax the NEB path:
minimizer = BFGS(neb)
minimizer.run(fmax=0.05)
# Write the path to a trajectory:
view(images) # 126 meV
write('jump1.traj', images)
from ase.structure import molecule
from ase.optimize import BFGS
from gpaw import GPAW
from gpaw.mixer import MixerDif
for name in ['H2', 'N2', 'O2', 'NO']:
mol = molecule(name)
mol.center(vacuum=5.0)
calc = GPAW(xc='PBE',
h=0.2,
txt=name + '.txt')
if name == 'NO':
mol.translate((0, 0.1, 0))
calc.set(mixer=MixerDif(0.05,5))
mol.set_calculator(calc)
opt = BFGS(mol, logfile=name + '.log', trajectory=name + '.traj')
opt.run(fmax=0.05)
calc.write(name)
forces_an = atoms.get_forces()
ref = np.array([[-1.26446863e-01, 4.09628186e-01, -0.00000000e+00],
[4.27934442e-01, 2.50425467e-02, -5.14220671e-05],
[-2.99225008e-01, -4.31533987e-01, -5.14220671e-05]])
error = np.sqrt(np.sum((forces_an - ref)**2))
print('forces_an')
print(forces_an)
print('diff from reference:')
print(error)
assert(error < tol)
# optimize geometry
dyn = BFGS(atoms)
dyn.run(fmax=0.01)
positions = atoms.get_positions()
ref = np.array([[ 9.61364579e-01, 2.81689367e-02, -1.58730770e-06],
[ -3.10444398e-01, 9.10289261e-01, -5.66399075e-06],
[ -1.56957763e-02, -2.26044053e-02, -2.34155615e-06]])
error = np.sqrt(np.sum((positions - ref)**2))
print('positions')
print(positions)
print('diff from reference:')
print(error)
assert(error < tol)
from ase.optimize import BFGS
from ase.io import read, write
from ase.calculators.emt import EMT
from ase.ga.relax_attaches import VariansBreak
import sys
fname = sys.argv[1]
print("Now relaxing {0}".format(fname))
a = read(fname)
a.set_calculator(EMT())
dyn = BFGS(a, trajectory=None, logfile=None)
vb = VariansBreak(a, dyn)
dyn.attach(vb.write)
dyn.run(fmax=0.05)
a.info["key_value_pairs"]["raw_score"] = -a.get_potential_energy()
write(fname[:-5] + "_done.traj", a)
print("Done relaxing {0}".format(fname))
nsteps = '0',
nstfout = '1',
nstlog = '1',
nstenergy = '1',
nstlist = '1',
ns_type = 'grid',
pbc = 'xyz',
rlist = '1.15',
coulombtype = 'PME-Switch',
rcoulomb = '0.8',
vdwtype = 'shift',
rvdw = '0.8',
rvdw_switch = '0.75',
DispCorr = 'Ener')
CALC_MM.generate_topology_and_g96file()
CALC_MM.generate_gromacs_run_file()
CALC_QMMM = AseQmmmManyqm(nqm_regions = 3,
qm_calculators = [CALC_QM1, CALC_QM2, CALC_QM3],
mm_calculator = CALC_MM,
link_info = 'byQM')
# link_info = 'byFILE')
SYSTEM = read_gromos('gromacs_qm.g96')
SYSTEM.set_calculator(CALC_QMMM)
DYN = BFGS(SYSTEM)
DYN.run(fmax = 0.05)
print('exiting fine')
LOG_FILE.close()
def test_dynamic_neb():
# Global counter of force evaluations:
force_evaluations = [0]
class EMT(OrigEMT):
def calculate(self, *args, **kwargs):
force_evaluations[0] += 1
OrigEMT.calculate(self, *args, **kwargs)
# Build Pt(111) slab with six surface atoms and add oxygen adsorbate
initial = fcc111('Pt', size=(3, 2, 3), orthogonal=True)
initial.center(axis=2, vacuum=10)
oxygen = Atoms('O')
oxygen.translate(initial[7].position + (0., 0., 3.5))
initial.extend(oxygen)
# EMT potential
initial.calc = EMT()
# Optimize initial state
opt = BFGS(initial)
opt.run(fmax=0.03)
# Move oxygen adsorbate to neighboring hollow site
final = initial.copy()
final[18].x += 2.8
final[18].y += 1.8
final.calc = EMT()
opt = BFGS(final)
opt.run(fmax=0.03)
# NEB with seven interior images
images = [initial]
for i in range(7):
images.append(initial.copy())
images.append(final)
fmax = 0.03 # Same for NEB and optimizer
for i in range(1, len(images) - 1):
calc = EMT()
images[i].calc = calc
def run_NEB():
if method == 'dyn':
neb = DyNEB(images, fmax=fmax, dynamic_relaxation=True)
neb.interpolate()
elif method == 'dyn_scale':
neb = DyNEB(images,
fmax=fmax,
dynamic_relaxation=True,
scale_fmax=6.)
neb.interpolate()
else:
# Default NEB
neb = DyNEB(images, dynamic_relaxation=False)
neb.interpolate()
# Optimize and check number of calculations.
# We use a hack with a global counter to count the force evaluations:
force_evaluations[0] = 0
opt = BFGS(neb)
opt.run(fmax=fmax)
force_calls.append(force_evaluations[0])
# Get potential energy of transition state.
Emax.append(
np.sort([image.get_potential_energy()
for image in images[1:-1]])[-1])
force_calls, Emax = [], []
for method in ['def', 'dyn', 'dyn_scale']:
run_NEB()
# Check force calculation count for default and dynamic NEB implementations
print('\n# Force calls with default NEB: {}'.format(force_calls[0]))
print('# Force calls with dynamic NEB: {}'.format(force_calls[1]))
print('# Force calls with dynamic and scaled NEB: {}\n'.format(
force_calls[2]))
assert force_calls[2] < force_calls[1] < force_calls[0]
# Assert reaction barriers are within 1 meV of default NEB
assert (abs(Emax[1] - Emax[0]) < 1e-3)
assert (abs(Emax[2] - Emax[0]) < 1e-3)
def do_short_relax(atoms, index=None, vc_relax=False, precon=True,
maxsteps=20):
''' Performs a (usually short) local optimization.
atoms: an Atoms object
index: index to be used as suffix for the output files
vc_relax: whether to also optimize the cell vectors
(after having run several steps with fixed cell)
precon: whether to use the preconditioned optimizers
maxsteps: maximum number of ionic steps
'''
if vc_relax:
assert precon
t = time()
label = 'opt' if index is None else 'opt_' + str(index)
logfile = '%s.log' % label
trajfile = '%s.traj' % label
traj = Trajectory(trajfile, 'a', atoms)
nsteps = 0
maxsteps_no_vc = maxsteps / 2 if vc_relax else maxsteps
fmax = 2. if vc_relax else 0.1
try:
if precon:
dyn = PreconLBFGS_My(atoms, precon=Exp(A=3), variable_cell=False,
use_armijo=True, a_min=1e-2, logfile=logfile)
else:
dyn = BFGS(atoms, maxstep=0.4, logfile=logfile)
dyn.attach(traj)
dyn.run(fmax=fmax, steps=maxsteps_no_vc)
except RuntimeError:
nsteps += dyn.get_number_of_steps()
if precon:
dyn = PreconFIRE_My(atoms, precon=Exp(A=3), variable_cell=False,
use_armijo=False, logfile=logfile,
dt=0.1, maxmove=0.5, dtmax=1.0, finc=1.1)
else:
dyn = FIRE(atoms, logfile=logfile, dt=0.1, maxmove=0.5, dtmax=1.0,
finc=1.1)
dyn.attach(traj)
steps = maxsteps_no_vc - nsteps
dyn.run(fmax=fmax, steps=steps)
nsteps += dyn.get_number_of_steps()
if vc_relax:
L = atoms.get_volume() / 4. # largest cell vector length allowed
cellbounds = CellBounds(bounds={'phi':[20., 160.], 'a':[1.5, L],
'chi':[20., 160.], 'b':[1.5, L],
'psi':[20., 160.], 'c':[1.5, L]})
try:
dyn = PreconLBFGS_My(atoms, precon=Exp(A=3), variable_cell=True,
use_armijo=True, logfile=logfile,
cellbounds=cellbounds, a_min=1e-2)
dyn.e1 = None
try:
dyn._just_reset_hessian
except AttributeError:
dyn._just_reset_hessian = True
dyn.attach(traj)
steps = maxsteps - nsteps
dyn.run(fmax=0., smax=0., steps=steps)
except RuntimeError:
nsteps += dyn.get_number_of_steps()
dyn = PreconFIRE_My(atoms, precon=Exp(A=3), variable_cell=True,
use_armijo=False, logfile=logfile,
cellbounds=cellbounds,
dt=0.1, maxmove=0.5, dtmax=1.0, finc=1.1)
dyn.attach(traj)
steps = maxsteps - nsteps
dyn.run(fmax=0., steps=steps)
name = atoms.calc.name
print('%s relaxation took %.3f seconds' % (name, time()-t))
return atoms
def run_moldy(N, save = False):
#
params = {'bond':bond, 'a':a, 'h':h}
# DEFINE FILES
mdfile, mdlogfile, mdrelax = get_fileName(N, 'tear_E_rebo+KC_v', taito, v, edge)
# GRAPHENE SLAB
atoms = make_graphene_slab(a,h,width,length,N, \
edge_type = edge, h_pass = True, \
stacking = 'abc')[3]
view(atoms)
#exit()
params['ncores'] = ncores
params['positions'] = atoms.positions.copy()
params['pbc'] = atoms.get_pbc()
params['cell'] = atoms.get_cell().diagonal()
params['ia_dist'] = 10
params['chemical_symbols'] \
= atoms.get_chemical_symbols()
# FIX
constraints = []
print 'hii'
left = get_ind(atoms.positions.copy(), 'left', 2, bond)
top = get_ind(atoms.positions.copy(), 'top', fixtop - 1, left)
rend_t = get_ind(atoms.positions.copy(), 'rend', atoms.get_chemical_symbols(), 1, edge)
rend = get_ind(atoms.positions.copy(), 'rend', atoms.get_chemical_symbols(), fixtop, edge)
print rend
print 'hoo'
exit()
fix_left = FixAtoms(indices = left)
fix_top = FixAtoms(indices = top)
add_kc = KC_potential_p(params)
for ind in rend:
fix_deform = FixedPlane(ind, (0., 0., 1.))
constraints.append(fix_deform)
for ind in rend_t:
fix_deform = FixedPlane(ind, (0., 0., 1.))
constraints.append(fix_deform)
constraints.append(fix_left)
constraints.append(fix_top)
constraints.append(add_kc)
# END FIX
# CALCULATOR LAMMPS
parameters = {'pair_style':'rebo',
'pair_coeff':['* * CH.airebo C H'],
'mass' :['1 12.0', '2 1.0'],
'units' :'metal',
'boundary' :'f p f'}
calc = LAMMPS(parameters=parameters)
atoms.set_calculator(calc)
# END CALCULATOR
#view(atoms)
# TRAJECTORY
if save: traj = PickleTrajectory(mdfile, 'w', atoms)
else: traj = None
#data = np.zeros((M/interval, 5))
# RELAX
atoms.set_constraint(add_kc)
dyn = BFGS(atoms, trajectory = mdrelax)
dyn.run(fmax=0.05)
# FIX AFTER RELAXATION
atoms.set_constraint(constraints)
# DYNAMICS
dyn = Langevin(atoms, dt*units.fs, T*units.kB, fric)
n = 0
header = '#t [fs], d [Angstrom], epot_tot [eV], ekin_tot [eV], etot_tot [eV] \n'
log_f = open(mdlogfile, 'w')
log_f.write(header)
log_f.close()
if T != 0:
# put initial MaxwellBoltzmann velocity distribution
mbd(atoms, T*units.kB)
print 'Start the dynamics for N = %i' %N
for i in range(0, M):
if T == 0:
for ind in rend:
atoms[ind].position[2] -= dz
elif T != 0:
if tau < i*dt:
hw = i*dz
for ind in rend:
atoms[ind].position[2] -= dz
dyn.run(1)
if i%interval == 0:
epot, ekin = saveAndPrint(atoms, traj, False)[:2]
if T != 0:
if tau < i*dt: hw = i*dz - tau*v
else: hw = 0
else: hw = i*dz
data = [i*dt, hw, epot, ekin, epot + ekin]
if save:
log_f = open(mdlogfile, 'a')
stringi = ''
for k,d in enumerate(data):
if k == 0:
stringi += '%.2f ' %d
elif k == 1:
stringi += '%.6f ' %d
else:
stringi += '%.12f ' %d
log_f.write(stringi + '\n')
log_f.close()
n += 1
if save and T != 0 and i*dt == tau:
log_f = open(mdlogfile, 'a')
log_f.write('# Thermalization complete. ' + '\n')
log_f.close()
if 1e2 <= M:
if i%(int(M/100)) == 0: print 'ready = %.1f' %(i/(int(M/100))) + '%'
import os
from ase import Atoms
from ase.test import require
from ase.calculators.dftb import Dftb
from ase.optimize import BFGS
require('dftb')
p = os.path.dirname(__file__)
os.environ['DFTB_PREFIX'] = p if p else './'
calc = Dftb(
label='dftb',
Hamiltonian_SCC='No',
Hamiltonian_PolynomialRepulsive='SetForAll {Yes}',
)
atoms = Atoms('Si2',
positions=[[5., 5., 5.], [7., 5., 5.]],
cell=[12.] * 3,
pbc=False)
atoms.set_calculator(calc)
dyn = BFGS(atoms, logfile='-')
dyn.run(fmax=0.1)
e = atoms.get_potential_energy()
assert abs(e - -64.830901) < 1., e
def find_adhesion_potential(params):
bond = params['bond']
a = np.sqrt(3)*bond # 2.462
h = params['h']
CperArea= (a**2*np.sqrt(3)/4)**(-1)
atoms = make_graphene_slab(a,h,width,length,N, (True, True, False))[3]
# FIX
constraints = []
top = get_mask(atoms.positions.copy(), 'top', 1, h)
bottom = np.logical_not(top)
fix_bot = FixAtoms(mask = bottom)
constraints.append(fix_bot)
# END FIX
# DEF CALC AND RELAX
parameters = {'pair_style':'airebo 3.0',
'pair_coeff':['* * CH.airebo C'],
'mass' :['* 12.01'],
'units' :'metal',
'boundary' :'p p f'}
calc = LAMMPS(parameters=parameters) #, files=['lammps.data'])
atoms.set_calculator(calc)
dyn = BFGS(atoms)
dyn.run(fmax=0.05)
# SLAB IS RELAXED
atoms.set_constraint(constraints)
zmax = np.amax(atoms.positions[bottom][:,2])
natoms = 0
for i in range(len(top)):
if top[i]: natoms += 1
def get_epot(z):
new_pos = atoms.positions.copy()
for iat in range(len(atoms)):
if top[iat]:
new_pos[iat][2] = z
atoms.positions = new_pos
return atoms.get_potential_energy()/natoms
def lj(z):
ecc = 0.002843732471143
sigmacc = 3.4
return 2./5*np.pi*CperArea*ecc*(2*(sigmacc**6/z**5)**2 - 5*(sigmacc**3/z**2)**2), \
8.*ecc*CperArea*np.pi*(sigmacc**12/z**11 - sigmacc**6/z**5)
# Start to move the top layer in z direction
zrange = np.linspace(h - .7, h + 8, 100)
adh_pot = np.zeros((len(zrange), 2))
for i, z in enumerate(zrange):
adh_pot[i] = [z, get_epot(zmax + z)]
adh_pot[:,1] = adh_pot[:,1] - np.min(adh_pot[:,1])
hmin = adh_pot[np.where(adh_pot[:,1] == np.min(adh_pot[:,1]))[0][0], 0]
np.savetxt('adh_potential.gz', adh_pot, fmt='%.12f')
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(adh_pot[:,0], adh_pot[:,1], label = 'lamps')
ax.plot(zrange, lj(zrange)[0] - np.min(lj(zrange)[0]), label = 'lj')
ax.plot(zrange, lj(zrange)[1], label = 'lj')
ax.scatter(hmin, 0)
ax.set_title('Adhesion energy')
ax.set_xlabel('height, Angst')
ax.set_ylabel('Pot. E, eV')
plt.legend(frameon = False)
plt.savefig('Adhesion_energy.svg')
plt.show()
fmax = 0.05
nimages = 3
print [a.get_potential_energy() for a in PickleTrajectory('H.traj')]
images = [PickleTrajectory('H.traj')[-1]]
for i in range(nimages):
images.append(images[0].copy())
images[-1].positions[6, 1] = 2 - images[0].positions[6, 1]
neb = NEB(images)
neb.interpolate()
for image in images[1:]:
image.set_calculator(Calculator())
dyn = BFGS(neb, trajectory='mep.traj')
dyn.run(fmax=fmax)
for a in neb.images:
print a.positions[-1], a.get_potential_energy()
results = [images[2].get_potential_energy()]
def run_neb_calculation(cpu):
images = [PickleTrajectory('H.traj')[-1]]
for i in range(nimages):
images.append(images[0].copy())
images[-1].positions[6, 1] = 2 - images[0].positions[6, 1]
neb = NEB(images, parallel=True, world=cpu)
neb.interpolate()
blmin = closest_distances_generator(all_atom_types, ratio_of_covalent_radii=0.7)
comp = InteratomicDistanceComparator(n_top=n_to_optimize, pair_cor_cum_diff=0.015, pair_cor_max=0.7, dE=0.02, mic=False)
pairing = CutAndSplicePairing(slab, n_to_optimize, blmin)
mutations = OperationSelector(
[1.0, 1.0, 1.0],
[MirrorMutation(blmin, n_to_optimize), RattleMutation(blmin, n_to_optimize), PermutationMutation(n_to_optimize)],
)
# Relax all unrelaxed structures (e.g. the starting population)
while da.get_number_of_unrelaxed_candidates() > 0:
a = da.get_an_unrelaxed_candidate()
a.set_calculator(EMT())
print("Relaxing starting candidate {0}".format(a.info["confid"]))
dyn = BFGS(a, trajectory=None, logfile=None)
dyn.run(fmax=0.05, steps=100)
a.set_raw_score(-a.get_potential_energy())
da.add_relaxed_step(a)
# create the population
population = Population(data_connection=da, population_size=population_size, comparator=comp)
# test n_to_test new candidates
for i in xrange(n_to_test):
print("Now starting configuration number {0}".format(i))
a1, a2 = population.get_two_candidates()
a3, desc = pairing.get_new_individual([a1, a2])
if a3 == None:
continue
da.add_unrelaxed_candidate(a3, description=desc)
from ase.optimize import BFGS
from ase.io import read, write
initial_structure = "input.traj"
calc = Vasp2(encut=400,
ispin=2,
ediff=1.0e-5,
nelm=120,
nelmin=5,
xc='pbe',
kpts=(1, 1, 1),
gamma=True,
prec="N",
algo="N",
ismear=0,
sigma=0.1,
npar=8,
lreal="Auto",
lcharg=False,
lwave=True,
directory='calculator')
atoms = read(initial_structure)
if "initial_magmoms" in atoms.arrays.keys():
del atoms.arrays['initial_magmoms']
atoms.set_calculator(calc)
opt = BFGS(atoms, trajectory="vasp_opt.traj", logfile="vasp_opt.log")
opt.run(fmax=0.01)
write("vasp_optimized.traj", atoms)
n = size // numOfImages
#j = rank * numOfImages // size
j = (((2 * rank) + 2) // n) - 1
#calc = EMT()
for i in range(0, numOfImages): #determines the number of nodes
image = initial.copy()
if i == j:
image.set_calculator(calc)
image.set_constraint(constraint)
images.append(image)
images.append(final)
neb = NEB(images, climb=True, parallel=True)
neb.interpolate()
dyn = BFGS(neb, logfile="logFile")
#if rank % (size // ) == 0:
# traj = PickleTrajectory('neb%d.traj' % j, 'w', images[j], master=True)
# qn.attach(traj)
for i in range(0, numOfImages):
dyn.attach(PickleTrajectory('neb-%d.traj' % i, 'w', images[i]),
master=True)
dyn.run(fmax=0.014)
#writes the coordinates for each image in NEB path in .xyz format
string = 'structure'
path = os.getcwd()
path = path + '/neb-scratch'
if not os.path.exists(path): os.makedirs(path)
#constraint = FixAtoms(mask=[0,1,0,1,0]) # fix OO #Works without patch
for image in images:
image.set_calculator(Turbomole()) #BUG No.2: (Over-)writes coord file
image.set_constraint(constraint)
# Write all commands for the define command in a string
define_str = '\n\na coord\n\n*\nno\nb all 3-21g hondo\n*\neht\n\n-1\nno\ns\n*\n\ndft\non\nfunc pwlda\n\n\nscf\niter\n300\n\n*'
# Run define
p = Popen('define', stdout=PIPE, stdin=PIPE, stderr=STDOUT)
stdout = p.communicate(input=define_str)
# Relax initial and final states:
if 1:
dyn1 = QuasiNewton(images[0])
dyn1.run(fmax=0.10)
dyn2 = QuasiNewton(images[-1])
dyn2.run(fmax=0.10)
# Interpolate positions between initial and final states:
neb.interpolate()
if 1:
for image in images:
print image.get_distance(1, 2), image.get_potential_energy()
dyn = BFGS(neb, trajectory='turbomole_h3o2m.traj')
dyn.run(fmax=0.10)
for image in images:
print image.get_distance(1, 2), image.get_potential_energy()
import numpy as np
from ase.build import bulk
from ase.optimize import BFGS
from ase.io import Trajectory
from ase.constraints import StrainFilter
from gpaw import GPAW, PW
co = bulk('Co')
co.set_initial_magnetic_moments([1.6, 1.6])
co.calc = GPAW(mode=PW(700), xc='PBE', kpts=(8, 8, 4), txt='co.txt')
BFGS(StrainFilter(co)).run(0.005)
a0 = co.cell[0, 0]
c0 = co.cell[2, 2]
traj = Trajectory('co.traj', 'w')
eps = 0.01
for a in a0 * np.linspace(1 - eps, 1 + eps, 3):
for c in c0 * np.linspace(1 - eps, 1 + eps, 3):
co.set_cell(bulk('Co', a=a, covera=c / a).cell, scale_atoms=True)
co.get_potential_energy()
traj.write(co)
E.append(dimer.get_potential_energy())
F.append(dimer.get_forces())
F = np.array(F)
# plt.plot(D, E)
F1 = np.polyval(np.polyder(np.polyfit(D, E, 7)), D)
F2 = F[:, :3, 0].sum(1)
error = abs(F1 - F2).max()
dimer.constraints = FixInternals(
bonds=[(r, (0, 2)), (r, (1, 2)),
(r, (3, 5)), (r, (4, 5))],
angles=[(a, (0, 2, 1)), (a, (3, 5, 4))])
opt = BFGS(dimer,
trajectory=calc.name + '.traj', logfile=calc.name + 'd.log')
opt.run(0.01)
e0 = dimer.get_potential_energy()
d0 = dimer.get_distance(2, 5)
R = dimer.positions
v1 = R[1] - R[5]
v2 = R[5] - (R[3] + R[4]) / 2
a0 = np.arccos(np.dot(v1, v2) /
(np.dot(v1, v1) * np.dot(v2, v2))**0.5) / np.pi * 180
fmt = '{0:>20}: {1:.3f} {2:.3f} {3:.3f} {4:.1f}'
print(fmt.format(calc.name, -min(E), -e0, d0, a0))
assert abs(e0 + eexp) < 0.002
assert abs(d0 - dexp) < 0.006
assert abs(a0 - aexp) < 2
def test_h2o():
from ase.calculators.demonnano import DemonNano
from ase import Atoms
from ase.optimize import BFGS
from ase.units import Bohr, Hartree
import numpy as np
d = 0.9775
t = np.pi / 180 * 110.51
atoms = Atoms('H2O',
positions=[(d, 0, 0), (d * np.cos(t), d * np.sin(t), 0),
(0, 0, 0)])
input_arguments = {'DFTB': 'SCC', 'CHARGE': '0.0', 'PARAM': 'PTYPE=MAT'}
calc = DemonNano(input_arguments=input_arguments)
atoms.calc = calc
# energy
energy = atoms.get_potential_energy()
ref = -4.08209 * Hartree
print('energy')
print(energy)
error = np.sqrt(np.sum((energy - ref)**2))
print('diff from reference:')
print(error)
tol = 1.0e-6
assert (error < tol)
# analytical forces
forces_an = atoms.get_forces()
ref = np.array([[0.11381E-01, -0.16761E-01, 0.0],
[-0.19688E-01, 0.47899E-02, 0.0],
[0.83062E-02, 0.11971E-01, 0.0]])
ref *= -Hartree / Bohr
error = np.sqrt(np.sum((forces_an - ref)**2))
print('forces_an')
print(forces_an)
print('diff from reference:')
print(error)
tol = 1.0e-3
assert (error < tol)
# optimize geometry
with BFGS(atoms) as dyn:
dyn.run(fmax=0.01)
positions = atoms.get_positions()
ref = np.array([[0.943765, 0.046188, 0.0], [-0.287409, 0.900126, 0.0],
[-0.021346, -0.030774, 0.0]])
error = np.sqrt(np.sum((positions - ref)**2))
print('positions')
print(positions)
print('diff from reference:')
print(error)
tol = 1.0e-3
assert (error < tol)
print('tests passed')
for i in range(3):
ranks = range(i * n, (i + 1) * n)
image = initial.copy()
if rank in ranks:
calc = GPAW(h=0.3,
kpts=(2, 2, 1),
txt='neb%d.txt' % j,
communicator=ranks)
image.set_calculator(calc)
image.set_constraint(constraint)
images.append(image)
images.append(final)
neb = NEB(images, parallel=True)
neb.interpolate()
qn = BFGS(neb, logfile='qn.log')
traj = PickleTrajectory('neb%d.traj' % j, 'w', images[j],
master=(rank % n == 0))
qn.attach(traj)
qn.run(fmax=0.05)
dE=0.02,
mic=False)
pairing = CutAndSplicePairing(slab, n_to_optimize, blmin)
mutations = OperationSelector([1., 1., 1.], [
MirrorMutation(blmin, n_to_optimize),
RattleMutation(blmin, n_to_optimize),
PermutationMutation(n_to_optimize)
])
# Relax all unrelaxed structures (e.g. the starting population)
while da.get_number_of_unrelaxed_candidates() > 0:
a = da.get_an_unrelaxed_candidate()
a.set_calculator(EMT())
print('Relaxing starting candidate {0}'.format(a.info['confid']))
dyn = BFGS(a, trajectory=None, logfile=None)
dyn.run(fmax=0.05, steps=100)
a.info['key_value_pairs']['raw_score'] = -a.get_potential_energy()
da.add_relaxed_step(a)
# create the population
population = Population(data_connection=da,
population_size=population_size,
comparator=comp)
# test n_to_test new candidates
for i in range(n_to_test):
print('Now starting configuration number {0}'.format(i))
a1, a2 = population.get_two_candidates()
a3, desc = pairing.get_new_individual([a1, a2])
if a3 is None:
2.A. NEB optimization using CI-NEB as implemented in ASE.
2.B. NEB optimization using our machine-learning surrogate model.
3. Comparison between the ASE NEB and our ML-NEB algorithm.
"""
# Define number of images:
n_images = 11
# 1. Structural relaxation. ##################################################
# Setup calculator:
ase_calculator = EMT()
#
slab = read('initial.traj')
slab.set_calculator(copy.deepcopy(ase_calculator))
qn = BFGS(slab, trajectory='initial_opt.traj')
qn.run(fmax=0.01)
# Final end-point:
slab = read('final.traj')
slab.set_calculator(copy.deepcopy(ase_calculator))
qn = BFGS(slab, trajectory='final_opt.traj')
qn.run(fmax=0.01)
# 2.A. NEB using ASE #########################################################
initial_ase = read('initial_opt.traj')
final_ase = read('final_opt.traj')
constraint = FixAtoms(mask=[atom.tag > 1 for atom in initial_ase])
images_ase = [initial_ase]
def run(gl):
#Read reactant definition
if gl.StartType == 'file':
Reac = read(gl.Start)
elif gl.StartType == 'Smile':
Reac = tl.getMolFromSmile(gl.Start)
#Read product definition
if gl.EndType == 'file':
Prod= read(gl.End)
elif gl.EndType == 'Smile':
Prod = tl.getMolFromSmile(gl.End)
#Set calculatiors
#Reac = tl.setCalc(Reac,"DOS/", gl.trajMethod, gl.atomTypes)
if gl.trajMethod == "openMM":
Reac = tl.setCalc(Reac,"GenBXD/", gl.trajMethod, gl)
else:
Reac = tl.setCalc(Reac,"GenBXD/", gl.trajMethod, gl.trajLevel)
Prod = tl.setCalc(Prod,"GenBXD/", gl.trajMethod, gl.trajLevel)
# Partially minimise both reactant and product
if gl.GenBXDrelax:
min = BFGS(Reac)
try:
min.run(fmax=0.1, steps=20)
except:
min.run(fmax=0.1, steps=20)
min2 = BFGS(Prod)
try:
min2.run(fmax=0.1, steps=20)
except:
min2.run(fmax=0.1, steps=20)
# Get important interatomic distances
if gl.CollectiveVarType == "changedBonds":
cbs = ct.getChangedBonds2(Reac, Prod)
elif gl.CollectiveVarType == "all":
cbs = ct.getChangedBonds2(Reac, Prod)
elif gl.CollectiveVarType == "specified":
cbs = gl.CollectiveVar
elif gl.CollectiveVarType == "file":
cbs = gl.principalCoordinates
#Get path to project along
distPath = []
totalPathLength = 0
if gl.PathType == 'curve' or gl.PathType == 'gates':
if gl.PathFile == 'none':
Path = getPath(Reac,Prod,gl)
else:
Path = read(gl.PathFile,index=('::'+str(gl.pathStride)))
distPath.append((ct.getDistMatrix(Path[0],cbs)[0],0))
for i in range(1,len(Path)):
l = np.linalg.norm(ct.getDistMatrix(Path[i],cbs)[0] - ct.getDistMatrix(Path[i-1], cbs)[0])
totalPathLength += l
distPath.append((ct.getDistMatrix(Path[i],cbs)[0],totalPathLength))
elif gl.PathType == 'linear':
distPath = ct.getDistMatrix(Prod,cbs)[0] - ct.getDistMatrix(Reac, cbs)[0]
if gl.PathType == 'curve' or gl.PathType == 'gates':
pathFile = open('reducedPath.txt','w')
for p in distPath:
pathFile.write('s = ' + str(p[0]) + '\n')
pathFile.close()
# initialise then run trajectory
t = Trajectory.Trajectory(Reac,gl,os.getcwd(),0,False)
t.runGenBXD(Reac,Prod,gl.maxHits,gl.maxAdapSteps,gl.PathType,distPath, cbs, gl.decorrelationSteps, gl.histogramBins,totalPathLength, gl.fixToPath, gl.pathDistCutOff,gl.epsilon)
def eval_energy(input):
"""Function to evaluate energy of an individual
Inputs:
input = [Optimizer class object with parameters, Individual class structure to be evaluated]
Outputs:
energy, bul, individ, signal
energy = energy of Individual evaluated
bul = bulk structure of Individual if simulation structure is Defect
individ = Individual class structure evaluated
signal = string of information about evaluation
"""
if input[0]==None:
energy=0
bul=0
individ=0
rank = MPI.COMM_WORLD.Get_rank()
signal='Evaluated none individual on '+repr(rank)+'\n'
else:
[Optimizer, individ]=input
if Optimizer.calc_method=='MAST':
energy = individ.energy
bul = individ.energy
signal = 'Recieved MAST structure\n'
else:
if Optimizer.parallel: rank = MPI.COMM_WORLD.Get_rank()
if not Optimizer.genealogy:
STR='----Individual ' + str(individ.index)+ ' Optimization----\n'
else:
STR='----Individual ' + str(individ.history_index)+ ' Optimization----\n'
indiv=individ[0]
if 'EE' in Optimizer.debug:
debug = True
else:
debug = False
if debug:
write_xyz(Optimizer.debugfile,indiv,'Recieved by eval_energy')
Optimizer.debugfile.flush()
if Optimizer.structure=='Defect':
indi=indiv.copy()
if Optimizer.alloy==True:
bulk=individ.bulki
else:
bulk=individ.bulko
nat=indi.get_number_of_atoms()
csize=bulk.get_cell()
totalsol=Atoms(cell=csize, pbc=True)
totalsol.extend(indi)
totalsol.extend(bulk)
for sym,c,m,u in Optimizer.atomlist:
nc=len([atm for atm in totalsol if atm.symbol==sym])
STR+='Defect configuration contains '+repr(nc)+' '+repr(sym)+' atoms\n'
elif Optimizer.structure=='Surface':
totalsol=Atoms()
totalsol.extend(indiv)
nat=indiv.get_number_of_atoms()
totalsol.extend(individ.bulki)
for sym,c,m,u in Optimizer.atomlist:
nc=len([atm for atm in totalsol if atm.symbol==sym])
STR+='Surface-Bulk configuration contains '+repr(nc)+' '+repr(sym)+' atoms\n'
cell=numpy.maximum.reduce(indiv.get_cell())
totalsol.set_cell([cell[0],cell[1],500])
totalsol.set_pbc([True,True,False])
if Optimizer.constrain_position:
ts = totalsol.copy()
indc,indb,vacant,swap,stro = find_defects(ts,Optimizer.solidbulk,0)
sbulk = Optimizer.solidbulk.copy()
bcom = sbulk.get_center_of_mass()
#totalsol.translate(-bulkcom)
#indc.translate(-bulkcom)
#totalsol.append(Atom(position=[0,0,0]))
# for one in indc:
# index = [atm.index for atm in totalsol if atm.position[0]==one.position[0] and atm.position[1]==one.position[1] and atm.position[2]==one.position[2]][0]
# if totalsol.get_distance(-1,index) > Optimizer.sf:
# r = random.random()
# totalsol.set_distance(-1,index,Optimizer.sf*r,fix=0)
# totalsol.pop()
# totalsol.translate(bulkcom)
com = indc.get_center_of_mass()
dist = (sum((bcom[i] - com[i])**2 for i in range(3)))**0.5
if dist > Optimizer.sf:
STR+='Shifting structure to within region\n'
r = random.random()*Optimizer.sf
comv = numpy.linalg.norm(com)
ncom = [one*r/comv for one in com]
trans = [ncom[i]-com[i] for i in range(3)]
indices = []
for one in indc:
id = [atm.index for atm in totalsol if atm.position[0]==one.position[0] and atm.position[1]==one.position[1] and atm.position[2]==one.position[2]][0]
totalsol[id].position += trans
# Check for atoms that are too close
min_len=0.7
#pdb.set_trace()
if not Optimizer.fixed_region:
if Optimizer.structure=='Defect' or Optimizer.structure=='Surface':
cutoffs=[2.0 for one in totalsol]
nl=NeighborList(cutoffs,bothways=True,self_interaction=False)
nl.update(totalsol)
for one in totalsol[0:nat]:
nbatoms=Atoms()
nbatoms.append(one)
indices, offsets=nl.get_neighbors(one.index)
for index, d in zip(indices,offsets):
index = int(index)
sym=totalsol[index].symbol
pos=totalsol[index].position + numpy.dot(d,totalsol.get_cell())
at=Atom(symbol=sym,position=pos)
nbatoms.append(at)
while True:
dflag=False
for i in range(1,len(nbatoms)):
d=nbatoms.get_distance(0,i)
if d < min_len:
nbatoms.set_distance(0,i,min_len+.01,fix=0.5)
STR+='--- WARNING: Atoms too close (<0.7A) - Implement Move ---\n'
dflag=True
if dflag==False:
break
for i in range(len(indices)):
totalsol[indices[i]].position=nbatoms[i+1].position
totalsol[one.index].position=nbatoms[0].position
nl.update(totalsol)
if debug:
write_xyz(Optimizer.debugfile,totalsol,'After minlength check')
Optimizer.debugfile.flush()
else:
for i in range(len(indiv)):
for j in range(len(indiv)):
if i != j:
d=indiv.get_distance(i,j)
if d < min_len:
indiv.set_distance(i,j,min_len,fix=0.5)
STR+='--- WARNING: Atoms too close (<0.7A) - Implement Move ---\n'
if debug:
write_xyz(Optimizer.debugfile,indiv,'After minlength check')
Optimizer.debugfile.flush()
# Set calculator to use to get forces/energies
if Optimizer.parallel:
calc = setup_calculator(Optimizer)
if Optimizer.fixed_region:
pms=copy.deepcopy(calc.parameters)
try:
pms['mass'][len(pms['mass'])-1] += '\ngroup RO id >= '+repr(nat)+'\nfix freeze RO setforce 0.0 0.0 0.0\n'
except KeyError:
pms['pair_coeff'][0] += '\ngroup RO id >= '+repr(nat)+'\nfix freeze RO setforce 0.0 0.0 0.0\n'
calc = LAMMPS(parameters=pms, files=calc.files, keep_tmp_files=calc.keep_tmp_files, tmp_dir=calc.tmp_dir)
lmin = copy.copy(Optimizer.lammps_min)
Optimizer.lammps_min = None
Optimizer.static_calc = setup_calculator(Optimizer)
Optimizer.lammps_min = lmin
else:
calc=Optimizer.calc
if Optimizer.structure=='Defect' or Optimizer.structure=='Surface':
totalsol.set_calculator(calc)
totalsol.set_pbc(True)
else:
indiv.set_calculator(calc)
indiv.set_pbc(True) #Current bug in ASE optimizer-Lammps prevents pbc=false
if Optimizer.structure=='Cluster':
indiv.set_cell([500,500,500])
indiv.translate([250,250,250])
cwd=os.getcwd()
# Perform Energy Minimization
if not Optimizer.parallel:
Optimizer.output.flush()
if Optimizer.ase_min == True:
try:
if Optimizer.structure=='Defect' or Optimizer.structure=='Surface':
dyn=BFGS(totalsol)
else:
dyn=BFGS(indiv)
dyn.run(fmax=Optimizer.ase_min_fmax, steps=Optimizer.ase_min_maxsteps)
except OverflowError:
STR+='--- Error: Infinite Energy Calculated - Implement Random ---\n'
box=Atoms()
indiv=gen_pop_box(Optimizer.natoms, Optimizer.atomlist, Optimizer.size)
indiv.set_calculator(calc)
dyn=BFGS(indiv)
dyn.run(fmax=fmax, steps=steps)
except numpy.linalg.linalg.LinAlgError:
STR+='--- Error: Singular Matrix - Implement Random ---\n'
indiv=gen_pop_box(Optimizer.natoms, Optimizer.atomlist, Optimizer.size)
indiv.set_calculator(calc)
dyn=BFGS(indiv)
dyn.run(fmax=fmax, steps=steps)
# Get Energy of Minimized Structure
if Optimizer.structure=='Defect' or Optimizer.structure=='Surface':
en=totalsol.get_potential_energy()
#force=numpy.maximum.reduce(abs(totalsol.get_forces()))
if Optimizer.fitness_scheme == 'enthalpyfit':
pressure=totalsol.get_isotropic_pressure(totalsol.get_stress())
cell_max=numpy.maximum.reduce(totalsol.get_positions())
cell_min=numpy.minimum.reduce(totalsol.get_positions())
cell=cell_max-cell_min
volume=cell[0]*cell[1]*cell[2]
else:
pressure=0
volume=0
na=totalsol.get_number_of_atoms()
ena=en/na
energy=en
individ[0]=totalsol[0:nat]
bul=totalsol[(nat):len(totalsol)]
STR+='Number of positions = '+repr(len(bul)+len(individ[0]))+'\n'
individ[0].set_cell(csize)
indiv=individ[0]
else:
en=indiv.get_potential_energy()
if Optimizer.fitness_scheme == 'enthalpyfit':
pressure=indiv.get_isotropic_pressure(indiv.get_stress())
cell_max=numpy.maximum.reduce(indiv.get_positions())
cell_min=numpy.minimum.reduce(indiv.get_positions())
cell=cell_max-cell_min
volume=cell[0]*cell[1]*cell[2]
else:
pressure=0
volume=0
na=indiv.get_number_of_atoms()
ena=en/na
energy=ena
individ[0]=indiv
bul=0
else:
if Optimizer.structure=='Defect' or Optimizer.structure=='Surface':
if Optimizer.calc_method=='VASP':
en=totalsol.get_potential_energy()
calcb=Vasp(restart=True)
totalsol=calcb.get_atoms()
stress=calcb.read_stress()
else:
try:
totcop=totalsol.copy()
if debug: write_xyz(Optimizer.debugfile,totcop,'Individual sent to lammps')
OUT=totalsol.calc.calculate(totalsol)
totalsol=OUT['atoms']
totalsol.set_pbc(True)
if Optimizer.fixed_region:
if debug:
print 'Energy of fixed region calc = ', OUT['thermo'][-1]['pe']
totalsol.set_calculator(Optimizer.static_calc)
OUT=totalsol.calc.calculate(totalsol)
totalsol=OUT['atoms']
totalsol.set_pbc(True)
if debug:
print 'Energy of static calc = ', OUT['thermo'][-1]['pe']
en=OUT['thermo'][-1]['pe']
stress=numpy.array([OUT['thermo'][-1][i] for i in ('pxx','pyy','pzz','pyz','pxz','pxy')])*(-1e-4*GPa)
#force=numpy.maximum.reduce(abs(totalsol.get_forces()))
if debug:
write_xyz(Optimizer.debugfile,totalsol,'After Lammps Minimization')
Optimizer.debugfile.flush()
except Exception, e:
os.chdir(cwd)
STR+='WARNING: Exception during energy eval:\n'+repr(e)+'\n'
f=open('problem-structures.xyz','a')
write_xyz(f,totcop,data='Starting structure hindex='+individ.history_index)
write_xyz(f,totalsol,data='Lammps Min structure')
en=10
stress=0
f.close()
if Optimizer.fitness_scheme == 'enthalpyfit':
pressure=totalsol.get_isotropic_pressure(stress)
cell_max=numpy.maximum.reduce(totalsol.get_positions())
cell_min=numpy.minimum.reduce(totalsol.get_positions())
cell=cell_max-cell_min
volume=cell[0]*cell[1]*cell[2]
else:
pressure=totalsol.get_isotropic_pressure(stress)
volume=0
na=totalsol.get_number_of_atoms()
ena=en/na
energy=en
if Optimizer.structure=='Defect':
if Optimizer.fixed_region==True or Optimizer.finddefects==False:
individ[0]=totalsol[0:nat]
bul=totalsol[(nat):len(totalsol)]
individ[0].set_cell(csize)
else:
if 'FI' in Optimizer.debug:
outt=find_defects(totalsol,Optimizer.solidbulk,Optimizer.sf,atomlistcheck=Optimizer.atomlist,trackvacs=Optimizer.trackvacs,trackswaps=Optimizer.trackswaps,debug=Optimizer.debugfile)
else:
outt=find_defects(totalsol,Optimizer.solidbulk,Optimizer.sf,atomlistcheck=Optimizer.atomlist,trackvacs=Optimizer.trackvacs,trackswaps=Optimizer.trackswaps,debug=False)
individ[0]=outt[0]
bul=outt[1]
individ.vacancies = outt[2]
individ.swaps = outt[3]
STR += outt[4]
indiv=individ[0]
else:
top,bul=find_top_layer(totalsol,Optimizer.surftopthick)
indiv=top.copy()
individ[0]=top.copy()
else:
import sys
import pathlib
current_dir = pathlib.Path(__file__).resolve().parent
sys.path.append( str(current_dir) + '/../' )
from aseInterface import *
from ase.build import bulk, make_supercell
from ase.optimize import BFGS
hdnnpy=hdnnpy()
hdnnpy.set_label('./Crystal')
a1 = bulk('Si', 'diamond', a=3.6)
super=make_supercell(a1,[[2,1,1],[1,2,1],[1,1,2]])
super.set_calculator(hdnnpy)
e=super.get_potential_energy()
print(e)
f=super.get_forces()
print(f)
dyn=BFGS(super)
dyn.run(fmax=0.05)
#!/usr/bin/env python
from ase import Atom, Atoms
from ase.structure import molecule
from ase.calculators.cp2k import CP2K
from ase.optimize import BFGS
from ase.vibrations import Vibrations
from multiprocessing import Pool
import os
from time import time
atoms = molecule('H2O')
atoms.center(vacuum=5.0)
start = time()
calc = CP2K(label = 'molecules/h2o',
xc='PBE')
atoms.set_calculator(calc)
gopt = BFGS(atoms, logfile=None)
gopt.run(fmax=1e-2)
end = time()
e = atoms.get_potential_energy()
pos = atoms.get_positions()
d = ((pos[0] - pos[1])**2).sum()**0.5
print('*===============================*')
print('{0:1.4f} {1:1.4f} '.format( e, d))
print('time = {0}'.format(end - start))
# Make a mask of zeros and ones that select fixed atoms (the
# two bottom layers):
mask = initial.positions[:, 2] - min(initial.positions[:, 2]) < 1.5 * h
constraint = FixAtoms(mask=mask)
print(mask)
for image in images:
# Let all images use an EMT calculator:
image.set_calculator(EMT())
image.set_constraint(constraint)
# Relax the initial and final states:
QuasiNewton(initial).run(fmax=0.05)
QuasiNewton(final).run(fmax=0.05)
# Create a Nudged Elastic Band:
neb = NEB(images)
# Make a starting guess for the minimum energy path (a straight line
# from the initial to the final state):
neb.interpolate()
# Relax the NEB path:
minimizer = BFGS(neb)
minimizer.run(fmax=0.05)
# Write the path to a trajectory:
view(images) # 126 meV
write('jump1.traj', images)
from ase.calculators.emt import EMT
from ase.optimize import BFGS
from ase.constraints import FixCom
from ase.build import molecule
atoms = molecule('H2O')
atoms.center(vacuum=4)
atoms.set_calculator(EMT())
cold = atoms.get_center_of_mass()
atoms.set_constraint(FixCom())
opt = BFGS(atoms)
opt.run(steps=5)
cnew = atoms.get_center_of_mass()
assert max(abs(cnew - cold)) < 1e-8
def test_qmmm_tip4p():
from math import cos, sin, pi
import numpy as np
#import matplotlib.pyplot as plt
import ase.units as units
from ase import Atoms
from ase.calculators.tip4p import TIP4P, epsilon0, sigma0, rOH, angleHOH
from ase.calculators.qmmm import (SimpleQMMM, EIQMMM, LJInteractions,
LJInteractionsGeneral)
from ase.constraints import FixInternals
from ase.optimize import BFGS
r = rOH
a = angleHOH * pi / 180
# From https://doi.org/10.1063/1.445869
eexp = 6.24 * units.kcal / units.mol
dexp = 2.75
aexp = 46
D = np.linspace(2.5, 3.5, 30)
i = LJInteractions({('O', 'O'): (epsilon0, sigma0)})
# General LJ interaction object
sigma_mm = np.array([sigma0, 0, 0])
epsilon_mm = np.array([epsilon0, 0, 0])
sigma_qm = np.array([sigma0, 0, 0])
epsilon_qm = np.array([epsilon0, 0, 0])
ig = LJInteractionsGeneral(sigma_qm, epsilon_qm, sigma_mm, epsilon_mm, 3)
for calc in [
TIP4P(),
SimpleQMMM([0, 1, 2], TIP4P(), TIP4P(), TIP4P()),
SimpleQMMM([0, 1, 2], TIP4P(), TIP4P(), TIP4P(), vacuum=3.0),
EIQMMM([0, 1, 2], TIP4P(), TIP4P(), i),
EIQMMM([3, 4, 5], TIP4P(), TIP4P(), i, vacuum=3.0),
EIQMMM([0, 1, 2], TIP4P(), TIP4P(), i, vacuum=3.0),
EIQMMM([0, 1, 2], TIP4P(), TIP4P(), ig),
EIQMMM([3, 4, 5], TIP4P(), TIP4P(), ig, vacuum=3.0),
EIQMMM([0, 1, 2], TIP4P(), TIP4P(), ig, vacuum=3.0)
]:
dimer = Atoms('OH2OH2', [(0, 0, 0), (r * cos(a), 0, r * sin(a)),
(r, 0, 0), (0, 0, 0),
(r * cos(a / 2), r * sin(a / 2), 0),
(r * cos(a / 2), -r * sin(a / 2), 0)])
dimer.calc = calc
E = []
F = []
for d in D:
dimer.positions[3:, 0] += d - dimer.positions[3, 0]
E.append(dimer.get_potential_energy())
F.append(dimer.get_forces())
F = np.array(F)
#plt.plot(D, E)
F1 = np.polyval(np.polyder(np.polyfit(D, E, 7)), D)
F2 = F[:, :3, 0].sum(1)
error = abs(F1 - F2).max()
assert error < 0.01
dimer.constraints = FixInternals(bonds=[(r, (0, 1)), (r, (0, 2)),
(r, (3, 4)), (r, (3, 5))],
angles=[(a, (2, 0, 1)),
(a, (5, 3, 4))])
opt = BFGS(dimer,
maxstep=0.04,
trajectory=calc.name + '.traj',
logfile=calc.name + 'd.log')
opt.run(0.01)
e0 = dimer.get_potential_energy()
d0 = dimer.get_distance(0, 3)
R = dimer.positions
v1 = R[2] - R[3]
v2 = R[3] - (R[5] + R[4]) / 2
a0 = np.arccos(
np.dot(v1, v2) /
(np.dot(v1, v1) * np.dot(v2, v2))**0.5) / np.pi * 180
fmt = '{0:>23}: {1:.3f} {2:.3f} {3:.3f} {4:.1f}'
print(fmt.format(calc.name, -min(E), -e0, d0, a0))
assert abs(e0 + eexp) < 0.002
assert abs(d0 - dexp) < 0.006
assert abs(a0 - aexp) < 2.5
print(fmt.format('reference', 9.999, eexp, dexp, aexp))
from math import sqrt, pi
from ase import Atoms
from ase.calculators.emt import EMT
from ase.constraints import FixBondLengths
from ase.optimize import BFGS, QuasiNewton
from ase.neb import SingleCalculatorNEB
from ase.lattice.surface import fcc111, add_adsorbate
from math import sqrt, cos, sin
zpos = cos(134.3 / 2.0 * pi / 180.0) * 1.197
xpos = sin(134.3 / 2.0 * pi / 180.0) * 1.19
co2 = Atoms('COO',
positions=[(-xpos + 1.2, 0, -zpos), (-xpos + 1.2, -1.1, -zpos),
(-xpos + 1.2, 1.1, -zpos)])
slab = fcc111('Au', size=(2, 2, 4), vacuum=2 * 5, orthogonal=True)
slab.center()
add_adsorbate(slab, co2, 1.5, 'bridge')
slab.set_pbc((True, True, False))
d0 = co2.get_distance(-3, -2)
d1 = co2.get_distance(-3, -1)
calc = EMT()
slab.set_calculator(calc)
constraint = FixBondLengths([[-3, -2], [-3, -1]])
slab.set_constraint(constraint)
dyn = BFGS(slab, trajectory='relax.traj')
dyn.run(fmax=0.05)
assert abs(co2.get_distance(-3, -2) - d0) < 1e-14
assert abs(co2.get_distance(-3, -1) - d1) < 1e-14
params['pair_coeff'] = ['1 1 {}'.format(pot_fn)]
calc = LAMMPS(specorder=['Pt'], files=[pot_fn], **params)
rng = np.random.RandomState(17)
atoms = bulk('Pt') * (2, 2, 2)
atoms.rattle(stdev=0.1)
atoms.cell += 2 * rng.rand(3, 3)
atoms.calc = calc
assert_allclose(atoms.get_stress(),
calc.calculate_numerical_stress(atoms),
atol=1e-4,
rtol=1e-4)
opt = BFGS(ExpCellFilter(atoms), trajectory='opt.traj')
for i, _ in enumerate(opt.irun(fmax=0.05)):
pass
cell1_ref = np.array([[0.16298762, 3.89912471, 3.92825365],
[4.21007577, 0.63362427, 5.04668170],
[4.42895706, 3.29171414, 0.44623618]])
assert_allclose(np.asarray(atoms.cell), cell1_ref, atol=1e-4, rtol=1e-4)
assert_allclose(atoms.get_stress(),
calc.calculate_numerical_stress(atoms),
atol=1e-4,
rtol=1e-4)
assert i < 80, 'Expected 59 iterations, got many more: {}'.format(i)
scale_atoms=True)
a *= (1, 2, 3)
cell0 *= np.array([1, 2, 3])[:, np.newaxis]
a.rattle()
# Verify analytical stress tensor against numerical value
s_analytical = a.get_stress()
s_numerical = a.calc.calculate_numerical_stress(a, 1e-5)
s_p_err = 100 * (s_numerical - s_analytical) / s_numerical
print("Analytical stress:\n", s_analytical)
print("Numerical stress:\n", s_numerical)
print("Percent error in stress:\n", s_p_err)
assert np.all(abs(s_p_err) < 1e-5)
# Minimize unit cell
opt = BFGS(UnitCellFilter(a))
opt.run(fmax=1e-3)
# Verify minimized unit cell using Niggli tensors
g_minimized = np.dot(a.cell, a.cell.T)
g_theory = np.dot(cell0, cell0.T)
g_p_err = 100 * (g_minimized - g_theory) / g_theory
print("Minimized Niggli tensor:\n", g_minimized)
print("Theoretical Niggli tensor:\n", g_theory)
print("Percent error in Niggli tensor:\n", g_p_err)
assert np.all(abs(g_p_err) < 1)
from ase.build import fcc111, add_adsorbate
slab = fcc111('Al', size=(2, 2, 3))
add_adsorbate(slab, 'Al', 2, 'hcp')
slab.center(vacuum=5.0, axis=2)
#view and set calculator
from ase.visualize import view
view(slab, viewer='x3d')
slab.set_calculator(zhou)
slab.get_potential_energy()
print(slab.get_calculator())
print(slab.get_potential_energy())
#relax structure
from ase.optimize import BFGS
dyn = BFGS(slab)
dyn.run(fmax=0.0001)
#make second endpoint structure, add adatom and relax
slab_2 = fcc111('Al', size=(2, 2, 3))
add_adsorbate(slab_2, 'Al', 2, 'fcc')
slab_2.center(vacuum=5.0, axis=2)
slab_2.set_calculator(EAM(potential=pot_file))
view(slab_2, viewer='x3d')
dyn = BFGS(slab_2)
slab_2.get_potential_energy()
print(slab_2.get_potential_energy())
dyn.run(fmax=0.0001)
#import NEB
def characteriseMinExt(self, mol, high):
# Low level optimisation with BFGS
os.chdir((self.workingDir))
mol = tl.setCalc(mol, self.lowString, self.lowMeth, self.lowLev)
min = BFGS(mol)
try:
min.run(fmax=0.1, steps=100)
except:
min.run(fmax=0.1, steps=100)
if high:
if self.highMeth == "gauss":
mol, freqs, zpe = tl.getGausOut(
self.workingDir + '/Raw/calcHigh' + self.procNum,
self.highLev, mol)
os.chdir((self.workingDir))
else:
# Higher level optimisation via some external program
mol = tl.setCalc(mol, self.highString, self.highMeth + 'Opt',
self.highLev)
try:
mol.get_forces()
except:
pass
mol = tl.getOptGeom(
self.workingDir + '/' + 'Raw/calcHigh' + self.procNum +
'/', 'none', self.Reac, self.highMeth)
# Then calculate frequencies
os.chdir((self.workingDir + '/Raw/' + self.procNum))
mol = tl.setCalc(mol, self.highString, self.highMeth + 'Freq',
self.highLev)
try:
mol.get_forces()
except:
pass
freqs, zpe = tl.getFreqs(
self.workingDir + '/Raw/' + self.procNum +
'/Raw/calcHigh' + self.procNum, self.highMeth)
os.chdir((self.workingDir))
else:
if self.lowMeth == "gauss":
mol, freqs, zpe = tl.getGausOut(
self.workingDir + '/Raw/calcLow' + self.procNum,
self.lowLev, mol)
os.chdir((self.workingDir))
else:
# Higher level optimisation via some external program
mol = tl.setCalc(mol, self.lowString, self.lowMeth + 'Opt',
self.lowLev)
try:
mol.get_forces()
except:
pass
mol = tl.getOptGeom(
self.workingDir + '/Raw/' + 'calcLow' + self.procNum + '/',
'none', self.Reac, self.lowMeth)
# Then calculate frequencies
os.chdir((self.workingDir + '/Raw/' + self.procNum))
mol = tl.setCalc(mol, self.lowString, self.lowMeth + 'Freq',
self.lowLev)
try:
mol.get_forces()
except:
pass
freqs, zpe = tl.getFreqs(
self.workingDir + '/Raw/' + self.procNum + '/Raw/calcLow' +
self.procNum, self.lowMeth)
os.chdir((self.workingDir))
# Finally get single point energy
mol = tl.setCalc(mol, self.singleString, self.singleMeth,
self.singleLev)
energy = mol.get_potential_energy() + zpe
return freqs, energy, mol
print([a.get_potential_energy() for a in Trajectory('H.traj')])
images = [Trajectory('H.traj')[-1]]
for i in range(nimages):
images.append(images[0].copy())
images[-1].positions[6, 1] = 2 - images[0].positions[6, 1]
neb = NEB(images)
neb.interpolate()
if 0: # verify that initial images make sense
from ase.visualize import view
view(neb.images)
for image in images:
image.set_calculator(MorsePotential())
dyn = BFGS(neb, trajectory='mep.traj') # , logfile='mep.log')
dyn.run(fmax=fmax)
for a in neb.images:
print(a.positions[-1], a.get_potential_energy())
neb.climb = True
dyn.run(fmax=fmax)
# Check NEB tools.
nt_images = read('mep.traj@-4:')
nebtools = NEBTools(nt_images)
nt_fmax = nebtools.get_fmax(climb=True)
Ef, dE = nebtools.get_barrier()
print(Ef, dE, fmax, nt_fmax)
def optDynPath(self, trans, path, MolList, changePoints):
# Open files for saving IRCdata
xyzfile = open((path + "/Data/dynPath.xyz"), "w")
MEP = open((path + "/Data/MEP.txt"), "w")
dyn = open((path + "/Data/traj.xyz"), "w")
dynList = []
end = int(changePoints + self.dynPrintStart)
length = int(len(MolList))
if end < length:
endFrame = end
else:
endFrame = length
for i in range(int(changePoints - self.dynPrintStart), int(endFrame),
self.inc):
iMol = MolList[i].copy()
tl.printTraj(dyn, iMol)
iMol = tl.setCalc(iMol, self.lowString, self.lowMeth, self.lowLev)
c = FixAtoms(trans)
iMol.set_constraint(c)
min = BFGS(iMol)
try:
min.run(fmax=0.1, steps=50)
except:
min.run(fmax=0.1, steps=1)
del iMol.constraints
tl.printTraj(xyzfile, iMol)
dynList.append(iMol.copy())
maxEne = -50000
point = 0
for i in range(0, len(dynList)):
dynList[i] = tl.setCalc(dynList[i], self.lowString, self.lowMeth,
self.lowLev)
MEP.write(
str(i) + ' ' + str(dynList[i].get_potential_energy()) + '\n')
if dynList[i].get_potential_energy() > maxEne:
point = i
maxEne = dynList[i].get_potential_energy()
self.TS2 = dynList[point]
TS2Guess = self.TS2.copy()
write(path + '/Data/TS2guess.xyz', self.TS2)
try:
self.TS2Freqs, self.imaginaryFreq2, zpe, energy, self.TS2, rmol, pmol = self.characteriseTSExt(
self.TS2, True, path, self.QTS3)
except:
try:
self.TS2Freqs, self.imaginaryFreq2, zpe, energy, self.TS2, rmol, pmol = self.characteriseTSExt(
self.TS2, False, path, self.QTS3)
except:
pass
try:
self.TS2correct = self.compareRandP(rmol, pmol)
except:
print("TS2 does not connect products")
self.TS2correct = False
self.TS2 = TS2Guess
self.TS2Freqs, self.imaginaryFreq2, zpe, energy = self.characteriseTSinternal(
self.TS2)
write(path + '/TS2.xyz', self.TS2)
self.forwardBarrier2 = energy
if rank in ranks:
calc = GPAW(mode=PW(500),
xc='PBE',
basis='dzp',
maxiter=99,
kpts=(2, 2, 1),
txt='neb{}.txt'.format(j),
communicator=ranks)
image.set_calculator(calc)
images.append(image)
images.append(final)
# Initialize neb object
neb = NEB(images, k=0.5, parallel=True, method='eb', climb=False)
#neb.interpolate()
# Converge path roughly before invoking the CI method
qn = BFGS(neb, trajectory='neb_rough.traj')
qn.run(fmax=0.15)
# Turn on CI
neb.climb = True
# Fully converge the path including an image climbing to the saddle point
qn = BFGS(neb, trajectory='neb_CI.traj')
qn.run(fmax=0.09)
write('neb_done.traj', images)
def optNEB(self, trans, path, changePoints, mols):
# Open files for saving IRCdata
xyzfile3 = open((path + "/IRC3.xyz"), "w")
MEP = open((path + "/MEP.txt"), "w")
imagesTemp1 = []
index = changePoints[0] - 100
molTemp = mols[index].copy()
imagesTemp1.append(molTemp.copy())
for i in range(0, 100):
imagesTemp1.append(mols[changePoints[0] - 100].copy())
try:
imagesTemp1.append(mols[changePoints[-1] + 300])
except:
imagesTemp1.append(self.CombProd.copy())
neb1 = NEB(imagesTemp1, k=1.0, remove_rotation_and_translation=True)
try:
neb1.interpolate('idpp')
except:
neb1.interpolate()
for i in range(0, len(imagesTemp1)):
try:
imagesTemp1[i] = tl.setCalc(imagesTemp1[i], self.lowString,
self.lowMeth, self.lowLev)
for i in range(0, len(trans)):
c = FixAtoms(trans)
imagesTemp1[i].set_constraint(c)
min = BFGS(imagesTemp1[i])
min.run(fmax=0.005, steps=40)
del imagesTemp1[i].constraints
except:
pass
optimizer = FIRE(neb1)
try:
optimizer.run(fmax=0.07, steps=300)
except:
pass
print("passed seccond neb")
neb1_2 = NEB(imagesTemp1, k=0.01, remove_rotation_and_translation=True)
try:
optimizer.run(fmax=0.07, steps=200)
except:
pass
neb2 = NEB(imagesTemp1,
climb=True,
remove_rotation_and_translation=True)
try:
optimizer.run(fmax=0.07, steps=200)
except:
pass
for i in range(0, len(imagesTemp1)):
tl.printTraj(xyzfile3, imagesTemp1[i])
print("NEB printed")
xyzfile3.close()
point = 0
maxEne = -50000000
try:
for i in range(0, len(imagesTemp1)):
MEP.write(
str(i) + ' ' + str(imagesTemp1[i].get_potential_energy()) +
'\n')
if imagesTemp1[i].get_potential_energy() > maxEne and i > 5:
point = i
maxEne = imagesTemp1[i].get_potential_energy()
self.TS2 = imagesTemp1[point]
except:
point = 0
print("TS Climb part")
write(path + '/TSClimbGuess.xyz', self.TS2)
try:
self.TS2Freqs, self.imaginaryFreq2, zpe, energy, self.TS2, rmol, pmol = self.characteriseTSExt(
self.TS2, False, path, self.QTS3)
except:
self.TS2Freqs, self.imaginaryFreq2, zpe, energy, self.TS2, rmol, pmol = self.characteriseTSExt(
self.TS2, True, path, self.QTS3)
self.TScorrect = self.compareRandP(rmol, pmol)
self.forwardBarrier2 = energy + zpe
constraint = FixAtoms(indices=[1, 3]) # fix OO
for image in images:
image.set_calculator(EMT())
image.set_constraint(constraint)
for image in images: # O-H(shared) distance
print(image.get_distance(1, 2), image.get_potential_energy())
# Relax initial and final states:
if 1:
# XXX: Warning:
# One would have to optimize more tightly in order to get
# symmetric anion from both images[0] and [1], but
# if one optimizes tightly one gets rotated(H2O) ... OH- instead
dyn1 = QuasiNewton(images[0])
dyn1.run(fmax=0.01)
dyn2 = QuasiNewton(images[-1])
dyn2.run(fmax=0.01)
# Interpolate positions between initial and final states:
neb.interpolate()
for image in images:
print(image.get_distance(1, 2), image.get_potential_energy())
dyn = BFGS(neb, trajectory='emt_h3o2m.traj')
dyn.run(fmax=0.05)
for image in images:
print(image.get_distance(1, 2), image.get_potential_energy())
def compareRandP(self, rmol, pmol):
#Check if TS links reac and prod
rmol = tl.setCalc(rmol, self.lowString, self.lowMeth, self.lowLev)
min = BFGS(rmol)
try:
min.run(fmax=0.1, steps=50)
except:
min.run(fmax=0.1, steps=50)
Name = tl.getSMILES(rmol, False).strip('\n\t')
FullName = Name.split('____')
if len(FullName) > 1:
FullName = FullName[0]
pmol = tl.setCalc(pmol, self.lowString, self.lowMeth, self.lowLev)
min = BFGS(pmol)
try:
min.run(fmax=0.1, steps=50)
except:
min.run(fmax=0.1, steps=50)
Name2 = tl.getSMILES(pmol, False).strip('\n\t')
FullName2 = Name2.split('____')
if len(FullName2) > 1:
FullName2 = FullName2[0]
if ((FullName == self.ReacName and FullName2 == self.ProdName)
or (FullName2 == self.ReacName and FullName == self.ProdName)):
TScorrect = True
else:
TScorrect = False
print('TS1 try does not connect reactants and products')
return TScorrect
