def optimize_rdkit_molecule(self, mrdk, cid, fmax=0.0001, steps=500, logger='opt.out'):
mol = __convert_rdkitmol_to_aseatoms__(mrdk,cid)
mol.set_calculator(ANI(False))
mol.calc.setnc(self.nc)
dyn = LBFGS(mol,logfile=logger)
#dyn = LBFGS(mol)
dyn.run(fmax=fmax,steps=steps)
stps = dyn.get_number_of_steps()
xyz = mol.get_positions()
for i,x in enumerate(xyz):
mrdk.GetConformer(cid).SetAtomPosition(i,x)
def optimize_molecule(self, X, S, fmax=0.1, steps=10000, logger='opt.out'):
mol = Atoms(symbols=S, positions=X)
mol.set_pbc((False, False, False))
mol.set_calculator(ANIENS(self.ens))
dyn = LBFGS(mol,logfile=logger)
#dyn = LBFGS(mol)
#dyn = QuasiNewton(mol,logfile=logger)
dyn.run(fmax=fmax,steps=steps)
stps = dyn.get_number_of_steps()
opt = True
if steps == stps:
opt = False
return np.array(mol.get_positions(),dtype=np.float32), opt
def SurfaceEnergy(images, natoms, calc, fmax=0.01, debug=False):
"""Calculate the surface energy from a list of slab images.
Parameters
----------
images: List of slab images which the calculation is based on. The x and
y dimensions of the images unit cell must be conserved.
natoms: Number of atoms in a atomic layer in the slabs.
calc: Calculator object that can be attached to the images.
"""
ucell = images[0].get_cell()
layers = np.zeros(len(images))
energies = np.zeros(len(images))
if debug:
print "Layers Energy LBFGS steps"
for i, atoms in enumerate(images):
cell = atoms.get_cell()
if (ucell[0] != cell[0]).any() or (ucell[1] != cell[1]).any():
raise ValueError(
"The x and y dimensions of the unit cell must be conserved.")
atoms.set_calculator(calc)
dyn = LBFGS(atoms, logfile=None, trajectory=None)
dyn.run(fmax=fmax, steps=1000)
assert dyn.converged(), "LBFGS not converged in 100 steps!"
layers[i] = len(atoms) / natoms
energies[i] = atoms.get_potential_energy()
if debug:
print "%4i%12.3f%10i" % (layers[i], energies[i],
dyn.get_number_of_steps())
p = polyfit(layers.reshape((-1, 1)), energies, 1)
surface_energy = p.c[0] / (2 * natoms)
assert surface_energy > 0.0
return surface_energy
def optimize_rdkit_molecule(self, mrdk, cid, fmax=0.1, steps=10000, logger='opt.out'):
mol = __convert_rdkitmol_to_aseatoms__(mrdk,cid)
mol.set_pbc((False, False, False))
mol.set_calculator(ANIENS(self.ens))
dyn = LBFGS(mol,logfile=logger)
#dyn = LBFGS(mol)
#dyn = QuasiNewton(mol,logfile=logger)
dyn.run(fmax=fmax,steps=steps)
stps = dyn.get_number_of_steps()
opt = True
if steps == stps:
opt = False
xyz = mol.get_positions()
for i,x in enumerate(xyz):
mrdk.GetConformer(cid).SetAtomPosition(i,x)
return opt
def SurfaceEnergy(images, natoms, calc, fmax=0.01, debug=False):
"""Calculate the surface energy from a list of slab images.
Parameters
----------
images: List of slab images which the calculation is based on. The x and
y dimensions of the images unit cell must be conserved.
natoms: Number of atoms in a atomic layer in the slabs.
calc: Calculator object that can be attached to the images.
"""
ucell = images[0].get_cell()
layers = np.zeros(len(images))
energies = np.zeros(len(images))
if debug:
print "Layers Energy LBFGS steps"
for i, atoms in enumerate(images):
cell = atoms.get_cell()
if (ucell[0] != cell[0]).any() or (ucell[1] != cell[1]).any():
raise ValueError("The x and y dimensions of the unit cell must be conserved.")
atoms.set_calculator(calc)
dyn = LBFGS(atoms, logfile=None, trajectory=None)
dyn.run(fmax=fmax, steps=1000)
assert dyn.converged(), "LBFGS not converged in 100 steps!"
layers[i] = len(atoms) / natoms
energies[i] = atoms.get_potential_energy()
if debug:
print "%4i%12.3f%10i" % (layers[i], energies[i], dyn.get_number_of_steps())
p = polyfit(layers.reshape((-1,1)), energies, 1)
surface_energy = p.c[0] / (2 * natoms)
assert surface_energy > 0.0
return surface_energy
for i, f in enumerate(files):
data = hdn.read_rcdb_coordsandnm(idir + f)
X = data['coordinates']
S = data['species']
mol = Atoms(positions=X, symbols=S)
mol.set_calculator(ANI(False))
mol.calc.setnc(nc)
dyn = LBFGS(mol, logfile='optimization.log')
dyn.run(fmax=0.00001, steps=1000)
X = mol.get_positions()
Nc = int(f.split(".")[0].split("-")[1])
Fp = f.split("-")[0]
smiles = get_smiles(idir + f)
print(i, 'of', len(files), ':', f, dyn.get_number_of_steps(), Nc)
hdn.write_rcdb_input(X,
S,
Nc,
sdir,
Fp,
50,
'wb97x/6-31g*',
'800.0',
fill=4,
comment='Smiles: ' + smiles)
xyz[i, 2] = pos.z
typ.append(sym)
tstr += sym
hbr.append(str(hyb))
# Set molecule
geometry = Atoms(tstr, xyz)
# Setup ANI and calculate single point energy
geometry.set_calculator(NeuroChem2ASE(nc))
# Geometry optimization with BFGS
start_time = time.time()
dyn = LBFGS(geometry, logfile='logfile.out')
dyn.run(fmax=0.0001, steps=500)
Ns = dyn.get_number_of_steps()
print('[ANI Total time:',
time.time() - start_time, 'seconds. Steps: ', Ns, ']')
# Get potential
E1 = geometry.get_potential_energy()
# Get geometry
xyz = geometry.get_positions()
gt.writexyzfile(dir + 'xyz/mol-' + gdbname + '-' + str(Nmol) + '.xyz',
xyz, typ)
#print('Energy: ' + str(E1))
fE.write("{:.10f}".format(E1) + '\n')
fS.write(Chem.MolToSmiles(m) + '\n')
xyz[i, 0] = pos.x
xyz[i, 1] = pos.y
xyz[i, 2] = pos.z
mol = Atoms(symbols=spc, positions=xyz)
mol.set_calculator(ANI(False))
mol.calc.setnc(nc[0])
#print(xyz)
#print(spc)
#print('Optimize...')
#start_time = time.time()
dyn = LBFGS(mol, logfile='logfile.txt')
dyn.run(fmax=0.001, steps=5000)
conv = True if dyn.get_number_of_steps() == 10000 else False
#print('[ANI Total time:', time.time() - start_time, 'seconds]')
xyz = np.array(mol.get_positions(),
dtype=np.float32).reshape(xyz.shape[0], 3)
xyz2 = np.array(mol.get_positions(),
dtype=np.float32).reshape(1, xyz.shape[0], 3)
if conv:
print('Failed to converge!!!')
#print(' SPECIES: ', spc)
energies = np.zeros((5), dtype=np.float64)
energies2 = np.zeros((5), dtype=np.float64)
N = 0
#print(np.vstack(forces))
csl = []
for j in range(0, len(list(spc))):
print(np.vstack(forces)[:, j, :])
print(np.sum(np.power(np.vstack(forces)[:, j, :], 2.0), axis=0))
csm = cosine_similarity(np.vstack(forces)[:, j, :])
cse = np.mean(np.asarray(csm[np.triu_indices_from(csm, 1)]))
csl.append((spc[j], cse))
energies = hdt.hatokcal * energies
sigma = np.std(energies) / float(len(spc))
f.write("{:.7f}".format((dyn.get_number_of_steps() * dt) / 1000.0) + ' ' +
"{:.7f}".format(energies[0]) + ' ' + "{:.7f}".format(energies[1]) +
' ' + "{:.7f}".format(energies[2]) + ' ' +
"{:.7f}".format(energies[3]) + ' ' + "{:.7f}".format(energies[4]) +
' ' + "{:.7f}".format(sigma) + '\n')
ekin = mol.get_kinetic_energy() / len(mol)
output = ' ' + str(i) + ' (' + str(len(spc)) + ',', "{:.4f}".format(
ekin / (1.5 * units.kB)), 'K) : stps=' + str(dyn.get_number_of_steps(
)) + ' : std(kcal/mol)=' + "{:.4f}".format(sigma)
print(output, csl)
'''
if sigma > 0.5:
vib = Vibrations(mol)
vib.run()