def write_chk(input_dict,working_directory='.'):
# collect data and initialize Yaff system
if 'cell' in input_dict.keys() and input_dict['cell'] is not None:
system = System(input_dict['numbers'], input_dict['pos']*angstrom, ffatypes=input_dict['ffatypes'], ffatype_ids=input_dict['ffatype_ids'], rvecs=input_dict['cell']*angstrom)
else:
system = System(input_dict['numbers'], input_dict['pos']*angstrom, ffatypes=input_dict['ffatypes'], ffatype_ids=input_dict['ffatype_ids'])
# determine masses, bonds and ffaypes from ffatype_rules
system.detect_bonds()
system.set_standard_masses()
# write dictionary to MolMod CHK file
system.to_file(posixpath.join(working_directory,'system.chk'))
def write_chk(input_dict, working_directory='.'):
# collect data and initialize Yaff system
if 'cell' in input_dict.keys() and input_dict['cell'] is not None:
system = System(input_dict['numbers'], input_dict['pos']*angstrom, rvecs=input_dict['cell']*angstrom, ffatypes=input_dict['ffatypes_man'], ffatype_ids=input_dict['ffatype_ids_man'])
else:
system = System(input_dict['numbers'], input_dict['pos']*angstrom, ffatypes=input_dict['ffatypes_man'], ffatype_ids=input_dict['ffatype_ids_man'])
# determine masses, bonds and ffaypes from ffatype_rules
system.detect_bonds()
system.set_standard_masses()
# write dictionnairy to MolMod CHK file
system.to_file(posixpath.join(working_directory,'input.chk'))
# Reload input.chk as dictionairy and add AI input data
d = load_chk(posixpath.join(working_directory,'input.chk'))
assert isinstance(input_dict['aiener'], float), "AI energy not defined in input, use job.read_abintio(...)"
assert isinstance(input_dict['aigrad'], np.ndarray), "AI gradient not defined in input, use job.read_abintio(...)"
assert isinstance(input_dict['aihess'], np.ndarray), "AI hessian not defined in input, use job.read_abintio(...)"
d['energy'] = input_dict['aiener']
d['grad'] = input_dict['aigrad']
d['hess'] = input_dict['aihess']
dump_chk(posixpath.join(working_directory,'input.chk'), d)
def run_GCMC(self, N_iterations, N_sample):
A = Acceptance()
if rank == 0:
if not (os.path.isdir('results')):
try:
os.mkdir('results')
except:pass
if self.write_traj:
ftraj = open('results/traj_%.8f.xyz'%(self.P/bar), 'w')
e = 0
t_it = time()
N_samples = []
E_samples = []
pressures = []
traj = []
q0s = []
if rank == 0:
print('\n Iteration inst. N inst. E inst. V time [s]')
print('--------------------------------------------------------')
for iteration in range(N_iterations+1):
if self.ads_ei:
sfac_init = deepcopy(self.sfac)
pos_init = deepcopy(self.pos)
rvecs_init = deepcopy(self.rvecs)
rvecs_flat_init = deepcopy(self.rvecs_flat)
V_init = self.V
e_el_real_init = self.e_el_real
e_vdw_init = self.e_vdw
switch = np.random.rand()
acc = 0
# Insertion / deletion
if(switch < self.prob[0] and not self.Z_ads == self.fixed_N):
if(switch < self.prob[0]/2):
new_pos = random_ads(self.pos_ads, self.rvecs)
e_new = self.insertion(new_pos)
exp_value = self.beta * (-e_new + e)
if(exp_value > 100):
acc = 1
elif(exp_value < -100):
acc = 0
else:
acc = min(1, self.V*self.beta*self.fugacity/self.Z_ads * np.exp(exp_value))
# Reject monte carlo move
if np.random.rand() > acc:
self.pos = pos_init
if self.ads_ei:
self.sfac = sfac_init
self.e_el_real = e_el_real_init
self.e_vdw = e_vdw_init
self.Z_ads -= 1
else:
e = e_new
elif(self.Z_ads > 0):
deleted_coord, e_new = self.deletion()
exp_value = -self.beta * (e_new - e)
if(exp_value > 100):
acc = 1
else:
acc = min(1, (self.Z_ads+1)/self.V/self.beta/self.fugacity * np.exp(exp_value))
# Reject monte carlo move
if np.random.rand() > acc:
self.pos = pos_init
if self.ads_ei:
self.sfac = sfac_init
self.e_el_real = e_el_real_init
self.e_vdw = e_vdw_init
self.Z_ads += 1
else:
e = e_new
elif(switch < self.prob[1]):
if self.Z_ads != 0:
trial = np.random.randint(self.Z_ads)
if((switch < self.prob[0] + (self.prob[1]-self.prob[0])/2) or self.nads == 1):
# Calculate translation energy as deletion + insertion of molecule
deleted_coord, e_new = self.deletion()
deleted_coord += self.step * (np.random.rand(3) - 0.5)
e_new = self.insertion(deleted_coord)
else:
# Calculate rotation energy as deletion + insertion of molecule
deleted_coord, e_new = self.deletion()
deleted_coord = random_rot(deleted_coord, circlefrac=0.1)
e_new = self.insertion(deleted_coord)
exp_value = -self.beta * (e_new - e)
if(exp_value > 0):
exp_value = 0
acc = min(1, np.exp(exp_value))
# Reject monte carlo move
if np.random.rand() > acc:
self.pos = pos_init
if self.ads_ei:
self.sfac = sfac_init
self.e_el_real = e_el_real_init
self.e_vdw = e_vdw_init
else:
e = e_new
else:
# Construct system and forcefield class for the MD engine
from yaff import System, ForceField, XYZWriter, VerletScreenLog, MTKBarostat, \
NHCThermostat, TBCombination, VerletIntegrator, HDF5Writer, log
log.set_level(0)
n = np.append(self.data.numbers_MOF, np.tile(self.data.numbers_ads, self.Z_ads))
ffa_MOF = self.data.system.ffatypes[self.data.system.ffatype_ids]
ffa_ads = self.data.system_ads.ffatypes[self.data.system_ads.ffatype_ids]
ffa = np.append(ffa_MOF, np.tile(ffa_ads, self.Z_ads))
assert len(self.pos) == len(ffa)
s = System(n, self.pos, ffatypes = ffa, rvecs=self.rvecs)
s.detect_bonds()
ff = ForceField.generate(s, self.ff_file,
rcut=self.rcut,
alpha_scale=self.alpha_scale,
gcut_scale=self.gcut_scale,
tailcorrections=True)
ff_lammps = swap_noncovalent_lammps(ff, fn_system='system_%.8f.dat'%(self.P/bar),
fn_table='table.dat',
nrows=5000,
kspace='pppm',
kspace_accuracy=1e-7,
scalings_ei = [1.0, 1.0, 1.0],
move_central_cell=False,
fn_log="none",
overwrite_table=False, comm=comm)
# Setup and NPT MD run
if rank == 0:
vsl = VerletScreenLog(step=50)
if self.write_h5s:
if self.fixed_N:
hdf5_writer = HDF5Writer(h5.File('results/temp_%d.h5'%self.fixed_N, mode='w'), step=101)
else:
hdf5_writer = HDF5Writer(h5.File('results/temp_%.8f.h5'%(self.P/bar), mode='w'), step=101)
ensemble_hook = NHCThermostat(temp=self.T, timecon=100*femtosecond, chainlength=3)
if self.barostat:
mtk = MTKBarostat(ff_lammps, temp=self.T, press=self.P, \
timecon=1000*femtosecond, vol_constraint = self.vol_constraint, anisotropic = True)
ensemble_hook = TBCombination(ensemble_hook, mtk)
if self.meta:
cv = CVVolume(ff_lammps.system)
sigma = 1000*angstrom**3
K = 20*kjmol
step = 498
# Run MD
t = time()
if self.write_h5s:
if rank == 0:
verlet = VerletIntegrator(ff_lammps, self.timestep, hooks=[ensemble_hook, vsl, hdf5_writer], temp0=self.T)
else:
verlet = VerletIntegrator(ff_lammps, self.timestep, hooks=[ensemble_hook], temp0=self.T)
else:
if rank == 0:
hooks = [ensemble_hook, vsl]
if self.meta:
meta = MTDHook(ff_lammps, cv, sigma, K, start=step, step=step)
for q0 in q0s:
meta.hills.add_hill(q0, K)
hooks.append(meta)
verlet = VerletIntegrator(ff_lammps, self.timestep, hooks=hooks, temp0=self.T)
else:
hooks = [ensemble_hook]
if self.meta:
meta = MTDHook(ff_lammps, cv, sigma, K, start=step, step=step)
for q0 in q0s:
meta.hills.add_hill(q0, K)
hooks.append(meta)
verlet = VerletIntegrator(ff_lammps, self.timestep, hooks=hooks, temp0=self.T)
e0_tot = verlet._compute_ekin() + ff_lammps.compute()
verlet.run(600)
ef_tot = verlet._compute_ekin() + ff_lammps.compute()
if not self.vol_constraint:
Vn = np.linalg.det(ff_lammps.system.cell.rvecs)
exp_value = -self.beta * (ef_tot - e0_tot + self.P * (Vn - self.V) - len(self.pos)/self.beta * np.log(Vn/self.V))
else:
exp_value = -self.beta * (ef_tot - e0_tot)
if(exp_value > 0):
exp_value = 0
acc = min(1, np.exp(exp_value))
# Accept monte carlo move
if np.random.rand() < acc:
if self.write_h5s:
# Append MD data to previous data
self.append_h5()
# Rebuild data for MC
pos_total = ff_lammps.system.pos
self.pos = pos_total[:self.N_frame]
pos_molecules = pos_total[self.N_frame:]
self.rvecs = ff_lammps.system.cell.rvecs
self.rvecs_flat = self.rvecs.reshape(9)
self.V = np.linalg.det(self.rvecs)
if self.meta:
q0s.append(self.V)
if self.ads_ei:
self.sfac = Sfac(self.pos, self.N_frame, self.rvecs_flat, \
self.charges, self.alpha, self.gcut)
self.e_el_real = 0
self.e_vdw = 0
if self.Z_ads > 0:
for p in np.split(pos_molecules, self.Z_ads):
e_new = self.insertion(p)
self.Z_ads -= 1
e = e_new
else:
e = 0
else:
self.pos = pos_init
self.rvecs = rvecs_init
self.rvecs_flat = rvecs_flat_init
self.V = V_init
if rank == 0:
log.set_level(log.medium)
if(iteration % N_sample == 0 and iteration > 0):
eprint = e
if np.abs(eprint) < 1e-10:
eprint = 0
if rank == 0:
print(' {:7.7} {:7.7} {:7.7} {:7.7} {:7.4}'.format(
str(iteration),str(self.Z_ads),str(eprint/kjmol),str(self.V/angstrom**3),time()-t_it)
)
t_it = time()
N_samples.append(self.Z_ads)
E_samples.append(e)
if self.Z_ads == self.fixed_N:
traj.append(self.pos)
if rank == 0 and self.write_traj:
natom = self.N_frame + self.nads * self.Z_ads
rv = self.rvecs_flat/angstrom
ffa_MOF = self.data.system.ffatypes[self.data.system.ffatype_ids]
ffa_ads = self.data.system_ads.ffatypes[self.data.system_ads.ffatype_ids]
ffa = np.append(ffa_MOF, np.tile(ffa_ads, self.Z_ads))
ftraj.write('%d\n%f %f %f %f %f %f %f %f %f\n'%(natom, rv[0], rv[1], rv[2], rv[3], rv[4], rv[5], rv[6], rv[7], rv[8]))
for s, p in zip(ffa, self.pos/angstrom):
ftraj.write('%s %f %f %f\n'%(s, p[0], p[1], p[2]))
if rank == 0:
print('Average N: %.3f'%np.average(N_samples))
if self.fixed_N:
np.save('results/N_%d.npy'%self.fixed_N, np.array(N_samples))
np.save('results/E_%d.npy'%self.fixed_N, np.array(E_samples))
else:
np.save('results/N_%.8f.npy'%(self.P/bar), np.array(N_samples))
np.save('results/E_%.8f.npy'%(self.P/bar), np.array(E_samples))
if self.fixed_N:
from yaff import System
n = np.append(self.data.numbers_MOF, np.tile(self.data.numbers_ads, self.Z_ads))
s = System(n, self.pos, rvecs=self.rvecs)
s.to_file('results/end_%d.xyz'%self.fixed_N)
mol = Molecule.from_file('results/end_%d.xyz'%self.fixed_N)
symbols = mol.symbols
self.write_traj(traj, symbols)
os.remove('results/end_%d.xyz'%self.fixed_N)
if self.write_traj:
ftraj.close()
