class MD:
"""Generates NVE dynamics from a GP model."""
def __init__(self,
dt: float,
number_of_steps: int,
gp: GaussianProcess,
pos_init: np.ndarray,
species,
cell,
masses,
prev_pos_init: np.ndarray = None,
par: bool = False,
skip: int = 0,
output_name='otf_run.out'):
self.dt = dt
self.Nsteps = number_of_steps
self.gp = gp
self.structure = Structure(cell=cell,
species=species,
positions=pos_init,
mass_dict=masses,
prev_positions=prev_pos_init)
self.noa = self.structure.positions.shape[0]
self.atom_list = list(range(self.noa))
self.curr_step = 0
# choose prediction function
if par is True:
self.pred_func = predict.predict_on_structure_par_en
else:
self.pred_func = predict.predict_on_structure_en
# initialize local energies
self.local_energies = np.zeros(self.noa)
self.pes = []
self.kes = []
self.output = Output(output_name)
def run(self):
self.output.write_header(self.gp.cutoffs, self.gp.kernel_name,
self.gp.hyps, self.gp.algo, self.dt,
self.Nsteps, self.structure)
self.start_time = time.time()
while self.curr_step < self.Nsteps:
# verlet algorithm follows Frenkel p. 70
self.gp.check_L_alpha()
self.pred_func()
new_pos = md.update_positions(self.dt, self.noa, self.structure)
self.update_temperature(new_pos)
self.record_state()
self.update_positions(new_pos)
self.curr_step += 1
self.output.conclude_run()
def update_positions(self, new_pos):
self.structure.prev_positions = self.structure.positions
self.structure.positions = new_pos
self.structure.wrap_positions()
def update_temperature(self, new_pos):
KE, temperature = \
md.calculate_temperature(new_pos, self.structure, self.dt,
self.noa)
self.KE = KE
self.temperature = temperature
def record_state(self):
self.pes.append(np.sum(self.local_energies))
self.kes.append(self.KE)
self.output.write_md_config(self.dt, self.curr_step, self.structure,
self.temperature, self.KE,
self.local_energies, self.start_time)
self.output.write_xyz_config(self.curr_step, self.structure,
self.dft_step)
class MD:
"""Generates NVE dynamics from a GP model."""
def __init__(self, dt: float, number_of_steps: int, gp: GaussianProcess,
pos_init: np.ndarray, species, cell, masses,
prev_pos_init: np.ndarray=None, par: bool=False, skip: int=0,
output_name='otf_run.out'):
self.dt = dt
self.Nsteps = number_of_steps
self.gp = gp
self.structure = Structure(cell=cell, species=species,
positions=pos_init,
mass_dict=masses,
prev_positions=prev_pos_init)
self.noa = self.structure.positions.shape[0]
self.atom_list = list(range(self.noa))
self.curr_step = 0
# choose prediction function
if par is True:
self.pred_func = self.predict_on_structure_par_en
else:
self.pred_func = self.predict_on_structure_en
# initialize local energies
self.local_energies = np.zeros(self.noa)
self.pes = []
self.kes = []
self.output = Output(output_name)
def run(self):
self.output.write_header(self.gp.cutoffs, self.gp.kernel_name, self.gp.hyps,
self.gp.algo, self.dt, self.Nsteps, self.structure)
self.start_time = time.time()
while self.curr_step < self.Nsteps:
# verlet algorithm follows Frenkel p. 70
self.pred_func()
new_pos = md.update_positions(self.dt, self.noa, self.structure)
self.update_temperature(new_pos)
self.record_state()
self.update_positions(new_pos)
self.curr_step += 1
self.output.conclude_run()
def predict_on_structure_en(self):
for n in range(self.structure.nat):
chemenv = AtomicEnvironment(self.structure, n, self.gp.cutoffs)
for i in range(3):
force, var = self.gp.predict(chemenv, i + 1)
self.structure.forces[n][i] = float(force)
self.structure.stds[n][i] = np.sqrt(np.absolute(var))
self.local_energies[n] = self.gp.predict_local_energy(chemenv)
def predict_on_structure_par_en(self):
n = 0
with concurrent.futures.ProcessPoolExecutor() as executor:
for res in executor.map(self.predict_on_atom_en, self.atom_list):
for i in range(3):
self.structure.forces[n][i] = res[0][i]
self.structure.stds[n][i] = res[1][i]
self.local_energies[n] = res[2]
n += 1
self.structure.dft_forces = False
def predict_on_atom_en(self, atom):
chemenv = AtomicEnvironment(self.structure, atom, self.gp.cutoffs)
comps = []
stds = []
# predict force components and standard deviations
for i in range(3):
force, var = self.gp.predict(chemenv, i+1)
comps.append(float(force))
stds.append(np.sqrt(np.absolute(var)))
# predict local energy
local_energy = self.gp.predict_local_energy(chemenv)
return comps, stds, local_energy
def update_positions(self, new_pos):
self.structure.prev_positions = self.structure.positions
self.structure.positions = new_pos
self.structure.wrap_positions()
def update_temperature(self, new_pos):
KE, temperature = \
md.calculate_temperature(new_pos, self.structure, self.dt,
self.noa)
self.KE = KE
self.temperature = temperature
def record_state(self):
self.pes.append(np.sum(self.local_energies))
self.kes.append(self.KE)
self.output.write_md_config(self.dt, self.curr_step, self.structure,
self.temperature, self.KE, self.local_energies,
self.start_time)
self.output.write_xyz_config(self.curr_step, self.structure,
self.dft_step)
