def main():
T = 300.0 # Simulation temperature
dt = 1 * units.fs # MD timestep
nsteps = 100000 # MD number of steps
mixing = [1,-1,0] # [1.0, -1.0, 0.3] # mixing weights for "real" and ML forces
lengthscale = 0.5 # KRR Gaussian width.
gamma = 1 / (2 * lengthscale**2)
grid_spacing = 0.05
# mlmodel = GaussianProcess(corr='squared_exponential',
# # theta0=1e-1, thetaL=1e-4, thetaU=1e+2,
# theta0=1.,
# random_start=100, normalize=False, nugget=1.0e-2)
mlmodel = KernelRidge(kernel='rbf',
gamma=gamma, gammaL = gamma/4, gammaU=2*gamma,
alpha=1.0e-2, variable_noise=False, max_lhood=False)
anglerange = sp.arange(0, 2*sp.pi + grid_spacing, grid_spacing)
X_grid = sp.array([[sp.array([x,y]) for x in anglerange]
for y in anglerange]).reshape((len(anglerange)**2, 2))
ext_field = None # IgnoranceField(X_grid, y_threshold=-0.075, cutoff = 3.)
# Bootstrap from initial database? uncomment
# data = sp.loadtxt('phi_psi_F.csv')
# # data[:,:2] -= 0.025 # fix because of old round_vector routine
# mlmodel.fit(data[:,:2], data[:,2])
# ext_field.update_cost(mlmodel.X_fit_, mlmodel.y)
# mlmodel.fit(X_grid, sp.zeros(len(X_grid)))
# mlmodel.fit(sp.load('X_fitD.npy'), sp.load('y_fitD.npy'))
# Prepare diagnostic visual effects.
plt.close('all')
plt.ion()
fig, ax = plt.subplots(1, 2, figsize=(24, 13))
atoms = ase.io.read('myplum.xyz')
with open('data.input', 'r') as file:
lammpsdata = file.readlines()
# Set temperature
MaxwellBoltzmannDistribution(atoms, 0.5 * units.kB * T, force_temp=True)
# Set total momentum to zero
p = atoms.get_momenta()
p -= p.sum(axis=0) / len(atoms)
atoms.set_momenta(p)
atoms.rescale_velocities(T)
# Select MD propagator
mdpropagator = Langevin(atoms, dt, T*units.kB, 1.0e-2, fixcm=True)
# mdpropagator = MLVerlet(atoms, dt, T)
# Zero-timestep evaluation and data files setup.
print("START")
pot_energy, f = calc_lammps(atoms, preloaded_data=lammpsdata)
mlmodel.accumulate_data(round_vector(atoms.colvars(), precision=grid_spacing), 0.)
# mlmodel.accumulate_data(round_vector(atoms.colvars(), precision=grid_spacing), pot_energy)
printenergy(atoms, pot_energy)
try:
os.remove('atomstraj.xyz')
except:
pass
traj = open("atomstraj.xyz", 'a')
atoms.write(traj, format='extxyz')
results, traj_buffer = [], []
# When in the simulation to update the ML fit -- optional.
teaching_points = sp.unique((sp.linspace(0, nsteps**(1/3), nsteps/20)**3).astype('int') + 1)
# MD Loop
for istep in range(nsteps):
# Flush Cholesky decomposition of K
if istep % 1000 == 0:
mlmodel.Cho_L = None
mlmodel.max_lhood = False
print("Dihedral angles | phi = %.3f, psi = %.3f " % (atoms.phi(), atoms.psi()))
do_update = (istep % 60 == 59)
t = get_time()
mdpropagator.halfstep_1of2(f)
print("TIMER 001 | %.3f" % (get_time() - t))
t = get_time()
f, pot_energy, _ = get_all_forces(atoms, mlmodel, grid_spacing, T, extfield=ext_field, mixing=mixing, lammpsdata=lammpsdata, do_update=do_update)
if do_update and mlmodel.max_lhood:
mlmodel.max_lhood = False
mdpropagator.halfstep_2of2(f)
print("TIMER 002 | %.3f" % (get_time() - t))
# manual cooldown!!!
if sp.absolute(atoms.get_kinetic_energy() / (1.5 * units.kB * atoms.get_number_of_atoms()) - T) > 100:
atoms.rescale_velocities(T)
printenergy(atoms, pot_energy/atoms.get_number_of_atoms(), step=istep)
# if do_update:
# try:
# print("Lengthscale = %.3e, Noise = %.3e" % (1/(2 * mlmodel.gamma)**0.5, mlmodel.noise.mean()))
# except:
# print("")
if istep % 60 == 59:
t = get_time()
if 'datasetplot' not in locals():
datasetplot = pl.Plot_datapts(ax[0], mlmodel)
else:
datasetplot.update()
if hasattr(mlmodel, 'dual_coef_'):
if 'my2dplot' not in locals():
my2dplot = pl.Plot_energy_n_point(ax[1], mlmodel, atoms.colvars().ravel())
else:
my2dplot.update_prediction()
my2dplot.update_current_point(atoms.colvars().ravel())
print("TIMER 003 | %.03f" % (get_time() - t))
t = get_time()
fig.canvas.draw()
print("TIMER 004 | %.03f" % (get_time() - t))
# fig.canvas.print_figure('current.png')
t = get_time()
# traj_buffer.append(atoms.copy())
# if istep % 100 == 0:
# for at in traj_buffer:
# atoms.write(traj, format='extxyz')
# traj_buffer = []
results.append(sp.array([atoms.phi(), atoms.psi(), pot_energy]))
print("TIMER 005 | %.03f" % (get_time() - t))
traj.close()
print("FINISHED")
sp.savetxt('results.csv', sp.array(results))
sp.savetxt('mlmodel.dual_coef_.csv', mlmodel.dual_coef_)
sp.savetxt('mlmodel.X_fit_.csv', mlmodel.X_fit_)
sp.savetxt('mlmodel.y.csv', mlmodel.y)
calc = None
return mlmodel
