"""
Flat-histogram simulation of TraPPE alkanes in the grand canonical ensemble.
https://www.nist.gov/mml/csd/chemical-informatics-group/sat-tmmc-liquid-vapor-coexistence-properties-trappe-ua-n-butane
"""
import argparse
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pyfeasst import fstio
from pyfeasst import physical_constants
from pyfeasst import macrostate_distribution
def parse():
""" Parse arguments from command line or change their default values. """
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--feasst_install', type=str, default='../../../build/',
help='FEASST install directory (e.g., the path to build)')
parser.add_argument('--fstprt', type=str, default='/feasst/particle/n-octane.fstprt',
#parser.add_argument('--fstprt', type=str, default='/feasst/particle/n-butane.fstprt',
help='FEASST particle definition')
parser.add_argument('--temperature', type=float, default=350, help='temperature in Kelvin')
parser.add_argument('--beta_mu', type=float, default=-6, help='beta time chemical potential')
parser.add_argument('--cutoff', type=float, default=12, help='real space cutoff distance')
parser.add_argument('--max_particles', type=int, default=285, help='maximum number of particles')
parser.add_argument('--min_particles', type=int, default=0, help='minimum number of particles')
parser.add_argument('--min_particles_second_window', type=int, default=15, help='minimum number of particles in the second window')
parser.add_argument('--min_sweeps', type=int, default=2,
help='Minimum number of sweeps defined in https://dx.doi.org/10.1063/1.4918557')
parser.add_argument('--cubic_side_length', type=float, default=45,
help='cubic periodic boundary length')
parser.add_argument('--tpi', type=int, default=int(1e6),
help='trials per iteration, similar to MC cycles, but not necessary num_particles')
parser.add_argument('--equilibration_iterations', type=int, default=0,
help='number of iterations for equilibration')
parser.add_argument('--hours_checkpoint', type=float, default=0.2, help='hours per checkpoint')
parser.add_argument('--hours_terminate', type=float, default=1, help='hours until termination')
parser.add_argument('--procs_per_node', type=int, default=32, help='number of processors')
parser.add_argument('--run_type', '-r', type=int, default=0,
help='0: run, 1: submit to queue, 2: post-process')
parser.add_argument('--seed', type=int, default=-1,
help='Random number generator seed. If -1, assign random seed to each sim.')
parser.add_argument('--max_restarts', type=int, default=10, help='Number of restarts in queue')
parser.add_argument('--num_nodes', type=int, default=1, help='Number of nodes in queue')
parser.add_argument('--scratch', type=str, default=None,
help='Optionally write scheduled job to scratch/logname/jobid.')
parser.add_argument('--node', type=int, default=0, help='node ID')
parser.add_argument('--queue_id', type=int, default=-1, help='If != -1, read args from file')
parser.add_argument('--queue_task', type=int, default=0, help='If > 0, restart from checkpoint')
# Convert arguments into a parameter dictionary, and add argument-dependent parameters.
args, unknown_args = parser.parse_known_args()
assert len(unknown_args) == 0, 'An unknown argument was included: '+str(unknown_args)
params = vars(args)
params['script'] = __file__
params['prefix'] = 'trappe'
params['sim_id_file'] = params['prefix']+ '_sim_ids.txt'
params['minutes'] = int(params['hours_terminate']*60) # minutes allocated on queue
params['hours_terminate'] = 0.95*params['hours_terminate'] - 0.1 # terminate FEASST before SLURM
params['hours_terminate'] *= params['procs_per_node'] # real time -> cpu time
params['hours_checkpoint'] *= params['procs_per_node']
params['num_sims'] = params['num_nodes']
params['procs_per_sim'] = params['procs_per_node']
params['beta'] = 1./(params['temperature']*physical_constants.MolarGasConstant().value()/1e3) # mol/kJ
params['mu'] = params['beta_mu']/params['beta']
params['dccb_cut'] = 4.
params['dccb_cut'] = params['cubic_side_length']/int(params['cubic_side_length']/params['dccb_cut']) # maximize inside box
params['mu_init']=10
if 'n-butane' in params['fstprt']:
params['num_sites'] = 4
params['molecular_weight'] = 58.12
elif 'n-octane' in params['fstprt']:
params['num_sites'] = 8
params['molecular_weight'] = 114.23
else:
assert False, "input new num_sites and molecular_weight into PARMS"
params['last_site'] = params['num_sites'] - 1
write_grow_file(filename="trappe_grow_canonical.txt", params=params, gce=False)
write_grow_file(filename="trappe_grow_grand_canonical.txt", params=params, gce=True)
return params, args
def write_partial(f, bond, angle, dihedral, params):
if params['num_sites'] == 2:
f.write(bond)
elif params['num_sites'] == 3:
f.write(angle)
elif params['num_sites'] > 3:
f.write(dihedral)
else:
print('unrecognized num_sites', params['num_sites'])
assert False
# write TrialGrowFile to include grand canonical ensemble growth and canonica ensemble reptations
def write_grow_file(filename, params, gce):
with open(filename, 'w') as f:
f.write("TrialGrowFile\n\n")
for inv in [True, False]:
for trial_type in range(3+int(params['num_sites']/2)): # 0: reptate, 1: full regrow, 2+: partial regrow
#for trial_type in [0, 1, 2]: # 0: reptate, 1: full regrow, 2: partial regrow
for site in range(params['num_sites']):
for i in range(4):
sign = -1
#if (trial_type == 0 or trial_type == 2) and site != params['num_sites'] - 1:
if trial_type == 0 and site != params['num_sites'] - 1:
sign = 1
params['site'+str(i)] = site + sign*i
if inv:
params['site'+str(i)] = params['num_sites'] - site - 1 - sign*i
bond = """bond true mobile_site {site0} anchor_site {site1} num_steps 4 reference_index 0\n""".format(**params)
angle = """angle true mobile_site {site0} anchor_site {site1} anchor_site2 {site2} num_steps 4 reference_index 0\n""".format(**params)
dihedral = """dihedral true mobile_site {site0} anchor_site {site1} anchor_site2 {site2} anchor_site3 {site3} num_steps 4 reference_index 0\n""".format(**params)
# full regrowth insertion/deletion
if trial_type == 1 and gce:
if site == 0:
f.write("""particle_type 0 weight 2 transfer true site {site0} num_steps 4 reference_index 0\n""".format(**params))
elif site == 1:
f.write(bond)
elif site == 2:
f.write(angle)
else:
f.write(dihedral)
# # reptation. There seems to be a problem with reptation.
# elif trial_type == 0 and not gce:
# if site == params['num_sites'] - 1:
# write_partial(f, bond, angle, dihedral, params)
# else:
# if site == 0:
# f.write("""particle_type 0 weight 2 """)
# f.write("""reptate true mobile_site {site0} anchor_site {site1} num_steps 1 reference_index 0\n""".format(**params))
# # partial regrow of the last site
# if not gce and trial_type == 2:
# if site == 0:
# f.write("""particle_type 0 weight 2 """)
# write_partial(f, bond, angle, dihedral, params)
# partial regrow
if not gce and trial_type > 1:
num_grow = trial_type - 1
if params['num_sites'] - site < num_grow:
if params['num_sites'] - site == num_grow - 1:
f.write('particle_type 0 weight '+str(2/(trial_type-2))+' ')
if site == 1:
f.write(bond)
elif site == 2:
f.write(angle)
elif site != 0:
f.write(dihedral)
f.write("\n")
def write_feasst_script(params, script_file):
""" Write fst script for a single simulation with keys of params {} enclosed. """
with open(script_file, 'w', encoding='utf-8') as myfile:
myfile.write("""
# first, initialize multiple clones into windows
CollectionMatrixSplice hours_per {hours_checkpoint} ln_prob_file {prefix}n{node}_lnpi.txt min_window_size -1
WindowExponential maximum {max_particles} min0 {min_particles} min1 {min_particles_second_window} num {procs_per_node} overlap 0 alpha 1.5 min_size 3
Checkpoint checkpoint_file {prefix}{sim}_checkpoint.fst num_hours {hours_checkpoint} num_hours_terminate {hours_terminate}
RandomMT19937 seed {seed}
Configuration cubic_side_length {cubic_side_length} particle_type0 {fstprt} cutoff {cutoff}
Potential Model LennardJones
Potential Model LennardJones VisitModel VisitModelIntra intra_cut 3
Potential VisitModel LongRangeCorrections
RefPotential Model LennardJones VisitModel VisitModelCell min_length {dccb_cut} reference_index 0
RefPotential Model LennardJones VisitModel VisitModelIntra intra_cut 3 reference_index 0
ThermoParams beta {beta} chemical_potential {mu_init}
Metropolis
TrialTranslate weight 1 tunable_param 0.2 tunable_target_acceptance 0.25
TrialParticlePivot weight 0.25 particle_type 0 tunable_param 0.2 tunable_target_acceptance 0.25 pivot_site 0
TrialParticlePivot weight 0.25 particle_type 0 tunable_param 0.2 tunable_target_acceptance 0.25 pivot_site {last_site}
TrialGrowFile grow_file trappe_grow_canonical.txt
CheckEnergy trials_per_update {tpi} decimal_places 4
# gcmc initialization and nvt equilibration
TrialGrowFile grow_file trappe_grow_grand_canonical.txt
Log trials_per_write {tpi} output_file {prefix}n{node}s[sim_index]_eq.txt include_bonds true
Tune
Run until_num_particles [soft_macro_min]
RemoveTrial name_contains add
RemoveTrial name_contains remove
ThermoParams beta {beta} chemical_potential {mu}
Metropolis num_trials_per_iteration {tpi} num_iterations_to_complete {equilibration_iterations}
Run until_criteria_complete true
RemoveModify name Tune
RemoveAnalyze name Log
# gcmc tm production
FlatHistogram Macrostate MacrostateNumParticles width 1 max {max_particles} min {min_particles} soft_macro_max [soft_macro_max] soft_macro_min [soft_macro_min] \
Bias WLTM min_sweeps {min_sweeps} min_flatness 25 collect_flatness 20 min_collect_sweeps 1
TrialGrowFile grow_file trappe_grow_grand_canonical.txt
Log trials_per_write {tpi} output_file {prefix}n{node}s[sim_index].txt include_bonds true
Movie trials_per_write {tpi} output_file {prefix}n{node}s[sim_index]_eq.xyz stop_after_iteration 1
Movie trials_per_write {tpi} output_file {prefix}n{node}s[sim_index].xyz start_after_iteration 1
Tune trials_per_write {tpi} output_file {prefix}n{node}s[sim_index]_tune.txt multistate true stop_after_iteration 1
Energy trials_per_write {tpi} output_file {prefix}n{node}s[sim_index]_en.txt multistate true start_after_iteration 1
CriteriaWriter trials_per_write {tpi} output_file {prefix}n{node}s[sim_index]_crit.txt
CriteriaUpdater trials_per_update 1e5
""".format(**params))
def post_process(params):
lnp = macrostate_distribution.splice_collection_matrix(prefix='trappen0s', suffix='_crit.txt', use_soft=True)
assert np.abs(lnp.ln_prob()[1] - lnp.ln_prob()[0] - 5.86440399999992) < 0.1
assert np.abs(lnp.ln_prob()[2] - lnp.ln_prob()[1] - 5.22683300000017) < 0.1
# equilibrium test below was abandoned to reduce max_particles for faster convergence
#lnp.equilibrium()
#lnp.plot(show=True)
#print('WARNING: max_particles should be higher but the liquid peak was truncated to make the simulation faster')
#vapor, liquid = lnp.split()
#volume = params['cubic_side_length']**3
#na = physical_constants.AvogadroConstant().value()
#dens_conv = 1./volume/na*params['molecular_weight']/1e3*1e30 # convert from N/V units of molecules/A^3 to kg/m
## https://www.nist.gov/mml/csd/chemical-informatics-research-group/sat-tmmc-liquid-vapor-coexistence-properties-trappe-ua-n
#density = vapor.average_macrostate()*dens_conv
#print('density', density)
#assert np.abs(30.6 - density) < 2
#assert np.abs(508 - liquid.average_macrostate()*dens_conv) < 30
if __name__ == '__main__':
parameters, arguments = parse()
fstio.run_simulations(params=parameters,
sim_node_dependent_params=None,
write_feasst_script=write_feasst_script,
post_process=post_process,
queue_function=fstio.slurm_single_node,
args=arguments)