def nvtw(num_particles, num_procs, num_equil, num_prod, num_hours, dccb_begin, temperature, mu, steps_per, model):
mc = fst.MakeMonteCarlo()
#mc.set(fst.MakeRandomMT19937(fst.args({"seed": "1633373856"})))
beta = 1./temperature
if model == "lj":
mc.add(fst.MakeConfiguration(fst.args({"cubic_box_length": "8",
"particle_type0": fst.install_dir() + "/forcefield/lj.fstprt"})))
mc.add(fst.MakePotential(fst.MakeLennardJones()))
mc.add(fst.MakePotential(fst.MakeLongRangeCorrections()))
elif model == "sqw":
config = fst.MakeConfiguration(fst.args({"cubic_box_length": "8", "particle_type0": fst.install_dir() + "/forcefield/atom.fstprt"}))
config.set_model_param("cutoff", 0, 1.5)
mc.add(config)
mc.add(fst.MakePotential(fst.MakeSquareWell()))
elif model == "spce":
mc.add(fst.MakeConfiguration(fst.args({"cubic_box_length": "20",
"particle_type0": fst.install_dir() + "/forcefield/spce.fstprt"})))
mc.add(fst.MakePotential(fst.MakeEwald(fst.args({"alpha": str(5.6/20),
"kmax_squared": "38"}))))
mc.add(fst.MakePotential(fst.MakeModelTwoBodyFactory(fst.MakeLennardJones(), fst.MakeChargeScreened()),
fst.args({"table_size": "1e6"})))
mc.add(fst.MakePotential(fst.MakeChargeScreenedIntra(), fst.MakeVisitModelBond()))
mc.add(fst.MakePotential(fst.MakeChargeSelf()))
mc.add(fst.MakePotential(fst.MakeLongRangeCorrections()))
beta = 1./fst.kelvin2kJpermol(temperature, mc.configuration())
else:
assert(False) # model not recognized
# fill box with larger temperature and mu
mc.set(fst.MakeThermoParams(fst.args({"beta": "0.01", "chemical_potential": "10"})))
mc.set(fst.MakeMetropolis());
#trial_args = {"particle_type": "0", "site": "0", "reference_index": ref, "num_steps": num_steps}
mc.add(fst.MakeTrialTranslate(fst.args({"tunable_param": "0.1"})))
#mc.add(fst.MakeTrialGrow(fst.ArgsVector([dict({"translate": "true", "tunable_param": "0.1"}, **trial_args)])))
mc.add(fst.MakeTrialAdd(fst.args({"particle_type": "0", "weight": "4"})))
mc.add(fst.MakeTune(fst.args({"steps_per": steps_per})))
mc.add(fst.MakeCheckEnergy(fst.args({"steps_per": steps_per, "tolerance": "0.0001"})))
mc.add(fst.MakeLogAndMovie(fst.args({"steps_per": steps_per,
"file_name": model + str(num_particles)})))
mc.set(fst.MakeCheckpoint(fst.args({"file_name": "checkpoint" + str(num_particles) + ".fst",
"num_hours": str(0.1*num_procs*num_hours),
"num_hours_terminate": str(0.9*num_procs*num_hours)})))
mc.run(fst.MakeRun(fst.args({"until_num_particles": str(num_particles)})))
mc.run(fst.MakeRemoveTrial(fst.args({"name": "TrialAdd"})))
# nvt equilibration at desired temperature
mc.set(fst.MakeThermoParams(fst.args({"beta": str(beta),
"chemical_potential": str(mu)})))
mc.attempt(int((num_particles+1)*num_equil))
mc.run(fst.MakeRemoveModify(fst.args({"name": "Tune"})))
mc.add(fst.MakeTrialTransfer(fst.args({"particle_type": "0", "weight": "4"})))
#mc.add(fst.MakeTrialGrow(fst.ArgsVector([dict({"transfer": "true", "weight": "4"}, **trial_args)])))
mc.set(fst.MakeFlatHistogram(fst.args({
"Macrostate": "MacrostateNumParticles", "width": "1", "max": str(num_particles), "min": str(num_particles),
"Bias": "TransitionMatrix", "min_sweeps": "1"})))
mc.add(fst.MakeEnergy(fst.args({"steps_per_write": steps_per,
"file_name": "en" + str(num_particles) + ".txt"})))
mc.add(fst.MakeCriteriaWriter(fst.args({"steps_per": steps_per,
"file_name": "crit" + str(num_particles) + ".txt"})))
mc.attempt(int((num_particles+1)*num_prod))
def mc(
target_acceptance=0.25,
tunable_param=0.1,
density=0.85,
cutoff=4,
temperature=0.88,
steps_per=int(1e6),
):
box_length = 2 * cutoff
file_app = "_a" + str(target_acceptance) + "_r" + str(cutoff)
num_particles = int(density * box_length**3)
print('num_particles', num_particles)
print('target_acceptance', target_acceptance)
print('tunable_param', tunable_param)
monte_carlo = feasst.Prefetch(feasst.args({"steps_per_check": "10000000"}))
monte_carlo.activate_prefetch(False)
monte_carlo.set(lj_system(box_length=box_length, cutoff=cutoff))
monte_carlo.set(
feasst.MakeMetropolis(
feasst.args({
"beta": str(1. / temperature),
"chemical_potential": "1.",
})))
monte_carlo.add(
feasst.MakeTrialTranslate(
feasst.args({
"weight": "1.",
"tunable_param": str(tunable_param),
"tunable_target_acceptance": str(target_acceptance),
"tunable_percent_change": "0.01",
# "num_steps": "4",
# "reference_index": "0",
})))
feasst.SeekNumParticles(num_particles).with_trial_add().run(monte_carlo)
monte_carlo.add(
feasst.MakeLog(
feasst.args({
"steps_per": str(steps_per),
"file_name": "log" + file_app + ".txt",
"clear_file": "true"
})))
monte_carlo.add(
feasst.MakeCheckEnergy(
feasst.args({
"steps_per": str(steps_per),
"tolerance": str(1e-8)
})))
monte_carlo.add(feasst.MakeTune(feasst.args({"steps_per":
str(steps_per)})))
#equilibrate
monte_carlo.attempt(int(1e7))
if not args.nopipe:
monte_carlo.activate_prefetch(True)
monte_carlo.add(
feasst.MakeMeanSquaredDisplacement(
feasst.args({
"steps_per_update": "10000",
"updates_per_origin": "1000",
"file_name": "msd" + file_app + ".txt",
"steps_per_write": str(int(1e5))
})))
monte_carlo.add(
feasst.MakeCPUTime(
feasst.args({
"steps_per_update": str(steps_per),
"steps_per_write": str(steps_per),
"file_name": "cpu" + file_app + ".txt",
})))
#h0 = feasst.cpu_hours()
monte_carlo.attempt(int(1e8))
def mc(
trials_per=int(1e6),
):
box_length = 2.*args.cutoff
file_app = "_a" + str(args.rel_disp_prob) + "_rc" + str(args.cutoff)
monte_carlo = feasst.Prefetch(feasst.args({"trials_per_check": str(int(1e7))}))
monte_carlo.activate_prefetch(False)
# monte_carlo.set(feasst.MakeRandomMT19937(feasst.args({"seed": "1578687129"})))
monte_carlo.set(lj_system(box_length=box_length))
monte_carlo.set(feasst.MakeMetropolis(feasst.args({
"beta": str(1./args.temperature),
"chemical_potential": str(args.chemical_potential),
})))
monte_carlo.add(feasst.MakeTrialTranslate(feasst.args({
"weight": str(args.rel_disp_prob),
"tunable_param": str(args.max_move),
"tunable_target_acceptance": str(args.target_prob),
"tunable_percent_change": "0.1",
})))
num_particles = int(args.density*box_length**3)
nmin = num_particles - args.window_half_width
nmax = num_particles + args.window_half_width
if not args.nofh:
feasst.SeekNumParticles(nmin).with_trial_add().run(monte_carlo)
monte_carlo.add(feasst.MakeTrialTransfer(feasst.args({
"weight": "1",
"particle_type": "0"})))
if not args.nofh:
monte_carlo.set(fh.criteria_flathist(
temperature=args.temperature,
chemical_potential=args.chemical_potential,
macro_max=nmax,
macro_min=nmin,
iterations=args.iterations,
))
monte_carlo.add(feasst.MakeCriteriaUpdater(feasst.args({"trials_per": str(trials_per)})))
monte_carlo.add(feasst.MakeCriteriaWriter(feasst.args(
{"trials_per": str(trials_per), "file_name": "crit"+file_app+".txt"})))
else:
monte_carlo.add(feasst.MakeNumParticles(feasst.args({
"file_name": "num"+file_app+".txt",
"trials_per_write": str(trials_per),
})))
analyze.add(monte_carlo,
trials_per,
proc=file_app,
log="log"+file_app+".txt",
)
if not args.nopara:
monte_carlo.activate_prefetch(True)
monte_carlo.add(feasst.MakeCPUTime(feasst.args({
"trials_per_update": str(trials_per),
"trials_per_write": str(trials_per),
"file_name": "cpu" + file_app + ".txt",
})))
monte_carlo.add(feasst.MakeEnergy(feasst.args(
{"file_name": "energy"+file_app+".txt",
"trials_per_update": "1",
"trials_per_write": str(trials_per),
"multistate": "true"})))
monte_carlo.set(feasst.MakeCheckpoint(feasst.args(
{"file_name": "checkpoint"+file_app+".txt", "num_hours": "0.1"})))
# run until complete is not pprefetched correctly
monte_carlo.run_until_complete()
def monte_carlo(
proc=0, # processor number
criteria=criteria_flathist(), # flat histogram criteria
steps_per=1e5, # steps per analysis
system=lj_system.system(
lj_system.configuration(box_length=8, forcefield="data.lj")),
run=True # run the simulation
):
"""Create, run and return a flat histogram grand canonical Monte Carlo simulation"""
monte_carlo0 = fst.MonteCarlo()
monte_carlo0.set(system)
# add the minimum number of particles
monte_carlo0.set(
fst.MakeMetropolis(fst.args({
"beta": "0.1",
"chemical_potential": "1"
})))
monte_carlo0.add(
fst.MakeTrialTranslate(
fst.args({
"weight": "0.375",
"tunable_param": "2."
})))
if monte_carlo0.system().configuration().particle_type(0).num_sites() > 1:
monte_carlo0.add(
fst.MakeTrialRotate(
fst.args({
"weight": "0.375",
"tunable_param": "2."
})))
fst.add_trial_transfer(monte_carlo0,
fst.args({
"weight": "0.125",
"particle_type": "0"
}))
min_macro = int(criteria.macrostate().histogram().center_of_bin(0))
# print("seeking", min_macro)
fst.SeekNumParticles(min_macro).run(monte_carlo0)
# replace the acceptance criteria with flat histogram
monte_carlo0.set(criteria)
analyze.add(monte_carlo0,
steps_per,
proc=proc,
log="log" + str(proc) + ".txt")
# periodically write the status of the flat histogram criteria
monte_carlo0.add(
fst.MakeCriteriaUpdater(fst.args({"steps_per": str(steps_per)})))
monte_carlo0.add(
fst.MakeCriteriaWriter(
fst.args({
"steps_per": str(steps_per),
"file_name": "crit" + str(proc) + ".txt"
})))
# periodically write a checkpoint file
monte_carlo0.add(
fst.MakeCheckpoint(
fst.args({
"file_name": "checkpoint" + str(proc) + ".txt",
"num_hours": "0.01"
})))
# periodically write the energy of the macrostates
monte_carlo0.add(
fst.MakeEnergy(
fst.args({
"file_name": "energy" + str(proc) + ".txt",
"steps_per_update": "1",
"steps_per_write": str(steps_per),
"multistate": "true"
})))
if run:
monte_carlo0.run_until_complete()
#print(monte_carlo0.criteria().write())
return monte_carlo0
def mc(thread, mn, mx):
mc = fst.MakeMonteCarlo()
mc.set(fst.MakeRandomMT19937(fst.args({"seed": "123"})))
mc.add(
fst.MakeConfiguration(
fst.args({
"side_length0": str(args.lx),
"side_length1": str(args.ly),
"side_length2": str(args.lz),
"particle_type0": args.particle
})))
for site_type in range(mc.configuration().num_site_types()):
mc.get_system().get_configuration().set_model_param(
"cutoff", site_type, args.cutoff)
mc.add(fst.MakePotential(fst.MakeLennardJones()))
mc.add(
fst.MakePotential(fst.MakeLennardJones(),
fst.MakeVisitModelIntra(fst.args({"cutoff": "4"}))))
mc.add(fst.MakePotential(fst.MakeLongRangeCorrections()))
if mx > args.dccb_begin:
reference = fst.Potential(
fst.MakeLennardJones(),
fst.MakeVisitModelCell(
fst.args({"min_length": str(args.dccb_cutoff)})))
reference.set_model_params(mc.configuration())
for site_type in range(mc.configuration().num_site_types()):
reference.set_model_param("cutoff", site_type, args.dccb_cutoff)
mc.add_to_reference(reference)
#mc.add_to_reference(fst.MakePotential(fst.MakeLennardJones(),
# fst.MakeVisitModelIntra(fst.args({"cutoff": "4"}))))
stage_args = {"reference_index": "0", "num_steps": "4"}
else:
mc.add_to_reference(fst.MakePotential(fst.DontVisitModel()))
stage_args = {"reference_index": "0", "num_steps": "1"}
beta = 1. / fst.kelvin2kJpermol(args.temperature)
mc.set(
fst.MakeThermoParams(
fst.args({
"beta": str(beta),
"chemical_potential0": str(args.beta_mu / beta)
})))
mc.set(fst.MakeMetropolis())
mc.add(fst.MakeTrialTranslate(fst.args({"weight": "0.5"})))
mc.add(fst.MakeTrialRotate(fst.args({"weight": "0.5"})))
print('thread', thread, 'mn', mn, 'mx', mx)
if thread == 0 and mn > 0:
mc.add(fst.MakeTrialAdd(fst.args({"particle_type": "0"})))
mc.run(fst.MakeRun(fst.args({"until_num_particles": str(mn)})))
mc.run(fst.RemoveTrial(fst.args({"name": "TrialAdd"})))
mc.set(
fst.MakeFlatHistogram(
fst.MakeMacrostateNumParticles(
fst.Histogram(
fst.args({
"width": "1",
"max": str(mx),
"min": str(mn)
}))),
# fst.MakeTransitionMatrix(fst.args({"min_sweeps": str(args.sweeps)})),
fst.MakeWLTM(
fst.args({
"collect_flatness": str(args.collect_flatness),
"min_flatness": str(args.min_flatness),
"min_sweeps": "1000"
}))))
# configurational bias with TrialGrow: full and partial regrows from 0-site, and reverse
num_sites = mc.configuration().particle_type(0).num_sites()
for site in range(num_sites):
for g in gen_grow(num_sites, reptate=False):
mc.add(
fst.MakeTrialGrow(
fst.ArgsVector(add_particle_type_weight(
site, num_sites, g)), fst.args(stage_args)))
# reptation
if num_sites > 3:
for g in gen_grow(num_sites, reptate=True):
mc.add(
fst.MakeTrialGrow(
fst.ArgsVector(add_particle_type_weight(0, num_sites, g)),
fst.args(stage_args)))
# these moves may not take bond energies into consideration properly. Instead, should implement a dihedral rotation.
#mc.add(fst.MakeTrialCrankshaft(fst.args(dict({"weight": "0.25", "tunable_param": "25.", "max_length": "5."}, **stage_args))))
#mc.add(fst.MakeTrialPivot(fst.args(dict({"weight": "0.25", "tunable_param": "25.", "max_length": "5."}, **stage_args))))
mc.add(
fst.MakeCheckEnergy(
fst.args({
"steps_per": str(args.steps_per),
"tolerance": "0.0001"
})))
mc.add(
fst.MakeTune(
fst.args({
"steps_per": str(args.steps_per),
"stop_after_phase": "0"
})))
mc.add(
fst.MakeLogAndMovie(
fst.args({
"steps_per": str(args.steps_per),
"file_name": "clones" + str(thread),
"file_name_append_phase": "True"
})))
mc.add(
fst.MakeEnergy(
fst.args({
"file_name": "en" + str(thread) + '.txt',
"file_name_append_phase": "True",
"start_after_phase": "0",
"steps_per_write": str(args.steps_per),
"steps_per_update": "1",
"multistate": "True"
})))
mc.add(
fst.MakeCriteriaUpdater(fst.args({"steps_per": str(args.steps_per)})))
mc.add(
fst.MakeCriteriaWriter(
fst.args({
"steps_per": str(args.steps_per),
"file_name": "clones" + str(thread) + "_crit.txt",
"file_name_append_phase": "True"
})))
mc.set(
fst.MakeCheckpoint(
fst.args({
"file_name":
"checkpoint" + str(thread) + ".fst",
"num_hours_terminate":
str(0.9 * args.num_procs * args.num_hours)
})))
return mc
if args.task > 0:
mc = fst.MakeMonteCarlo("checkpoint.fst")
mc.attempt(args.trials - mc.trials().num_attempts())
quit()
mc.set(fst.MakeRandomMT19937(fst.args({"seed": args.seed})))
mc.add(
fst.Configuration(
fst.MakeDomain(
fst.args({
"cubic_box_length":
str((args.num / args.density)**(1. / 3.))
})), fst.args({"particle_type": args.data})))
mc.add(fst.MakePotential(fst.MakeLennardJones()))
mc.add(fst.MakePotential(fst.MakeLongRangeCorrections()))
mc.set(fst.MakeThermoParams(fst.args({"beta": str(args.beta)})))
mc.set(fst.MakeMetropolis())
mc.add(
fst.MakeTrialTranslate(
fst.args({
"tunable_param": "0.2",
"tunable_target_acceptance": "0.2"
})))
mc.add(fst.MakeTrialAdd(fst.args({"particle_type": "0"})))
mc.run(fst.MakeRun(fst.args({"until_num_particles": str(args.num)})))
mc.run(fst.MakeRemoveTrial(fst.args({"name": "TrialAdd"})))
steps_per = str(int(1e5))
mc.add(
fst.MakeCheckEnergyAndTune(
fst.args({
"steps_per": steps_per,
"tolerance": "1e-8"
import feasst
monte_carlo = feasst.MonteCarlo()
monte_carlo.set(feasst.MakeRandomMT19937(feasst.args({"seed": "time"})))
monte_carlo.add(
feasst.Configuration(
feasst.MakeDomain(feasst.args({"cubic_box_length": "8"})),
feasst.args(
{"particle_type": feasst.install_dir() + "/forcefield/data.lj"})))
monte_carlo.add(feasst.Potential(feasst.MakeLennardJones()))
monte_carlo.add(feasst.Potential(feasst.MakeLongRangeCorrections()))
monte_carlo.add(feasst.MakeMetropolis(feasst.args({"beta": "1.5"})))
monte_carlo.add(
feasst.MakeTrialTranslate(
feasst.args({
"tunable_param": "2.",
"tunable_target_acceptance": "0.2"
})))
steps_per = int(1e3)
monte_carlo.add(feasst.MakeTuner(feasst.args({"steps_per": str(steps_per)})))
feasst.SeekNumParticles(50)\
.with_metropolis(feasst.args({"beta": "0.1", "chemical_potential": "10"}))\
.with_trial_add().run(monte_carlo)
monte_carlo.add(feasst.MakeLog(feasst.args({"steps_per": str(steps_per)})))
monte_carlo.add(
feasst.MakeMovie(
feasst.args({
"steps_per": str(steps_per),
"file_name": "movie.xyz"
})))
monte_carlo.add(