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 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(
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()
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(
feasst.MakeCheckEnergy(
feasst.args({
"steps_per": str(steps_per),
"tolerance": "1e-8"
})))
monte_carlo.attempt(int(1e5))