def test_two_particle(self):
"""Test the LJ potential against analytical calculation of two particles"""
params = {"displacement": 1.2345}
with open("two.xyz", "w") as myfile:
myfile.write("""2
-1 8 8 8
0 0 0 0
1 0 0 {displacement}""".format(**params))
run_fst({
"config_params":
"particle_type0 /feasst/forcefield/lj.fstprt xyz_file two.xyz"
})
mc = fst.MonteCarlo().deserialize(
pyfeasst.read_checkpoint("checkpoint.fst"))
self.assertEqual(mc.configuration().num_particles(), 2)
# compute the expected analytical LJ and LRC energies
enlj = 8 * (params["displacement"]**(-12) -
params["displacement"]**(-6))
rcut = mc.system().configuration().model_params().select(
"cutoff").value(0)
enlrc = (8./3.)*fst.PI*mc.system().configuration().num_particles()**2/ \
mc.configuration().domain().volume()*((1./3.)*rcut**(-9) - rcut**(-3))
# Compare the analytical results with the FEASST computed energies.
# The energies of the individual potentials (e.g., LJ and LRC) are stored as profiles with
# indices based on the order that the potentials were initialized.
# Thus, profile index 0 refers to LJ while 1 refers to LRC.
# In addition, the last computed value of the energy of all potentials is also stored.
self.assertAlmostEqual(enlj,
mc.system().stored_energy_profile()[0], 15)
self.assertAlmostEqual(enlrc,
mc.system().stored_energy_profile()[1], 15)
self.assertAlmostEqual(enlj + enlrc, mc.system().stored_energy(), 15)
def test_srsw_ref_config(self):
"""Test the LJ potential against a configuration of 30 particles.
In particular, the 4th configuration of the LJ SRSW reference:
https://www.nist.gov/mml/csd/chemical-informatics-research-group/lennard-jones-fluid-reference-calculations
"""
run_fst({
"config_params":
"cubic_box_length 8 particle_type0 /feasst/forcefield/lj.fstprt \
xyz_file /feasst/plugin/configuration/test/data/lj_sample_config_periodic4.xyz"
})
mc = fst.MonteCarlo().deserialize(
pyfeasst.read_checkpoint("checkpoint.fst"))
self.assertEqual(mc.configuration().num_particles(), 30)
enlj = -16.790321304625856
enlrc = -0.5451660014945704
self.assertEqual(enlj, mc.system().stored_energy_profile()[0], 15)
self.assertAlmostEqual(enlrc,
mc.system().stored_energy_profile()[1], 15)
self.assertAlmostEqual(enlj + enlrc, mc.system().energy(), 15)
def test_srsw_ref_config_triclinic(self):
"""Test the LJ potential against a configuration of 300 particles in a trinclinic cell.
In particular, the 3th configuration of the triclinic LJ SRSW reference:
https://www.nist.gov/mml/csd/chemical-informatics-group/lennard-jones-fluid-reference-calculations-non-cuboid-cell
"""
run_fst({
"config_params":
"side_length0 10.0 side_length1 9.84807753012208 side_length2 9.64974312607518 \
xy 1.7364817766693041 xz 2.5881904510252074 yz 0.42863479791864567 \
particle_type0 /feasst/forcefield/lj.fstprt \
xyz_file /feasst/plugin/configuration/test/data/lj_triclinic_sample_config_periodic3.xyz"
})
mc = fst.MonteCarlo().deserialize(
pyfeasst.read_checkpoint("checkpoint.fst"))
self.assertEqual(mc.configuration().num_particles(), 300)
enlj = -505.78567945268367
enlrc = -29.37186430697248
self.assertEqual(enlj, mc.system().stored_energy_profile()[0], 15)
self.assertAlmostEqual(enlrc,
mc.system().stored_energy_profile()[1], 15)
self.assertAlmostEqual(enlj + enlrc, mc.system().energy(), 15)
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
type=int,
help="number of trials for equilibration",
default=int(5e7))
parser.add_argument(
"--trials",
type=int,
help="total number of trials (equilibration and production)",
default=int(3e8))
parser.add_argument("--num_hours",
type=float,
help="number of hours before restart",
default=1.)
args = parser.parse_args()
print("args:", args)
mc = fst.MonteCarlo()
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)})))
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(
def mc(thread, mn, mx):
steps_per = str(int(1e5))
mc = fst.MakeMonteCarlo()
#mc.set(fst.MakeRandomMT19937(fst.args({"seed": "12345"})))
#mc.set(fst.MakeRandomMT19937(fst.args({"seed": "1634926410"})))
chi = 0.7 # patch coverage
patch_angle = 2 * math.asin(math.sqrt(chi / 2)) * 180 / math.pi
print('patch_angle', patch_angle)
mc = fst.MonteCarlo()
config = fst.MakeConfiguration(
fst.args({
"cubic_box_length":
"8",
"particle_type0":
fst.install_dir() +
"/plugin/patch/forcefield/two_patch_linear.fstprt"
}))
config.add(fst.MakeGroup(fst.args({"site_type0": "0"})))
mc.add(config)
mc.add(
fst.MakePotential(
fst.MakeHardSphere(),
fst.MakeVisitModelCell(
fst.args({
"min_length": "1",
"cell_group": "1"
})), fst.args({"group_index": "1"})))
patch = fst.MakeVisitModelInnerPatch(
fst.args({"patch_degrees_of_type1": str(patch_angle)}))
mc.add(
fst.MakePotential(
fst.MakeSquareWell(),
fst.MakeVisitModel(patch),
#fst.MakeVisitModelCell(patch, fst.args({"min_length": "1.5", "cell_group": "1"})),
fst.args({"group_index": "1"})))
mc.set(
fst.MakeThermoParams(
fst.args({
"beta": str(1. / args.temperature),
"chemical_potential": str(args.mu)
})))
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.min_sweeps)})),
fst.MakeWLTM(
fst.args({
"collect_flatness": "18",
"min_flatness": "22",
"min_sweeps": str(args.min_sweeps)
}))))
mc.add(
fst.MakeTrialTranslate(
fst.args({
"weight": "0.5",
"tunable_param": "0.1"
})))
mc.add(
fst.MakeTrialRotate(fst.args({
"weight": "0.5",
"tunable_param": "20."
})))
mc.add(
fst.MakeTrialTransfer(fst.args({
"weight": "4",
"particle_type": "0"
})))
# mc.add(fst.MakeTrialGrow(fst.ArgsVector([{"translate": "true", "site": "0", "tunable_param": "1"}]),
# {"reference_index": ref, "num_steps": num_steps}))
# mc.add(fst.MakeTrialGrow(fst.ArgsVector([{"transfer": "true", "site": "0", "weight": "4"}]),
# {"reference_index": ref, "num_steps": num_steps}))
mc.add(
fst.MakeCheckEnergy(
fst.args({
"steps_per": steps_per,
"tolerance": "0.0001"
})))
mc.add(
fst.MakeTune(
fst.args({
"steps_per": steps_per,
"stop_after_phase": "0"
})))
mc.add(
fst.MakeLog(
fst.args({
"steps_per": steps_per,
"file_name": "clones" + str(thread) + ".txt",
"file_name_append_phase": "True"
})))
mc.add(
fst.MakeMoviePatch(
fst.args({
"steps_per": steps_per,
"file_name": "clones" + str(thread) + ".xyz",
"file_name_append_phase": "True"
})))
mc.add(
fst.MakeEnergy(
fst.args({
"steps_per_write": steps_per,
"file_name": "en" + str(thread) + ".txt",
"file_name_append_phase": "True",
"start_after_phase": "0",
"multistate": "True"
})))
mc.add(fst.MakeCriteriaUpdater(fst.args({"steps_per": steps_per})))
mc.add(
fst.MakeCriteriaWriter(
fst.args({
"steps_per": 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":
str(0.1 * args.num_procs * args.num_hours),
"num_hours_terminate":
str(0.9 * args.num_procs * args.num_hours)
})))
return mc
def test_srsw_alt(self,
num_particles=500,
density=0.001,
steps_per=1e5,
beta=1. / 0.9,
fstprt=fst.install_dir() + "/forcefield/lj.fstprt",
num_equil=1e7,
num_prod=1e7):
"""Compare with the reported average energy from the NIST SRSW.
https://mmlapps.nist.gov/srs/LJ_PURE/mc.htm
https://www.nist.gov/programs-projects/nist-standard-reference-simulation-website
num_particles -- number of LJ particles
density -- number density
steps_per -- steps between each Anaylze/Modify
"""
params = {"box_length": (num_particles / density)**(1. / 3.)}
params = dict(locals(), **params)
with open('tutorial_1_lj_nvt.txt', 'w') as fsttxt:
fsttxt.write("""
Checkpoint file_name checkpoint.fst
RandomMT19937 seed time
Configuration cubic_box_length {box_length} particle_type {fstprt}
Potential Model LennardJones
Potential VisitModel LongRangeCorrections
ThermoParams beta 0.1 chemical_potential 10
Metropolis
TrialTranslate tunable_param 2. tunable_target_acceptance 0.2
TrialAdd particle_type 0
Run until_num_particles {num_particles}
RemoveTrial name TrialAdd
ThermoParams beta {beta}
Tune steps_per {steps_per}
CheckEnergy steps_per {steps_per} tolerance 1e-8
# equilibrate
Run num_attempts {num_equil}
RemoveModify name Tune
# production analysis and output
Log steps_per {steps_per} file_name lj.txt
Energy steps_per_write {steps_per} file_name en.txt
# Run num_attempts {num_prod} # optionally run production here instead of python
WriteCheckpoint
""".format(**params))
import pyfeasst
syscode = subprocess.call(
fst.install_dir() +
"/build/bin/fst < tutorial_1_lj_nvt.txt >> launch.log",
shell=True,
executable='/bin/bash')
if syscode > 0: sys.exit(1)
mc = fst.MonteCarlo().deserialize(
pyfeasst.read_checkpoint('checkpoint.fst'))
# run production trials with an alternative running average of the energy
# this demonstrates custom on-the-fly analysis in python scripts.
energy_alt = fst.Accumulator()
for _ in range(int(params["num_prod"])):
mc.attempt(1)
energy_alt.accumulate(mc.criteria().current_energy())
energy = fst.SeekAnalyze().reference("Energy", mc).accumulator()
# test that the two methods for computing the energy give the same result
self.assertAlmostEqual(energy.average(),
energy_alt.average(),
delta=1e-6)
# test the average against the NIST SRSW
stdev = (energy.block_stdev()**2 + (1.89E-05)**2)**(1. / 2.)
self.assertAlmostEqual(-9.9165E-03 * num_particles,
energy.average(),
delta=2.576 * stdev)
