def init_BC():
conc_args = {
"conc_ratio_min_1": [[64, 0, 0]],
"conc_ratio_max_1": [[24, 40, 0]],
"conc_ratio_min_2": [[64, 0, 0]],
"conc_ratio_max_2": [[22, 21, 21]]
}
orig_spin_dict = {"Mg": 1.0, "Si": -1.0, "Al": 0.0}
kwargs = {
"crystalstructure": "fcc",
"a": 4.05,
"size": [4, 4, 4],
"basis_elements": [["Mg", "Si", "Al"]],
"conc_args": conc_args,
"db_name": "data/almgsi.db",
"max_cluster_size": 4
}
ceBulk = BulkCrystal(**kwargs)
ceBulk.spin_dict = orig_spin_dict
ceBulk.basis_functions = ceBulk._get_basis_functions()
ceBulk._get_cluster_information()
eci_file = "data/almgsi_fcc_eci.json"
with open(eci_file, 'r') as infile:
ecis = json.load(infile)
print(ecis)
#calc = CE( ceBulk, ecis, size=(3,3,3) )
calc = get_ce_calc(ceBulk, kwargs, ecis, size=[10, 10, 10])
ceBulk = calc.BC
ceBulk.atoms.set_calculator(calc)
return ceBulk
def run(T, mg_conc, si_conc, precs):
conc_args = {
"conc_ratio_min_1": [[64, 0, 0]],
"conc_ratio_max_1": [[24, 40, 0]],
"conc_ratio_min_2": [[64, 0, 0]],
"conc_ratio_max_2": [[22, 21, 21]]
}
orig_spin_dict = {
"Mg": 1.0,
"Si": -1.0,
"Al": 0.0
}
kwargs = {
"crystalstructure": "fcc",
"a": 4.05,
"size": [4, 4, 4],
"basis_elements": [["Mg", "Si", "Al"]],
"conc_args": conc_args,
"db_name": "data/almgsi.db",
"max_cluster_size": 4
}
ceBulk = BulkCrystal(**kwargs)
ceBulk.spin_dict = orig_spin_dict
ceBulk.basis_functions = ceBulk._get_basis_functions()
ceBulk._get_cluster_information()
eci_file = "data/almgsi_fcc_eci.json"
with open(eci_file, 'r') as infile:
ecis = json.load(infile)
print(ecis)
#calc = CE( ceBulk, ecis, size=(3,3,3) )
calc = get_ce_calc(ceBulk, kwargs, ecis, size=[10, 10, 10])
ceBulk = calc.BC
ceBulk.atoms.set_calculator(calc)
comp = {
"Mg": mg_conc,
"Si": si_conc,
"Al": 1.0 - mg_conc - si_conc
}
calc.set_composition(comp)
for temp, prec in zip(T, precs):
print("Current temperature {}K".format(temp))
mc_obj = Montecarlo(ceBulk.atoms, temp, mpicomm=comm)
mode = "prec"
mc_obj.runMC(mode=mode, prec=prec)
thermo = mc_obj.get_thermodynamic()
thermo["temperature"] = temp
thermo["prec"] = prec
thermo["internal_energy"] = thermo.pop("energy")
thermo["converged"] = True
thermo["prec"] = prec
if (rank == 0):
db = dataset.connect("sqlite:///{}".format(mc_db_name))
tbl = db["results"]
thermo["sysID"] = sysID
tbl.insert(thermo)
def main():
conc_args = {
"conc_ratio_min_1": [[1, 0]],
"conc_ratio_max_1": [[0, 1]],
}
kwargs = {
"crystalstructure": "fcc",
"a": 4.05,
"size": [4, 4, 4],
"basis_elements": [["Al", "Mg"]],
"conc_args": conc_args,
"db_name": "data/temporary_bcnucleationdb.db",
"max_cluster_size": 4
}
ceBulk = BulkCrystal(**kwargs)
print(ceBulk.basis_functions)
eci_file = "data/ce_hydrostatic.json"
with open(eci_file, 'r') as infile:
ecis = json.load(infile)
print(ecis)
#calc = CE( ceBulk, ecis, size=(3,3,3) )
calc = get_ce_calc(ceBulk,
kwargs,
ecis,
size=[10, 10, 10],
free_unused_arrays_BC=True)
ceBulk = calc.BC
ceBulk.atoms.set_calculator(calc)
T = 500
temps = [200, 300, 400, 500, 600, 700, 800]
mg_concs = [
0.005, 0.01, 0.025, 0.05, 0.075, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4,
0.45, 0.5, 0.55, 0.6, 0.65, 0.70, 0.75, 0.8, 0.85, 0.9
]
mg_concs = np.linspace(0.005, 0.995, 100)
act_coeffs = []
eff_conc = []
for T in temps:
for c_mg in mg_concs:
comp = {"Mg": c_mg, "Al": 1.0 - c_mg}
calc.set_composition(comp)
act_sampler = ActivitySampler(ceBulk.atoms,
T,
moves=[("Al", "Mg")],
mpicomm=comm)
act_sampler.runMC(mode="fixed", steps=100000)
thermo = act_sampler.get_thermodynamic()
act_sampler.save(
fname="data/effective_concentration_full_range.db")
def main():
conc_args = {
"conc_ratio_min_1": [[1, 0]],
"conc_ratio_max_1": [[0, 1]],
}
#with open(bc_filename,'rb') as infile:
# ceBulk = pck.load(infile)
kwargs = {
"crystalstructure": "fcc",
"a": 4.05,
"size": [4, 4, 4],
"basis_elements": [["Al", "Mg"]],
"conc_args": conc_args,
"db_name": "dos-db.db",
"max_cluster_size": 4
}
ceBulk = BulkCrystal(**kwargs)
print(ceBulk.basis_functions)
eci_file = "data/ce_hydrostatic.json"
with open(eci_file, 'r') as infile:
ecis = json.load(infile)
print(ecis)
#calc = CE( ceBulk, ecis, size=(3,3,3) )
calc = get_ce_calc(ceBulk, kwargs, ecis, size=[10, 10, 10])
ceBulk = calc.BC
ceBulk.atoms.set_calculator(calc)
print("Number of atoms: {}".format(len(ceBulk.atoms)))
T = 300
dos = SGCCompositionFreeEnergy(nbins=10000,
hist_limits=[("c1_0", 0.45, 1.0)])
mc = sgc.SGCMonteCarlo(ceBulk.atoms, T, symbols=["Al", "Mg"])
mc.chemical_potential = {"c1_0": -1.066}
if (run_array):
T = [
150, 200, 250, 300, 350, 375, 390, 400, 410, 420, 430, 440, 450,
460, 470
]
mus = np.linspace(-1.064, -1.069, 10)
mu = [{"c1_0": value} for value in mus]
array_runner = FreeEnergyMuTempArray(
T, mu, fname="data/free_energy_al_rich.h5")
array_runner.run(mc, dos, min_num_steps=100000)
else:
comp_free_eng = dos.find_dos(mc,
min_num_steps=500000,
max_rel_unc=0.01)
comp_free_eng.plot()
plt.show()
def run(maxT, minT, n_temp, mg_conc):
T = np.linspace(minT, maxT, n_temp)[::-1]
conc_args = {
"conc_ratio_min_1": [[1, 0]],
"conc_ratio_max_1": [[0, 1]],
}
kwargs = {
"crystalstructure": "fcc",
"a": 4.05,
"size": [4, 4, 4],
"basis_elements": [["Al", "Mg"]],
"conc_args": conc_args,
"db_name": "data/temporary_bcdb.db",
"max_cluster_size": 4
}
ceBulk = BulkCrystal(**kwargs)
print(ceBulk.basis_functions)
eci_file = "data/ce_hydrostatic.json"
with open(eci_file, 'r') as infile:
ecis = json.load(infile)
print(ecis)
#calc = CE( ceBulk, ecis, size=(3,3,3) )
calc = get_ce_calc(ceBulk, kwargs, ecis, size=[10, 10, 10])
ceBulk = calc.BC
ceBulk.atoms.set_calculator(calc)
comp = {"Al": 1.0 - mg_conc, "Mg": mg_conc}
calc.set_composition(comp)
print("Number of atoms: {}".format(len(ceBulk.atoms)))
for temp in T:
print("Current temperature {}K".format(temp))
mc_obj = Montecarlo(ceBulk.atoms, temp, mpicomm=comm)
mode = "prec"
prec = 1E-5
mc_obj.runMC(mode=mode, prec=prec)
thermo = mc_obj.get_thermodynamic()
thermo["temperature"] = temp
thermo["prec"] = prec
thermo["internal_energy"] = thermo.pop("energy")
thermo["converged"] = True
if (rank == 0):
db = connect(mc_db_name)
db.write(ceBulk.atoms, key_value_pairs=thermo)
def main():
conc_args = {
"conc_ratio_min_1": [[1, 0]],
"conc_ratio_max_1": [[0, 1]],
}
kwargs = {
"crystalstructure": "fcc",
"a": 4.05,
"size": [4, 4, 4],
"basis_elements": [["Al", "Mg"]],
"conc_args": conc_args,
"db_name": "data/temporary_bcnucleationdb.db",
"max_cluster_size": 4
}
ceBulk = BulkCrystal(**kwargs)
print(ceBulk.basis_functions)
eci_file = "data/ce_hydrostatic.json"
with open(eci_file, 'r') as infile:
ecis = json.load(infile)
print(ecis)
#calc = CE( ceBulk, ecis, size=(3,3,3) )
calc = get_ce_calc(ceBulk,
kwargs,
ecis,
size=[10, 10, 10],
free_unused_arrays_BC=True)
ceBulk = calc.BC
ceBulk.atoms.set_calculator(calc)
chem_pot = {"c1_0": -1.069}
T = 300
#mc = SGCFreeEnergyBarrier( ceBulk.atoms, T, symbols=["Al","Mg"], \
#n_windows=20, n_bins=10, min_singlet=0.5, max_singlet=1.0, mpicomm=comm )
mc = SGCFreeEnergyBarrier.load(ceBulk.atoms,
"data/free_energy_barrier_restart.json",
mpicomm=comm)
#mc.run( nsteps=100000, chem_pot=chem_pot )
#mc.save( fname="data/free_energy_barrier_restart.json" )
mc.plot(fname="data/free_energy_barrier_restart.json")
plt.show()
def main():
chem_pots = {"c1_0": -1.067}
conc_args = {
"conc_ratio_min_1": [[1, 0]],
"conc_ratio_max_1": [[0, 1]],
}
#with open(bc_filename,'rb') as infile:
# ceBulk = pck.load(infile)
kwargs = {
"crystalstructure": "fcc",
"a": 4.05,
"size": [4, 4, 4],
"basis_elements": [["Al", "Mg"]],
"conc_args": conc_args,
"db_name": "random_test.db",
"max_cluster_size": 4
}
ceBulk = BulkCrystal(**kwargs)
print(ceBulk.basis_functions)
eci_file = "../data/ce_hydrostatic.json"
with open(eci_file, 'r') as infile:
ecis = json.load(infile)
print(ecis)
#calc = CE( ceBulk, ecis, size=(3,3,3) )
calc = get_ce_calc(ceBulk, kwargs, ecis, size=[10, 10, 10])
ceBulk = calc.BC
ceBulk.atoms.set_calculator(calc)
print("Number of atoms: {}".format(len(ceBulk.atoms)))
mc = SGCMonteCarlo(ceBulk.atoms, 100000, symbols=["Al", "Mg"])
mc.chemical_potential = chem_pots
dn_sampler = DNSampler(mc_sampler=mc)
dnest4_args = {"max_num_levels": 50, "lam": 10}
dn_sampler.run(dnest4_args=dnest4_args)
dn_sampler.make_histograms(bins=100)
plt.show()
def run(T,mg_conc):
conc_args = {
"conc_ratio_min_1":[[1,0]],
"conc_ratio_max_1":[[0,1]],
}
kwargs = {
"crystalstructure":"fcc", "a":4.05, "size":[4,4,4], "basis_elements":[["Al","Mg"]],
"conc_args":conc_args, "db_name":"data/temporary_bcdb.db",
"max_cluster_size":4
}
ceBulk = BulkCrystal( **kwargs )
print (ceBulk.basis_functions)
eci_file = "data/ce_hydrostatic.json"
with open( eci_file, 'r' ) as infile:
ecis = json.load( infile )
print (ecis)
#calc = CE( ceBulk, ecis, size=(3,3,3) )
calc = get_ce_calc( ceBulk, kwargs, ecis, size=[10,10,10], free_unused_arrays_BC=True )
ceBulk = calc.BC
ceBulk.atoms.set_calculator( calc )
comp = {
"Al":1.0-mg_conc,
"Mg":mg_conc
}
calc.set_composition(comp)
mc_obj = Montecarlo( ceBulk.atoms, T, mpicomm=comm )
pairs = PairCorrelationObserver( calc )
network = NetworkObserver( calc=calc, cluster_name="c2_1414_1", element="Mg", nbins=100 )
mode = "fixed"
camera = Snapshot( "data/precipitation.traj", atoms=ceBulk.atoms )
mc_obj.attach( camera, interval=10000 )
mc_obj.attach( pairs, interval=1 )
mc_obj.attach( network, interval=500 )
mc_obj.runMC( mode=mode, steps=10000, equil=True )
pair_mean = pairs.get_average()
pair_std = pairs.get_std()
if ( rank == 0 ):
data = {
"pairs":pair_mean,
"pairs_std":pair_std,
"mg_conc":mg_conc,
"temperature":T
}
pairfname = "data/precipitation_pairs/precipitation_pairs_{}_{}K.json".format(int(1000*mg_conc),int(T))
with open(pairfname,'w') as outfile:
json.dump(data,outfile,indent=2, separators=(",",":"))
print ( "Thermal averaged pair correlation functions written to {}".format(pairfname) )
atoms = network.get_atoms_with_largest_cluster()
data = network.get_statistics() # This collects the histogram data from all processors
size,occurence = network.get_size_histogram()
data["histogram"] = {}
data["histogram"]["size"] = size.tolist()
data["histogram"]["occurence"] = occurence.tolist()
data["temperature"] = T
data["mg_conc"] = mg_conc
cluster_fname = "data/cluster_statistics_{}_{}K.json".format(int(1000*mg_conc),int(T))
atoms_fname = "data/largest_cluster_{}_{}K.cif".format( int(1000*mg_conc), int(T) )
if ( rank == 0 ):
print (occurence)
if ( rank == 0 ):
try:
with open( cluster_fname,'r') as infile:
data = json.load(infile)
old_size = np.array(data["histogram"]["size"])
old_hist = np.array(data["histogram"]["occurence"])
if ( np.allclose(old_size,size) ):
occurence += old_hist
data["histogram"]["occurence"] = occurence.tolist()
except Exception as exc:
print (str(exc))
with open( cluster_fname, 'w' ) as outfile:
json.dump( data, outfile, indent=2, separators=(",",":") )
print ("Cluster statistics written to {}".format(cluster_fname) )
write( atoms_fname, atoms )
print ("Atoms with largest cluster written to {}".format(atoms_fname))
#view(atoms)
#plt.plot( network.size_histogram, ls="steps")
#plt.show()
print ("Proc: {} reached final barrier".format(rank))
comm.barrier()
def run(mg_conc):
bs_kwargs = {
"conc_args": {
"conc_ratio_min_1": [[1, 0]],
"conc_ratio_max_1": [[0, 1]]
},
"basis_elements": [["Al", "Mg"], ["Al", "Mg"], ["Al", "Mg"],
["Al", "Mg"]],
"cellpar": [10.553, 10.553, 10.553, 90, 90, 90],
"basis": [(0, 0, 0), (0.324, 0.324, 0.324), (0.3582, 0.3582, 0.0393),
(0.0954, 0.0954, 0.2725)],
"spacegroup":
217,
"max_cluster_size":
4,
"db_name":
"trial_217.db",
"size": [1, 1, 1],
"grouped_basis": [[0, 1, 2, 3]]
}
bs = BulkSpacegroup(**bs_kwargs)
eci_file = "data/almg_217_eci.json"
with open(eci_file, 'r') as infile:
ecis = json.load(infile)
calc = get_ce_calc(bs, bs_kwargs, ecis, size=[3, 3, 3])
bs = calc.BC
bs.atoms.set_calculator(calc)
comp = {"Al": 1.0 - mg_conc, "Mg": mg_conc}
calc.set_composition(comp)
print("Number of atoms: {}".format(len(bs.atoms)))
high_temps = [10000, 9000, 8000, 7000, 6000, 5000, 4000, 3000, 2000, 1000]
low_temps = range(200, 1000, 50)[::-1]
T = np.array(high_temps + low_temps)
#T = np.array([10000,9000,8000,7000,6000,5000,4000,3000,2000,1000,800,700,600,500,400,375,350,325,300,275,250,225,200,175,150])
#T = np.array([1E6,100000])
precs = np.zeros(len(T)) + 1E-4
#precs[T<=500] = 1E-5
print(bs.atoms.get_chemical_formula())
mc_obj = Montecarlo(bs.atoms, T[0], mpicomm=comm)
mc_obj.accept_first_trial_move_after_reset = False
for prec, temp in zip(precs, T):
mc_obj.T = temp
mc_obj.reset()
#if ( temp==T[0] ):
# mc_obj.is_first = False # Do not accept the first move
print("Current temperature {}K".format(temp))
mode = "fixed"
mc_obj.runMC(mode=mode, prec=prec, steps=1000000)
thermo = mc_obj.get_thermodynamic()
thermo["temperature"] = temp
thermo["prec"] = prec
thermo["internal_energy"] = thermo.pop("energy")
thermo["converged"] = True
if (rank == 0):
cf = calc.get_cf()
db = connect(mc_db_name)
thermo.update(cf)
db.write(bs.atoms, key_value_pairs=thermo)
if (rank == 0 and run_mfa):
# At this point a ground state should have been reached
mfa = CanonicalMeanField(atoms=bs.atoms, T=T)
mfa.relax()
res = mfa.calculate()
formula = bs.atoms.get_chemical_formula()
fname = "data/mfa217/{}mfa.json".format(formula)
with open(fname, 'w') as outfile:
json.dump(res, outfile, indent=2, separators=(",", ":"))
def run(mu, temps, save=False):
chem_pots = {"c1_0": mu}
linvib = LinearVibCorrection(vib_eci)
conc_args = {
"conc_ratio_min_1": [[1, 0]],
"conc_ratio_max_1": [[0, 1]],
}
#with open(bc_filename,'rb') as infile:
# ceBulk = pck.load(infile)
kwargs = {
"crystalstructure": "fcc",
"a": 4.05,
"size": [4, 4, 4],
"basis_elements": [["Al", "Mg"]],
"conc_args": conc_args,
"db_name": "random_test.db",
"max_cluster_size": 4
}
ceBulk = BulkCrystal(**kwargs)
print(ceBulk.basis_functions)
eci_file = "data/ce_hydrostatic.json"
with open(eci_file, 'r') as infile:
ecis = json.load(infile)
print(ecis)
#calc = CE( ceBulk, ecis, size=(3,3,3) )
calc = get_ce_calc(ceBulk, kwargs, ecis, size=[10, 10, 10])
ceBulk = calc.BC
ceBulk.atoms.set_calculator(calc)
print("Number of atoms: {}".format(len(ceBulk.atoms)))
#view(ceBulk.atoms)
#exit()
n_burn = 40000
n_sample = 10000
thermo = []
#size = comm.Get_size()
#n_per_proc = int( len(temps)/size )
#if ( rank == comm.Get_size()-1 ):
# my_temps = temps[n_per_proc*rank:]
#else:
# my_temps = temps[n_per_proc*rank:n_per_proc*(rank+1)]
for T in temps:
if (rank == 0):
print("{}: Current temperature {}".format(rank, T))
mc = sgc.SGCMonteCarlo(ceBulk.atoms,
T,
symbols=["Al", "Mg"],
mpicomm=comm)
#mc.linear_vib_correction = linvib
mc.runMC(steps=2000000, chem_potential=chem_pots, equil=False)
exit()
mc.runMC(mode="prec", chem_potential=chem_pots, prec=1E-4)
if (rank == 0):
print(mc.atoms._calc.eci["c1_0"])
thermo_properties = mc.get_thermodynamic()
thermo.append(thermo_properties)
mc.reset()
n_unreachable = gc.collect()
print("GC number of unreachable: {}".format(n_unreachable))
#all_thermos = []
#all_thermos = comm.gather( thermo, root=0 )
if ((rank == 0) and save):
#thermo = []
#for sublist in all_thermos:
# thermo += sublist
name = "mu%d" % (int(abs(mu) * 10000))
if (mu < 0.0):
name += "m"
data_written_to_file = False
data = {}
try:
with open(OUTFILE, 'r') as infile:
data = json.load(infile)
if (name in data.keys()):
for i, entry in enumerate(thermo):
for key, value in entry.iteritems():
data[name][key].append(value)
#data[name]["singlets"].append( entry["singlets"][0] )
#data[name]["energy"].append( entry["energy"] )
#data[name]["heat_capacity"].append( entry["heat_capacity"] )
#data[name]["temperature"].append( entry["temperature"] )
data_written_to_file = True
except Exception as exc:
print(str(exc))
print("Could not load file! Creating a new one!")
if (not data_written_to_file):
data[name] = {}
for key in thermo[0].keys():
for entry in thermo:
data[name][key] = [entry[key] for entry in thermo]
#data[name]["singlets"] = [entry["singlets"][0] for entry in thermo]
#data[name]["temperature"] = list(temps)
#data[name]["energy"] = [entry["energy"] for entry in thermo]
#data[name]["heat_capacity"] = [entry["heat_capacity"] for entry in thermo]
#data[name]["mu"] = mu
with open(OUTFILE, 'w') as outfile:
json.dump(data,
outfile,
sort_keys=True,
indent=2,
separators=(",", ":"))
print("Data written to {}".format(OUTFILE))
if (comm.Get_rank() == 0):
print(ceBulk.atoms.get_chemical_formula())
#view( ceBulk.atoms )
ceBulk.atoms.set_calculator(None)
if (rank == 0):
with open(pck_file, 'wb') as outfile:
pck.dump((ceBulk, calc.get_cf(), calc.eci), outfile)
print("Bulk crystal object pickled to {}".format(pck_file))