def sa(al_comp, kwargs, eci, db_name, size, lattice):
comp = {"Al": al_comp, "Zn": 1.0 - al_comp}
bc = BulkCrystal(**kwargs)
calc = get_ce_calc(bc, kwargs, eci, size)
bc = calc.BC
bc.atoms.set_calculator(calc)
temperatures = [800, 700, 600, 500, 400, 300, 200, 100]
N = len(bc.atoms)
if rank == 0:
print("Supercell has {} atoms".format(N))
# Define parameters for equillibration
equil_params = {"maxiter": 10 * N, "mode": "fixed"}
nsteps = 100 * N
calc.set_composition(comp)
for T in temperatures:
mc = Montecarlo(bc.atoms, T, mpicomm=comm)
mc.runMC(mode="fixed", steps=nsteps, equil_params=equil_params)
thermo = mc.get_thermodynamic()
thermo["converged"] = True
thermo["al_conc"] = al_comp
thermo["temperature"] = T
if rank == 0:
db = dataset.connect("sqlite:///{}".format(db_name))
tbl = db["results"]
tbl.insert(thermo)
if rank == 0:
fname = "data/atoms_{}_{}.xyz".format(lattice,
bc.atoms.get_chemical_formula())
write(fname, bc.atoms)
def pure_phase_entropy(small_bc):
from cemc.mcmc import Montecarlo
from ase.clease.tools import wrap_and_sort_by_position
from ase.io import read
from cemc.mcmc import SiteOrderParameter, Snapshot
import dataset
T = [1, 5, 10, 20, 30, 40, 50, 75, 100, 150, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310,
320, 330, 340, 350, 360, 370, 380, 390, 400, 420, 440, 460, 480, 500]
calc = get_ce_calc(small_bc, kwargs, ecis, size=[10, 10, 10], db_name="data/db10x10x10Al3Mg.db")
bc = calc.BC
bc.atoms.set_calculator(calc)
atoms = read("data/al3mg_template.xyz")
atoms = wrap_and_sort_by_position(atoms)
symbs = [atom.symbol for atom in atoms]
calc.set_symbols(symbs)
site_order = SiteOrderParameter(bc.atoms)
db = dataset.connect("sqlite:///data/pure_al3mg3.db")
syst = db["thermodynamic"]
camera = Snapshot(trajfile="data/pure_phase_entropy.traj", atoms=bc.atoms)
for temp in T:
print("Current temperature: {}K".format(temp))
site_order.reset()
mc = Montecarlo(bc.atoms, temp)
mc.attach(site_order)
mc.attach(camera, interval=50000)
equil_param = {"window_length": 100000, "mode": "fixed"}
mc.runMC(mode="fixed", steps=500000, equil=True, equil_params=equil_param)
mean, stddev = site_order.get_average()
thermo = mc.get_thermodynamic()
thermo["site_order"] = mean
thermo["site_order_std"] = stddev
syst.insert(thermo)
def main():
energies = []
sizes = list(range(1, 6))
atoms = get_atoms()
two_atoms(atoms)
exit()
insert_np(6, atoms)
mc = Montecarlo(atoms, 0.1)
camera = Snapshot(atoms=mc.atoms, trajfile="/work/sophus/nuc_cluster.traj")
db = dataset.connect("sqlite:////work/sophus/mgsi_nuc_barrier_kamijo.db")
tbl = db["systems"]
mc.attach(camera, interval=100 * len(atoms))
num_mg = sum(1 for atom in atoms if atom.symbol == "Mg")
while num_mg > 2:
print(atoms.get_chemical_formula())
mc.runMC(mode="fixed", equil=False, steps=100 * len(atoms))
thermo = mc.get_thermodynamic()
tbl.insert(thermo)
# Remove one Mg atom and one Si atom
symbols = [a.symbol for a in atoms]
for i in range(20):
i = symbols.index("Si")
symbols[i] = "Al"
i = symbols.index("Mg")
symbols[i] = "Al"
mc.set_symbols(symbols)
num_mg = sum(1 for atom in atoms if atom.symbol == "Mg")
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 get_thermodynamic(self):
"""
Override the thermodynamics function.
Normalization of the averagers:
The values in average track should represent the ratio
between partition functions when one have one extra of the
inserted atom and one less of the removed atom.
In the high temperature limit this is given by
Z_{n+1} = N!/((n+1)!(N-n-1)!)
and
Z_n = N!/(n!(N-n)!)
where N is the overall number of atoms of the two atoms being swapped.
Hence, the ratio becomes
Z_{n+1}/Z_n = (N-n)/(n+1),
the sums are normalized such that they results in this value
at high temperatures.
:return: dict with the thermodynamical properties
"""
self.collect_results()
res = {}
res = Montecarlo.get_thermodynamic(self)
at_count = self.count_atoms()
# Inser the insertion enegies
for move in self.insertion_moves:
key = self.get_key(move[0], move[1])
name = "insert_energy_{}".format(key)
N = self.averager_track[key].num_samples
inf_temp = float(at_count[move[0]]) / (at_count[move[1]] + 1)
E0 = self.averager_track[key].ref_value
res[name] = E0 - kB * self.T * \
np.log(self.averager_track[key].average * inf_temp)
name = "raw_insert_energy_{}".format(key)
res[name] = self.raw_insertion_energy[key] / N
name = "boltzmann_avg_insert_energy_{}".format(key)
res[name] = self.boltzmann_weight_ins_energy[key].average / \
self.averager_track[key].average
name = "boltzmann_avg_insert_energy_sq_{}".format(key)
res[name] = self.boltzmann_weight_ins_eng_sq[key].average / \
self.averager_track[key].average
return res
def _estimate_internal_energy(self, conc, sweeps=2):
"""
Estimates the internal energy of one structure
"""
try:
self.atoms._calc.set_composition(conc)
mc = Montecarlo(self.atoms, self.temperature)
mc.runMC(mode="fixed", steps=sweeps * len(self.atoms), equil=False)
energy = mc.get_thermodynamic()["energy"]
except TooFewElementsError as exc:
energy = 1.0
return energy
def get_gs(self,
BC,
ecis=None,
composition=None,
temps=None,
n_steps_per_temp=1000,
atoms=None):
"""
Computes the ground states
:param BC: Instance of *CEBulk* or *CECrystal* from ASE
:param ecis: Dictionary with the Effecitve Cluster Interactions
:param composition: Dictionary with compositions (i.e. {"Mg":0.2,"Al":0.8})
:param temps: List of cooling temperatures
:param n_steps_per_temp: Number of MC steps per temperature
"""
if atoms is None:
atoms = BC.atoms.copy()
if atoms.get_calculator() is None:
if ecis is None:
raise ValueError("When a calculator is not attached "
"the ECIs has to be given!")
calc = CE(atoms, BC, ecis)
else:
calc = atoms.get_calculator()
#print (calc.get_cf())
if (temps is None):
temps = np.linspace(1, 1500, 30)[::-1]
if (composition is not None):
calc.set_composition(composition)
minimum_energy = LowestEnergyStructure(calc, None)
for T in temps:
print("Temperature {}".format(T))
mc_obj = Montecarlo(atoms, T)
mc_obj.constraints = self.constraints
minimum_energy.mc_obj = mc_obj
mc_obj.attach(minimum_energy)
mc_obj.runMC(steps=n_steps_per_temp, verbose=False, equil=False)
thermo = mc_obj.get_thermodynamic()
result = {
"atoms": minimum_energy.atoms,
"energy": minimum_energy.lowest_energy,
"cf": minimum_energy.lowest_energy_cf
}
return result
def sa(au_comp, kwargs, eci, db_name, size):
bc = CEBulk(**kwargs)
atoms = get_atoms_with_ce_calc(bc, kwargs, eci, size, db_name="cu-au_quad.db")
temperatures = [1500, 1400, 1300, 1200, 1100, 1000, 900, 800, 700, 600,
500, 400, 300, 200, 100, 50, 25, 10]
N = len(atoms)
gs = wrap_and_sort_by_position(read("data/atoms_Au250Cu750.xyz"))
symbs = [atom.symbol for atom in gs]
atoms.get_calculator().set_symbols(symbs)
print(atoms.get_calculator().get_energy())
exit()
# Define parameters for equillibration
equil_params = {
"maxiter": 10 * N,
"mode": "fixed"
}
nsteps = 200 * N
calc = atoms.get_calculator()
comp = {"Au": au_comp, "Cu": 1.0-au_comp}
calc.set_composition(comp)
energies = []
for T in temperatures:
mc = Montecarlo(atoms, T, accept_first_trial_move_after_reset=True)
energy_obs = EnergyEvolution(mc)
energies.append(energy_obs.energies)
mc.attach(energy_obs, interval=100)
mc.runMC(mode="fixed", steps=nsteps, equil_params=equil_params)
thermo = mc.get_thermodynamic()
thermo["converged"] = True
thermo["temperature"] = T
cf = calc.get_cf()
db = dataset.connect("sqlite:///{}".format(db_name))
tbl = db["results"]
uid = tbl.insert(thermo)
cf_tbl = db["corrfunc"]
cf["runID"] = uid
cf_tbl.insert(cf)
fname = "data/atoms_{}_{}.xyz".format(atoms.get_chemical_formula(), thermo['energy'])
write(fname, atoms)
np.savetxt("data/energy_evolution_{}.csv".format(atoms.get_chemical_formula()),
np.array(energies).T, delimiter=",")
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 perform_mc(uid):
from ase.io import read
from cemc.mcmc import Montecarlo
db = dataset.connect(PHASE_DIAG_DB)
tbl = db["simulation_plan"]
row = tbl.find_one(id=uid)
print(row)
temperatures = list(np.arange(1, 600, 50))
ceBulk = get_ce_with_calc()
swap_symbs = row["swap_new"].split("-")
current = 0
if row["phase"] == "mgsi":
atoms = read("data/ground_stateMgSi.xyz")
symbols = [atom.symbol for atom in atoms]
else:
symbols = ["Al" for _ in range(len(ceBulk.atoms))]
num_inserted = np.zeros(len(swap_symbs))
for i in range(len(symbols)):
if symbols[i] == row["swap_old"]:
symbols[i] = swap_symbs[current]
num_inserted[current] += 1
if num_inserted[-1] >= row["num_insert"]:
break
elif num_inserted[current] >= row["num_insert"]:
current += 1
elif num_inserted[current] >= row["num_insert"]:
current += 1
ceBulk.atoms.get_calculator().set_symbols(symbols)
result_tab = db["simulations"]
for T in temperatures:
print("Current temperature: {}".format(T))
mc = Montecarlo(ceBulk.atoms, T)
equil_params = {"mode": "fixed", "maxiter": 10000}
mc.runMC(steps=1000 * len(symbols), equil_params=equil_params)
thermo = mc.get_thermodynamic()
thermo["runID"] = uid
result_tab.insert(thermo)
def run(N, T):
bc = init_bc(N)
mc = Montecarlo(bc.atoms, T)
nanop = get_nanoparticle()
symbs = insert_nano_particle(bc.atoms.copy(), nanop)
mc.set_symbols(symbs)
order_param = SiteOrderParameter(mc.atoms)
nsteps = int(1E6)
equil_params = {"mode": "fixed", "window_length": int(1E5)}
mc.attach(order_param)
mc.runMC(steps=nsteps, equil=True, equil_params=equil_params)
thermo = mc.get_thermodynamic()
mean, std = order_param.get_average()
thermo["order_param_mean"] = mean
thermo["order_param_std"] = std
thermo.update(equil_params)
db = dataset.connect("sqlite:///{}".format(mc_db_name))
tbl = db["cluster_stability"]
tbl.insert(thermo)
fname = workdir + "/final_structure{}K.xyz".format(T)
write(fname, mc.atoms)
def sa(au_comp, kwargs, eci, db_name, size):
bc = CEBulk(**kwargs)
atoms = get_atoms_with_ce_calc(bc, kwargs, eci, size)
temperatures = [800, 700, 600, 500, 400, 300, 200, 100, 50, 25, 10]
N = len(bc.atoms)
# Define parameters for equillibration
equil_params = {
"maxiter": 10 * N,
"mode": "fixed"
}
comps = [0.05, 0.1, 0.15, 0.2, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95]
nsteps = 100 * N
calc = atoms.get_calculator()
for au_comp in comps:
comp = {
"Au": au_comp,
"Cu": 1.0-au_comp
}
calc.set_composition(comp)
for T in temperatures:
mc = Montecarlo(atoms, T)
mc.runMC(mode="fixed", steps=nsteps, equil_params=equil_params)
thermo = mc.get_thermodynamic()
thermo["converged"] = True
thermo["au_conc"] = au_comp
thermo["temperature"] = T
cf = calc.get_cf()
db = dataset.connect("sqlite:///{}".format(db_name))
tbl = db["results"]
uid = tbl.insert(thermo)
cf_tbl = db["corrfunc"]
cf["runID"] = uid
cf_tbl.insert(cf)
fname = "data/atoms_{}.xyz".format(atoms.get_chemical_formula())
write(fname, atoms)
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 thermodynamic_integration(phase):
alat = 3.8
conc_args = {}
conc_args['conc_ratio_min_1'] = [[1, 0]]
conc_args['conc_ratio_max_1'] = [[0, 1]]
kwargs = {
"crystalstructure": 'fcc',
"a": 3.8,
"size": [10, 10, 10],
"basis_elements": [['Cu', 'Au']],
"conc_args": conc_args,
"db_name": 'temp_sgc.db',
"max_cluster_size": 3,
"max_cluster_dist": 1.5 * alat
}
bc1 = BulkCrystal(**kwargs)
bf = bc1._get_basis_functions()[0]
with open("data/eci_aucu.json", 'r') as infile:
eci = json.load(infile)
db = dataset.connect("sqlite:///{}".format(canonical_db))
tbl = db["corrfunc"]
if phase == "Au" or phase == "Cu":
atoms1 = bc1.atoms
for atom in atoms1:
atom.symbol = phase
cf1 = get_pure_cf(eci, bf[phase])
else:
atoms = read(phase)
row = tbl.find_one(runID=gs[phase])
row.pop("id")
row.pop("runID")
cf1 = row
# TODO: Fix this when running with a pure phase
symbs = [atom.symbol for atom in atoms]
cf1 = get_pure_cf(eci, bf[bc1.atoms[0].symbol])
atoms = bc1.atoms
calc = CE(bc1, eci=eci, initial_cf=cf1)
calc.set_symbols(symbs)
atoms.set_calculator(calc)
sgc_db = dataset.connect("sqlite:///{}".format(sgc_db_name))
tbl = sgc_db["results"]
tbl_cf = sgc_db["corr_func"]
mu = 0.223
nsteps = 100 * len(atoms)
equil_param = {"mode": "fixed", "maxiter": 10 * len(atoms)}
order_param = SiteOrderParameter(atoms)
T = np.linspace(100, 600, 40)
# mu = np.linspace(0.223, 0.19, 20).tolist()
mu = [0.223]
for temp in T:
for m in mu:
chemical_potential = {"c1_0": m}
# mc = SGCMonteCarlo(atoms, temp, mpicomm=comm, symbols=["Au", "Cu"])
mc = Montecarlo(atoms, temp)
mc.attach(order_param)
init_formula = atoms.get_chemical_formula()
# mc.runMC(steps=nsteps, equil_params=equil_param,
# chem_potential=chemical_potential)
mc.runMC(steps=nsteps, equil_params=equil_param)
# thermo = mc.get_thermodynamic(reset_ecis=True)
thermo = mc.get_thermodynamic()
thermo["init_formula"] = init_formula
thermo["final_formula"] = atoms.get_chemical_formula()
avg, std = order_param.get_average()
thermo["order_avg"] = avg
thermo["order_std"] = std
thermo["valid"] = True
thermo["integration_path"] = "NN"
if rank == 0:
uid = tbl.insert(thermo)
cf = calc.get_cf()
cf["runID"] = uid
tbl_cf.insert(cf)