def setUp(self):
struct = Structure.from_str("""FCC Al
1.0
2.473329 0.000000 1.427977
0.824443 2.331877 1.427977
0.000000 0.000000 2.855955
Al
1
direct
0.000000 0.000000 0.000000 Al""",
fmt='POSCAR')
self.energies = [
-3.69150886, -3.70788383, -3.71997361, -3.72522301, -3.73569569,
-3.73649743, -3.74054982
]
self.volumes = [
14.824542034870653, 18.118887714656875, 15.373596786943025,
17.569833126580278, 15.92265868064787, 17.02077912220064,
16.471717630914863
]
self.eos = "vinet"
self.T = 500
self.qhda = QuasiharmonicDebyeApprox(self.energies,
self.volumes,
struct,
t_min=self.T,
t_max=self.T,
eos=self.eos,
anharmonic_contribution=True)
self.opt_vol = 17.216094889116807
class TestAnharmonicQuasiharmociDebyeApprox(unittest.TestCase):
def setUp(self):
struct = Structure.from_str("""FCC Al
1.0
2.473329 0.000000 1.427977
0.824443 2.331877 1.427977
0.000000 0.000000 2.855955
Al
1
direct
0.000000 0.000000 0.000000 Al""",
fmt='POSCAR')
self.energies = [
-3.69150886, -3.70788383, -3.71997361, -3.72522301, -3.73569569,
-3.73649743, -3.74054982
]
self.volumes = [
14.824542034870653, 18.118887714656875, 15.373596786943025,
17.569833126580278, 15.92265868064787, 17.02077912220064,
16.471717630914863
]
self.eos = "vinet"
self.T = 500
self.qhda = QuasiharmonicDebyeApprox(self.energies,
self.volumes,
struct,
t_min=self.T,
t_max=self.T,
eos=self.eos,
anharmonic_contribution=True)
self.opt_vol = 17.216094889116807
def test_optimum_volume(self):
opt_vol = self.qhda.optimum_volumes[0]
np.testing.assert_almost_equal(opt_vol, self.opt_vol, 3)
def test_debye_temperature(self):
theta = self.qhda.debye_temperature(self.opt_vol)
np.testing.assert_approx_equal(theta, 601.239096, 4)
def test_gruneisen_paramter(self):
gamma = self.qhda.gruneisen_parameter(0, self.qhda.ev_eos_fit.v0)
np.testing.assert_almost_equal(gamma, 2.188302, 3)
def test_thermal_conductivity(self):
kappa = self.qhda.thermal_conductivity(self.T, self.opt_vol)
np.testing.assert_almost_equal(kappa, 21.810997, 1)
def test_vibrational_internal_energy(self):
u = self.qhda.vibrational_internal_energy(self.T, self.opt_vol)
np.testing.assert_almost_equal(u, 0.13845, 3)
def test_vibrational_free_energy(self):
A = self.qhda.vibrational_free_energy(self.T, self.opt_vol)
np.testing.assert_almost_equal(A, -0.014620, 3)
class TestAnharmonicQuasiharmociDebyeApprox(unittest.TestCase):
def setUp(self):
struct = Structure.from_str("""FCC Al
1.0
2.473329 0.000000 1.427977
0.824443 2.331877 1.427977
0.000000 0.000000 2.855955
Al
1
direct
0.000000 0.000000 0.000000 Al""", fmt='POSCAR')
self.energies = [-3.69150886, -3.70788383, -3.71997361, -3.72522301,
-3.73569569, -3.73649743, -3.74054982]
self.volumes = [14.824542034870653, 18.118887714656875, 15.373596786943025,
17.569833126580278, 15.92265868064787, 17.02077912220064,
16.471717630914863]
self.eos = "vinet"
self.T = 500
self.qhda = QuasiharmonicDebyeApprox(self.energies, self.volumes, struct, t_min=self.T,
t_max=self.T, eos=self.eos, anharmonic_contribution=True)
self.opt_vol = 17.216094889116807
def test_optimum_volume(self):
opt_vol = self.qhda.optimum_volumes[0]
np.testing.assert_almost_equal(opt_vol, self.opt_vol, 3)
def test_debye_temperature(self):
theta = self.qhda.debye_temperature(self.opt_vol)
np.testing.assert_approx_equal(theta, 601.239096, 4 )
def test_gruneisen_paramter(self):
gamma = self.qhda.gruneisen_parameter(0, self.qhda.ev_eos_fit.v0)
np.testing.assert_almost_equal(gamma, 2.188302, 3)
def test_thermal_conductivity(self):
kappa = self.qhda.thermal_conductivity(self.T, self.opt_vol)
np.testing.assert_almost_equal(kappa, 21.810997, 1)
def test_vibrational_internal_energy(self):
u = self.qhda.vibrational_internal_energy(self.T, self.opt_vol)
np.testing.assert_almost_equal(u, 0.13845, 3)
def test_vibrational_free_energy(self):
A = self.qhda.vibrational_free_energy(self.T, self.opt_vol)
np.testing.assert_almost_equal(A, -0.014620, 3)
def setUp(self):
struct = Structure.from_dict({
'lattice': {'a': 2.5630200477817295,
'alpha': 59.999993839702206,
'b': 2.563020442699644,
'beta': 59.999988742674944,
'c': 2.56301993,
'gamma': 60.00000504373715,
'matrix': [[2.21964022, 0.0, 1.28151046],
[0.73987974, 2.09269747, 1.28151046],
[-0.0, -0.0, 2.56301993]],
'volume': 11.905318492097948},
'sites': [{'abc': [-0.0, -0.0, -0.0],
'label': 'B',
'species': [{'element': 'B', 'occu': 1}],
'xyz': [0.0, 0.0, 0.0]},
{'abc': [0.25, 0.25, 0.25],
'label': 'N',
'species': [{'element': 'N', 'occu': 1}],
'xyz': [0.73987999, 0.5231743675, 1.2815102125]}]})
self.energies = [-15.76315281, -16.11541813, -16.41784171, -16.47471523,
-16.63624155, -16.6741551, -16.78661144, -16.88768073,
-16.92450672, -17.04863261, -17.06126553, -17.15786866,
-17.19784976, -17.25078749, -17.30017149, -17.32578594,
-17.3708922, -17.38125127, -17.41231934, -17.41534352,
-17.42636644]
self.volumes = [8.678977833994137, 8.971505437031707, 9.27052889309282, 15.845976281427582,
15.417733609491387, 9.576127994353376, 14.997270631725604, 9.888370962140854,
14.584523227465766, 14.179424329180256, 10.20732378093211, 13.78189117535765,
10.533067462993838, 13.391864274742145, 10.865663655755416, 13.009260480347871,
11.205193091129587, 12.634015019827533, 11.551718049704352, 12.26606042141808,
11.90531496343142]
self.eos = "vinet"
self.T = 300
self.qhda = QuasiharmonicDebyeApprox(self.energies, self.volumes, struct, t_min=self.T,
t_max=self.T, eos=self.eos)
self.opt_vol = 11.957803302392925
def setUp(self):
struct = Structure.from_str("""FCC Al
1.0
2.473329 0.000000 1.427977
0.824443 2.331877 1.427977
0.000000 0.000000 2.855955
Al
1
direct
0.000000 0.000000 0.000000 Al""", fmt='POSCAR')
self.energies = [-3.69150886, -3.70788383, -3.71997361, -3.72522301,
-3.73569569, -3.73649743, -3.74054982]
self.volumes = [14.824542034870653, 18.118887714656875, 15.373596786943025,
17.569833126580278, 15.92265868064787, 17.02077912220064,
16.471717630914863]
self.eos = "vinet"
self.T = 500
self.qhda = QuasiharmonicDebyeApprox(self.energies, self.volumes, struct, t_min=self.T,
t_max=self.T, eos=self.eos, anharmonic_contribution=True)
self.opt_vol = 17.216094889116807
def setUp(self):
struct = Structure.from_dict({
'lattice': {'a': 2.5630200477817295,
'alpha': 59.999993839702206,
'b': 2.563020442699644,
'beta': 59.999988742674944,
'c': 2.56301993,
'gamma': 60.00000504373715,
'matrix': [[2.21964022, 0.0, 1.28151046],
[0.73987974, 2.09269747, 1.28151046],
[-0.0, -0.0, 2.56301993]],
'volume': 11.905318492097948},
'sites': [{'abc': [-0.0, -0.0, -0.0],
'label': 'B',
'species': [{'element': 'B', 'occu': 1}],
'xyz': [0.0, 0.0, 0.0]},
{'abc': [0.25, 0.25, 0.25],
'label': 'N',
'species': [{'element': 'N', 'occu': 1}],
'xyz': [0.73987999, 0.5231743675, 1.2815102125]}]})
self.energies = [-15.76315281, -16.11541813, -16.41784171, -16.47471523,
-16.63624155, -16.6741551, -16.78661144, -16.88768073,
-16.92450672, -17.04863261, -17.06126553, -17.15786866,
-17.19784976, -17.25078749, -17.30017149, -17.32578594,
-17.3708922, -17.38125127, -17.41231934, -17.41534352,
-17.42636644]
self.volumes = [8.678977833994137, 8.971505437031707, 9.27052889309282, 15.845976281427582,
15.417733609491387, 9.576127994353376, 14.997270631725604, 9.888370962140854,
14.584523227465766, 14.179424329180256, 10.20732378093211, 13.78189117535765,
10.533067462993838, 13.391864274742145, 10.865663655755416, 13.009260480347871,
11.205193091129587, 12.634015019827533, 11.551718049704352, 12.26606042141808,
11.90531496343142]
self.eos = "vinet"
self.T = 300
self.qhda = QuasiharmonicDebyeApprox(self.energies, self.volumes, struct, t_min=self.T,
t_max=self.T, eos=self.eos)
self.opt_vol = 11.957803302392925
def run_task(self, fw_spec):
gibbs_dict = {}
tag = self["tag"]
t_step = self.get("t_step", 10)
t_min = self.get("t_min", 0)
t_max = self.get("t_max", 1000)
mesh = self.get("mesh", [20, 20, 20])
eos = self.get("eos", "vinet")
qha_type = self.get("qha_type", "debye_model")
pressure = self.get("pressure", 0.0)
poisson = self.get("poisson", 0.25)
anharmonic_contribution = self.get("anharmonic_contribution", False)
gibbs_dict["metadata"] = self.get("metadata", {})
db_file = env_chk(self.get("db_file"), fw_spec)
mmdb = VaspCalcDb.from_db_file(db_file, admin=True)
# get the optimized structure
d = mmdb.collection.find_one(
{"task_label": "{} structure optimization".format(tag)},
{"calcs_reversed": 1})
structure = Structure.from_dict(
d["calcs_reversed"][-1]["output"]['structure'])
gibbs_dict["structure"] = structure.as_dict()
gibbs_dict["formula_pretty"] = structure.composition.reduced_formula
# get the data(energy, volume, force constant) from the deformation runs
docs = mmdb.collection.find(
{
"task_label": {
"$regex": "{} gibbs*".format(tag)
},
"formula_pretty": structure.composition.reduced_formula
}, {"calcs_reversed": 1})
energies = []
volumes = []
force_constants = []
for d in docs:
s = Structure.from_dict(
d["calcs_reversed"][-1]["output"]['structure'])
energies.append(d["calcs_reversed"][-1]["output"]['energy'])
if qha_type not in ["debye_model"]:
force_constants.append(
d["calcs_reversed"][-1]["output"]['force_constants'])
volumes.append(s.volume)
gibbs_dict["energies"] = energies
gibbs_dict["volumes"] = volumes
if qha_type not in ["debye_model"]:
gibbs_dict["force_constants"] = force_constants
try:
# use quasi-harmonic debye approximation
if qha_type in ["debye_model"]:
from pymatgen.analysis.quasiharmonic import QuasiharmonicDebyeApprox
qhda = QuasiharmonicDebyeApprox(
energies,
volumes,
structure,
t_min,
t_step,
t_max,
eos,
pressure=pressure,
poisson=poisson,
anharmonic_contribution=anharmonic_contribution)
gibbs_dict.update(qhda.get_summary_dict())
gibbs_dict["anharmonic_contribution"] = anharmonic_contribution
gibbs_dict["success"] = True
# use the phonopy interface
else:
from atomate.vasp.analysis.phonopy import get_phonopy_gibbs
G, T = get_phonopy_gibbs(energies, volumes, force_constants,
structure, t_min, t_step, t_max, mesh,
eos, pressure)
gibbs_dict["gibbs_free_energy"] = G
gibbs_dict["temperatures"] = T
gibbs_dict["success"] = True
# quasi-harmonic analysis failed, set the flag to false
except:
import traceback
logger.warn("Quasi-harmonic analysis failed!")
gibbs_dict["success"] = False
gibbs_dict["traceback"] = traceback.format_exc()
gibbs_dict['metadata'].update({"task_label_tag": tag})
gibbs_dict["created_at"] = datetime.utcnow()
gibbs_dict = jsanitize(gibbs_dict)
# TODO: @matk86: add a list of task_ids that were used to construct the analysis to DB?
# -computron
if not db_file:
dump_file = "gibbs.json"
logger.info("Dumping the analysis summary to {}".format(dump_file))
with open(dump_file, "w") as f:
f.write(json.dumps(gibbs_dict, default=DATETIME_HANDLER))
else:
coll = mmdb.db["gibbs_tasks"]
coll.insert_one(gibbs_dict)
logger.info("Gibbs free energy calculation complete.")
if not gibbs_dict["success"]:
return FWAction(defuse_children=True)
class TestQuasiharmociDebyeApprox(unittest.TestCase):
def setUp(self):
struct = Structure.from_dict(
{
"lattice": {
"a": 2.5630200477817295,
"alpha": 59.999993839702206,
"b": 2.563020442699644,
"beta": 59.999988742674944,
"c": 2.56301993,
"gamma": 60.00000504373715,
"matrix": [
[2.21964022, 0.0, 1.28151046],
[0.73987974, 2.09269747, 1.28151046],
[-0.0, -0.0, 2.56301993],
],
"volume": 11.905318492097948,
},
"sites": [
{
"abc": [-0.0, -0.0, -0.0],
"label": "B",
"species": [{"element": "B", "occu": 1}],
"xyz": [0.0, 0.0, 0.0],
},
{
"abc": [0.25, 0.25, 0.25],
"label": "N",
"species": [{"element": "N", "occu": 1}],
"xyz": [0.73987999, 0.5231743675, 1.2815102125],
},
],
}
)
self.energies = [
-15.76315281,
-16.11541813,
-16.41784171,
-16.47471523,
-16.63624155,
-16.6741551,
-16.78661144,
-16.88768073,
-16.92450672,
-17.04863261,
-17.06126553,
-17.15786866,
-17.19784976,
-17.25078749,
-17.30017149,
-17.32578594,
-17.3708922,
-17.38125127,
-17.41231934,
-17.41534352,
-17.42636644,
]
self.volumes = [
8.678977833994137,
8.971505437031707,
9.27052889309282,
15.845976281427582,
15.417733609491387,
9.576127994353376,
14.997270631725604,
9.888370962140854,
14.584523227465766,
14.179424329180256,
10.20732378093211,
13.78189117535765,
10.533067462993838,
13.391864274742145,
10.865663655755416,
13.009260480347871,
11.205193091129587,
12.634015019827533,
11.551718049704352,
12.26606042141808,
11.90531496343142,
]
self.eos = "vinet"
self.T = 300
self.qhda = QuasiharmonicDebyeApprox(
self.energies,
self.volumes,
struct,
t_min=self.T,
t_max=self.T,
eos=self.eos,
)
self.opt_vol = 11.957803302392925
def test_bulk_modulus(self):
eos = EOS(self.eos)
eos_fit = eos.fit(self.volumes, self.energies)
bulk_modulus = float(str(eos_fit.b0_GPa).split()[0])
bulk_modulus_ans = float(str(self.qhda.bulk_modulus).split()[0])
np.testing.assert_almost_equal(bulk_modulus, bulk_modulus_ans, 3)
def test_optimum_volume(self):
opt_vol = self.qhda.optimum_volumes[0]
np.testing.assert_almost_equal(opt_vol, self.opt_vol, 3)
def test_debye_temperature(self):
theta = self.qhda.debye_temperature(self.opt_vol)
np.testing.assert_almost_equal(theta, 2559.675227, 3)
def test_gruneisen_paramter(self):
gamma = self.qhda.gruneisen_parameter(self.T, self.opt_vol)
np.testing.assert_almost_equal(gamma, 1.670486, 3)
def test_thermal_conductivity(self):
kappa = self.qhda.thermal_conductivity(self.T, self.opt_vol)
np.testing.assert_almost_equal(kappa, 131.736242, 1)
def test_vibrational_internal_energy(self):
u = self.qhda.vibrational_internal_energy(self.T, self.opt_vol)
np.testing.assert_almost_equal(u, 0.50102, 3)
def test_vibrational_free_energy(self):
A = self.qhda.vibrational_free_energy(self.T, self.opt_vol)
np.testing.assert_almost_equal(A, 0.494687, 3)
class TestQuasiharmociDebyeApprox(unittest.TestCase):
def setUp(self):
struct = Structure.from_dict({
'lattice': {'a': 2.5630200477817295,
'alpha': 59.999993839702206,
'b': 2.563020442699644,
'beta': 59.999988742674944,
'c': 2.56301993,
'gamma': 60.00000504373715,
'matrix': [[2.21964022, 0.0, 1.28151046],
[0.73987974, 2.09269747, 1.28151046],
[-0.0, -0.0, 2.56301993]],
'volume': 11.905318492097948},
'sites': [{'abc': [-0.0, -0.0, -0.0],
'label': 'B',
'species': [{'element': 'B', 'occu': 1}],
'xyz': [0.0, 0.0, 0.0]},
{'abc': [0.25, 0.25, 0.25],
'label': 'N',
'species': [{'element': 'N', 'occu': 1}],
'xyz': [0.73987999, 0.5231743675, 1.2815102125]}]})
self.energies = [-15.76315281, -16.11541813, -16.41784171, -16.47471523,
-16.63624155, -16.6741551, -16.78661144, -16.88768073,
-16.92450672, -17.04863261, -17.06126553, -17.15786866,
-17.19784976, -17.25078749, -17.30017149, -17.32578594,
-17.3708922, -17.38125127, -17.41231934, -17.41534352,
-17.42636644]
self.volumes = [8.678977833994137, 8.971505437031707, 9.27052889309282, 15.845976281427582,
15.417733609491387, 9.576127994353376, 14.997270631725604, 9.888370962140854,
14.584523227465766, 14.179424329180256, 10.20732378093211, 13.78189117535765,
10.533067462993838, 13.391864274742145, 10.865663655755416, 13.009260480347871,
11.205193091129587, 12.634015019827533, 11.551718049704352, 12.26606042141808,
11.90531496343142]
self.eos = "vinet"
self.T = 300
self.qhda = QuasiharmonicDebyeApprox(self.energies, self.volumes, struct, t_min=self.T,
t_max=self.T, eos=self.eos)
self.opt_vol = 11.957803302392925
def test_bulk_modulus(self):
eos = EOS(self.eos)
eos_fit = eos.fit(self.volumes, self.energies)
bulk_modulus = float(str(eos_fit.b0_GPa).split()[0])
bulk_modulus_ans = float(str(self.qhda.bulk_modulus).split()[0])
np.testing.assert_almost_equal(bulk_modulus, bulk_modulus_ans, 3)
def test_optimum_volume(self):
opt_vol = self.qhda.optimum_volumes[0]
np.testing.assert_almost_equal(opt_vol, self.opt_vol, 3)
def test_debye_temperature(self):
theta = self.qhda.debye_temperature(self.opt_vol)
np.testing.assert_almost_equal(theta, 2559.675227, 3)
def test_gruneisen_paramter(self):
gamma = self.qhda.gruneisen_parameter(self.T, self.opt_vol)
np.testing.assert_almost_equal(gamma, 1.670486, 3)
def test_thermal_conductivity(self):
kappa = self.qhda.thermal_conductivity(self.T, self.opt_vol)
np.testing.assert_almost_equal(kappa, 131.736242, 1)
def test_vibrational_internal_energy(self):
u = self.qhda.vibrational_internal_energy(self.T, self.opt_vol)
np.testing.assert_almost_equal(u, 0.50102, 3)
def test_vibrational_free_energy(self):
A = self.qhda.vibrational_free_energy(self.T, self.opt_vol)
np.testing.assert_almost_equal(A, 0.494687, 3)
def run_task(self, fw_spec):
gibbs_dict = {}
tag = self["tag"]
t_step = self.get("t_step", 10)
t_min = self.get("t_min", 0)
t_max = self.get("t_max", 1000)
mesh = self.get("mesh", [20, 20, 20])
eos = self.get("eos", "vinet")
qha_type = self.get("qha_type", "debye_model")
pressure = self.get("pressure", 0.0)
poisson = self.get("poisson", 0.25)
anharmonic_contribution = self.get("anharmonic_contribution", False)
gibbs_dict["metadata"] = self.get("metadata", {})
db_file = env_chk(self.get("db_file"), fw_spec)
mmdb = VaspCalcDb.from_db_file(db_file, admin=True)
# get the optimized structure
d = mmdb.collection.find_one({"task_label": "{} structure optimization".format(tag)},
{"calcs_reversed": 1})
structure = Structure.from_dict(d["calcs_reversed"][-1]["output"]['structure'])
gibbs_dict["structure"] = structure.as_dict()
gibbs_dict["formula_pretty"] = structure.composition.reduced_formula
# get the data(energy, volume, force constant) from the deformation runs
docs = mmdb.collection.find({"task_label": {"$regex": "{} gibbs*".format(tag)},
"formula_pretty": structure.composition.reduced_formula},
{"calcs_reversed": 1})
energies = []
volumes = []
force_constants = []
for d in docs:
s = Structure.from_dict(d["calcs_reversed"][-1]["output"]['structure'])
energies.append(d["calcs_reversed"][-1]["output"]['energy'])
if qha_type not in ["debye_model"]:
force_constants.append(d["calcs_reversed"][-1]["output"]['force_constants'])
volumes.append(s.volume)
gibbs_dict["energies"] = energies
gibbs_dict["volumes"] = volumes
if qha_type not in ["debye_model"]:
gibbs_dict["force_constants"] = force_constants
try:
# use quasi-harmonic debye approximation
if qha_type in ["debye_model"]:
from pymatgen.analysis.quasiharmonic import QuasiharmonicDebyeApprox
qhda = QuasiharmonicDebyeApprox(energies, volumes, structure, t_min, t_step, t_max,
eos, pressure=pressure, poisson=poisson,
anharmonic_contribution=anharmonic_contribution)
gibbs_dict.update(qhda.get_summary_dict())
gibbs_dict["anharmonic_contribution"] = anharmonic_contribution
gibbs_dict["success"] = True
# use the phonopy interface
else:
from atomate.vasp.analysis.phonopy import get_phonopy_gibbs
G, T = get_phonopy_gibbs(energies, volumes, force_constants, structure, t_min,
t_step, t_max, mesh, eos, pressure)
gibbs_dict["gibbs_free_energy"] = G
gibbs_dict["temperatures"] = T
gibbs_dict["success"] = True
# quasi-harmonic analysis failed, set the flag to false
except:
import traceback
logger.warn("Quasi-harmonic analysis failed!")
gibbs_dict["success"] = False
gibbs_dict["traceback"] = traceback.format_exc()
gibbs_dict['metadata'].update({"task_label_tag": tag})
gibbs_dict["created_at"] = datetime.utcnow()
gibbs_dict = jsanitize(gibbs_dict)
# TODO: @matk86: add a list of task_ids that were used to construct the analysis to DB?
# -computron
if not db_file:
dump_file = "gibbs.json"
logger.info("Dumping the analysis summary to {}".format(dump_file))
with open(dump_file, "w") as f:
f.write(json.dumps(gibbs_dict, default=DATETIME_HANDLER))
else:
coll = mmdb.db["gibbs_tasks"]
coll.insert_one(gibbs_dict)
logger.info("Gibbs free energy calculation complete.")
if not gibbs_dict["success"]:
return FWAction(defuse_children=True)
