def test_get_concentration(self):
doc = [["Na", 3], ["P", 1]]
conc = get_concentration(doc, ["Na", "P"])
self.assertEqual(conc, [0.75])
conc = get_concentration(doc, ["Na", "P"], include_end=True)
self.assertEqual(conc, [0.75, 0.25])
conc = get_concentration(doc, ["K", "P"], include_end=True)
self.assertEqual(conc, [0.0, 0.25])
doc = {"stoichiometry": [["Na", 3], ["P", 1]]}
conc = get_concentration(doc, ["Na", "P"])
self.assertEqual(conc, [0.75])
conc = get_concentration(doc, ["Na", "P"], include_end=True)
self.assertEqual(conc, [0.75, 0.25])
def concentration(self):
""" Return concentration of each species in stoichiometry. """
if 'concentration' not in self._data:
self._data['concentration'] = get_concentration(
self.stoichiometry, [elem[0] for elem in self.stoichiometry],
include_end=True)
return self._data['concentration']
def test_gravimetric_capacity(self):
test_docs = []
test_elements = []
Q = []
# Li3P and symmetry test
doc = dict()
doc["stoichiometry"] = [["Li", 3], ["P", 1]]
test_docs.append(doc)
test_elements.append(["Li", "P"])
doc = dict()
doc["stoichiometry"] = [["P", 1], ["Li", 3]]
test_docs.append(doc)
test_elements.append(["Li", "P"])
# ternary test
doc = dict()
doc["stoichiometry"] = [["Li", 1], ["Mo", 1], ["S", 2]]
test_docs.append(doc)
test_elements.append(["Li", "Mo", "S"])
doc = dict()
doc["stoichiometry"] = [["Li", 8], ["Mo", 1], ["S", 2]]
test_docs.append(doc)
test_elements.append(["Li", "Mo", "S"])
doc = dict()
doc["stoichiometry"] = [["Li", 1], ["Mo", 2], ["S", 4]]
test_docs.append(doc)
test_elements.append(["Li", "Mo", "S"])
for doc, elem in zip(test_docs, test_elements):
doc["concentration"] = get_concentration(doc, elem)
temp_conc = list(doc["concentration"])
temp_conc.append(1.0)
for conc in doc["concentration"]:
temp_conc[-1] -= conc
Q.append(get_generic_grav_capacity(temp_conc, elem))
self.assertAlmostEqual(Q[0], 2596.096626125, places=3)
self.assertAlmostEqual(Q[2], 167.449, places=3)
self.assertEqual(Q[0], Q[1])
self.assertEqual(round(8 * Q[2], 3), round(Q[3], 3))
self.assertEqual(round(Q[2], 3), round(2 * Q[4], 3))
def _get_hull_distance(self, generation):
""" Assign distance from the hull from hull for generation,
assigning it.
Parameters:
generation (Generation): list of optimised structures.
Returns:
hull_dist (list(float)): list of distances to the hull.
"""
for ind, populum in enumerate(generation):
generation[ind]["concentration"] = get_concentration(
populum, self.hull.elements)
generation[ind][
"formation_enthalpy_per_atom"] = get_formation_energy(
self.chempots, populum)
if self.debug:
print(
generation[ind]["concentration"],
generation[ind]["formation_enthalpy_per_atom"],
)
if self.testing:
for ind, populum in enumerate(generation):
generation[ind][
"formation_enthalpy_per_atom"] = np.random.rand() - 0.5
structures = np.hstack((
get_array_from_cursor(generation, "concentration"),
get_array_from_cursor(generation,
"formation_enthalpy_per_atom").reshape(
len(generation), 1),
))
if self.debug:
print(structures)
hull_dist = self.hull.get_hull_distances(structures, precompute=False)
for ind, populum in enumerate(generation):
generation[ind]["hull_distance"] = hull_dist[ind]
return hull_dist
def construct_structure_attributes(self, doc: Crystal):
structure_attributes = {}
# from optimade StructureResourceAttributes
structure_attributes["elements"] = doc.elems
structure_attributes["nelements"] = len(doc.elems)
concentration = get_concentration(doc._data,
elements=doc.elems,
include_end=True)
structure_attributes["elements_ratios"] = concentration
structure_attributes["chemical_formula_descriptive"] = doc.formula
structure_attributes["chemical_formula_reduced"] = doc.formula
structure_attributes["chemical_formula_hill"] = None
sorted_stoich = sorted(doc.stoichiometry,
key=lambda x: x[1],
reverse=True)
gen = anonymous_element_generator()
for ind, elem in enumerate(sorted_stoich):
elem[0] = next(gen)
structure_attributes[
"chemical_formula_anonymous"] = get_formula_from_stoich(
doc.stoichiometry,
elements=[elem[0] for elem in sorted_stoich])
structure_attributes["dimension_types"] = [1, 1, 1]
structure_attributes["nperiodic_dimensions"] = 3
structure_attributes["lattice_vectors"] = doc.lattice_cart
structure_attributes["lattice_abc"] = doc.lattice_abc
structure_attributes["cell_volume"] = cart2volume(doc.lattice_cart)
structure_attributes["fractional_site_positions"] = doc.positions_frac
structure_attributes["cartesian_site_positions"] = doc.positions_abs
structure_attributes["nsites"] = doc.num_atoms
structure_attributes["species_at_sites"] = doc.atom_types
species = []
for ind, atom in enumerate(doc.elems):
species.append(
Species(name=atom,
chemical_symbols=[atom],
concentration=[1.0]))
structure_attributes["species"] = species
structure_attributes["assemblies"] = None
structure_attributes["structure_features"] = []
# from optimade EntryResourceAttributes
if "text_id" not in doc._data:
structure_attributes["local_id"] = " ".join([
self.wlines[random.randint(0, self.num_words - 1)].strip(),
self.nlines[random.randint(0, self.num_nouns - 1)].strip(),
])
else:
structure_attributes["local_id"] = " ".join(doc._data["text_id"])
structure_attributes["last_modified"] = datetime.datetime.now()
if "_id" in doc._data:
structure_attributes["immutable_id"] = str(doc._data["_id"])
else:
structure_attributes["immutable_id"] = str(
bson.objectid.ObjectId())
# if "date" in doc._data:
# date = [int(val) for val in doc._data["date"].split("-")]
# structure_attributes["date"] = datetime.date(
# year=date[-1], month=date[1], day=date[0]
# )
# from matador extensions
structure_attributes[
"dft_parameters"] = self.construct_dft_hamiltonian(doc)
structure_attributes["submitter"] = self.construct_submitter(doc)
structure_attributes["thermodynamics"] = self.construct_thermodynamics(
doc)
structure_attributes["space_group"] = self.construct_spacegroup(doc)
structure_attributes["calculator"] = self.construct_calculator(doc)
structure_attributes["stress_tensor"] = doc._data.get("stress")
structure_attributes["stress"] = doc._data["pressure"]
structure_attributes["forces"] = doc._data.get("forces")
structure_attributes["max_force_on_atom"] = doc._data.get(
"max_force_on_atom")
return MatadorStructureResourceAttributes(**structure_attributes)
def test_toy_ternary(self):
cursor = [
{
"stoichiometry": [["K", 1.0]],
"enthalpy_per_atom": 0,
"cell_volume": 100,
"num_atoms": 10,
},
{
"stoichiometry": [["Sn", 1.0]],
"enthalpy_per_atom": 0,
"cell_volume": 100,
"num_atoms": 10,
},
{
"stoichiometry": [["P", 1.0]],
"enthalpy_per_atom": 0,
"cell_volume": 100,
"num_atoms": 10,
},
{
"stoichiometry": [["K", 1.0], ["Sn", 1.0], ["P", 1.0]],
"enthalpy_per_atom": -1,
"cell_volume": 100,
"num_atoms": 30,
},
{
"stoichiometry": [["K", 1.0], ["Sn", 1.0], ["P", 1.0]],
"enthalpy_per_atom": -0.5,
"cell_volume": 100,
"num_atoms": 30,
},
{
"stoichiometry": [["Sn", 1.0], ["P", 1.0]],
"enthalpy_per_atom": 0.1,
"cell_volume": 100,
"num_atoms": 30,
},
{
"stoichiometry": [["K", 4.0], ["Sn", 2.0], ["P", 2.0]],
"enthalpy_per_atom": -0.74,
"cell_volume": 100,
"num_atoms": 30,
},
{
"stoichiometry": [["K", 2.0], ["Sn", 2.0], ["P", 4.0]],
"enthalpy_per_atom": -0.74,
"cell_volume": 100,
"num_atoms": 30,
},
{
"stoichiometry": [["K", 2.0], ["Sn", 4.0], ["P", 2.0]],
"enthalpy_per_atom": -0.74,
"cell_volume": 100,
"num_atoms": 30,
},
{
"stoichiometry": [["Sn", 1.0], ["P", 1.0]],
"enthalpy_per_atom": 0,
"cell_volume": 100,
"num_atoms": 30,
},
]
for ind, doc in enumerate(cursor):
cursor[ind]["concentration"] = get_concentration(
doc, ["K", "Sn", "P"])
cursor[ind]["source"] = ["abcde"]
cursor[ind]["num_fu"] = 10
cursor[ind]["enthalpy"] = (cursor[ind]["enthalpy_per_atom"] *
doc["num_atoms"])
hull = QueryConvexHull(cursor=cursor,
elements=["K", "Sn", "P"],
no_plot=True)
hull_dists = [doc["hull_distance"] for doc in hull.cursor]
np.testing.assert_array_almost_equal(
hull_dists, [0, 0, 0, 0, 0.5, 0.1, 0.01, 0.01, 0.01, 0])
def test_ternary_volume_curve(self):
cursor = [
{
"stoichiometry": [["K", 1.0]],
"enthalpy_per_atom": 0,
"cell_volume": 10,
"num_atoms": 10,
"num_fu": 10,
},
{
"stoichiometry": [["Sn", 1.0]],
"enthalpy_per_atom": 0,
"cell_volume": 100,
"num_atoms": 10,
"num_fu": 10,
},
{
"stoichiometry": [["P", 1.0]],
"enthalpy_per_atom": 0,
"cell_volume": 100,
"num_atoms": 10,
"num_fu": 10,
},
{
"stoichiometry": [["K", 1.0], ["Sn", 1.0], ["P", 1.0]],
"enthalpy_per_atom": -1,
"cell_volume": 210,
"num_atoms": 30,
"num_fu": 10,
},
{
"stoichiometry": [["Sn", 2.0], ["P", 1.0]],
"enthalpy_per_atom": -0.1,
"cell_volume": 300,
"num_atoms": 30,
"num_fu": 10,
},
]
for ind, doc in enumerate(cursor):
cursor[ind]["concentration"] = get_concentration(
doc, ["K", "Sn", "P"])
cursor[ind]["source"] = ["abcde"]
cursor[ind]["enthalpy"] = (cursor[ind]["enthalpy_per_atom"] *
doc["num_atoms"])
hull = QueryConvexHull(cursor=cursor,
elements=["K", "Sn", "P"],
no_plot=True,
voltage=True)
np.testing.assert_array_almost_equal(
hull.volume_data["electrode_volume"][0], [30, 31])
np.testing.assert_array_almost_equal(
hull.volume_data["volume_ratio_with_bulk"][0], [1, 31 / 30])
cursor = [
{
"stoichiometry": [["K", 1.0]],
"enthalpy_per_atom": 0,
"cell_volume": 10,
"num_atoms": 10,
"num_fu": 10,
},
{
"stoichiometry": [["Sn", 1.0]],
"enthalpy_per_atom": 0,
"cell_volume": 100,
"num_atoms": 10,
"num_fu": 10,
},
{
"stoichiometry": [["P", 1.0]],
"enthalpy_per_atom": 0,
"cell_volume": 100,
"num_atoms": 10,
"num_fu": 10,
},
{
"stoichiometry": [["K", 1.0], ["Sn", 1.0], ["P", 1.0]],
"enthalpy_per_atom": -1,
"cell_volume": 210,
"num_atoms": 30,
"num_fu": 10,
},
{
"stoichiometry": [["Sn", 2.0], ["P", 1.0]],
"enthalpy_per_atom": -0.1,
"cell_volume": 150,
"num_atoms": 30,
"num_fu": 10,
},
]
for ind, doc in enumerate(cursor):
cursor[ind]["concentration"] = get_concentration(
doc, ["K", "Sn", "P"])
cursor[ind]["source"] = ["abcde"]
cursor[ind]["enthalpy"] = (cursor[ind]["enthalpy_per_atom"] *
doc["num_atoms"])
hull = QueryConvexHull(cursor=cursor,
elements=["K", "Sn", "P"],
no_plot=True,
voltage=True)
np.testing.assert_array_almost_equal(
hull.volume_data["electrode_volume"][0], [15, 31])
np.testing.assert_array_almost_equal(
hull.volume_data["volume_ratio_with_bulk"][0], [1, 31 / 15])