def __init__(self):
# Load in the mixing enthalpy data
# Creates a lookup table of the liquid mixing enthalpies
self.dhf_mix = MixingEnthalpy()
# Load in a table of elemental properties
self.elem_data = MagpieData()
class TestMixingEnthalpy(TestCase):
def setUp(self):
self.data = MixingEnthalpy()
def test_get_data(self):
self.assertEqual(
-27, self.data.get_mixing_enthalpy(Element('H'), Element('Pd')))
self.assertEqual(
-27, self.data.get_mixing_enthalpy(Element('Pd'), Element('H')))
self.assertTrue(
isnan(self.data.get_mixing_enthalpy(Element('He'), Element('H'))))
class TestMixingEnthalpy(TestCase):
def setUp(self):
self.data = MixingEnthalpy()
def test_get_data(self):
self.assertEqual(-27, self.data.get_mixing_enthalpy(Element('H'),
Element('Pd')))
self.assertEqual(-27, self.data.get_mixing_enthalpy(Element('Pd'),
Element('H')))
self.assertTrue(isnan(self.data.get_mixing_enthalpy(Element('He'),
Element('H'))))
def setUp(self):
self.data = MixingEnthalpy()
class YangSolidSolution(BaseFeaturizer):
"""
Mixing thermochemistry and size mismatch terms of Yang and Zhang (2012)
This featurizer returns two different features developed by
.. Yang and Zhang `https://linkinghub.elsevier.com/retrieve/pii/S0254058411009357`
to predict whether metal alloys will form metallic glasses,
crystalline solid solutions, or intermetallics.
The first, Omega, is related to the balance between the mixing entropy and
mixing enthalpy of the liquid phase. The second, delta, is related to the
atomic size mismatch between the different elements of the material.
Features
Yang omega - Mixing thermochemistry feature, Omega
Yang delta - Atomic size mismatch term
References:
.. Yang and Zhang (2012) `https://linkinghub.elsevier.com/retrieve/pii/S0254058411009357`.
"""
def __init__(self):
# Load in the mixing enthalpy data
# Creates a lookup table of the liquid mixing enthalpies
self.dhf_mix = MixingEnthalpy()
# Load in a table of elemental properties
self.elem_data = MagpieData()
def precheck(self, c: Composition) -> bool:
"""
Precheck a single entry. YangSolidSolution does not work for compositons
containing any binary elment combinations for which the model has no
parameters. We can nearly equivalently approximate this by checking
against the unary element list.
To precheck an entire dataframe (qnd automatically gather
the fraction of structures that will pass the precheck), please use
precheck_dataframe.
Args:
c (pymatgen.Composition): The composition to precheck.
Returns:
(bool): If True, s passed the precheck; otherwise, it failed.
"""
return all([
e in self.dhf_mix.valid_element_list
for e in c.element_composition.elements
])
def featurize(self, comp):
return [self.compute_omega(comp), self.compute_delta(comp)]
def compute_omega(self, comp):
"""Compute Yang's mixing thermodynamics descriptor
:math:`\\frac{T_m \Delta S_{mix}}{ | \Delta H_{mix} | }`
Where :math:`T_m` is average melting temperature,
:math:`\Delta S_{mix}` is the ideal mixing entropy,
and :math:`\Delta H_{mix}` is the average mixing enthalpies
of all pairs of elements in the alloy
Args:
comp (Composition) - Composition to featurizer
Returns:
(float) Omega
"""
# Special case: Elemental compound (entropy == 0 -> Omega == 1)
if len(comp) == 1:
return 0
# Get the element names and fractions
elements, fractions = zip(
*comp.element_composition.fractional_composition.items())
# Get the mean melting temperature
mean_Tm = PropertyStats.mean(
self.elem_data.get_elemental_properties(elements, "MeltingT"),
fractions)
# Get the mixing entropy
entropy = np.dot(fractions, np.log(fractions)) * 8.314 / 1000
# Get the mixing enthalpy
enthalpy = 0
for i, (e1, f1) in enumerate(zip(elements, fractions)):
for e2, f2 in zip(elements[:i], fractions):
enthalpy += f1 * f2 * self.dhf_mix.get_mixing_enthalpy(e1, e2)
enthalpy *= 4
# Make sure the enthalpy is nonzero
# The limit as dH->0 of omega is +\inf. A very small positive dH will approximate
# this limit without causing issues with infinite features
enthalpy = max(1e-6, abs(enthalpy))
return abs(mean_Tm * entropy / enthalpy)
def compute_delta(self, comp):
"""Compute Yang's delta parameter
:math:`\sqrt{\sum^n_{i=1} c_i \left( 1 - \\frac{r_i}{\\bar{r}} \\right)^2 }`
where :math:`c_i` and :math:`r_i` are the fraction and radius of
element :math:`i`, and :math:`\\bar{r}` is the fraction-weighted
average of the radii. We use the radii compiled by
.. Miracle et al. `https://www.tandfonline.com/doi/ref/10.1179/095066010X12646898728200?scroll=top`.
Args:
comp (Composition) - Composition to assess
Returns:
(float) delta
"""
elements, fractions = zip(*comp.element_composition.items())
# Get the radii of elements
radii = self.elem_data.get_elemental_properties(
elements, "MiracleRadius")
mean_r = PropertyStats.mean(radii, fractions)
# Compute the mean (1 - r/\\bar{r})^2
r_dev = np.power(1.0 - np.divide(radii, mean_r), 2)
return np.sqrt(PropertyStats.mean(r_dev, fractions))
def feature_labels(self):
return ['Yang omega', 'Yang delta']
def citations(self):
return [
"@article{Yang2012,"
"author = {Yang, X. and Zhang, Y.},"
"doi = {10.1016/j.matchemphys.2011.11.021},"
"journal = {Materials Chemistry and Physics},"
"number = {2-3},"
"pages = {233--238},"
"title = {{Prediction of high-entropy stabilized solid-solution in multi-component alloys}},"
"url = {http://dx.doi.org/10.1016/j.matchemphys.2011.11.021},"
"volume = {132},year = {2012}}"
]
def implementors(self):
return ['Logan Ward']
def setUp(self):
self.data = MixingEnthalpy()