"""
MDSuite: A Zincwarecode package.
License
-------
This program and the accompanying materials are made available under the terms
of the Eclipse Public License v2.0 which accompanies this distribution, and is
available at https://www.eclipse.org/legal/epl-v20.html
SPDX-License-Identifier: EPL-2.0
Copyright Contributors to the Zincwarecode Project.
Contact Information
-------------------
email: zincwarecode@gmail.com
github: https://github.com/zincware
web: https://zincwarecode.com/
Citation
--------
If you use this module please cite us with:
Summary
-------
"""
import logging
import operator
from mdsuite.calculators.calculator import Calculator, call
from mdsuite.utils.units import boltzmann_constant, elementary_charge
log = logging.getLogger(__name__)
[docs]class NernstEinsteinIonicConductivity(Calculator):
"""
Class for the calculation of the Nernst-Einstein ionic conductivity
See Also
--------
mdsuite.calculators.calculator.Calculator class
Examples
--------
experiment.run_computation.NernstEinsteinIonicConductivity()
"""
def __init__(self, **kwargs):
"""
Standard constructor
Parameters
----------
experiment : Experiment
Experiment class from which to read
"""
super().__init__(**kwargs)
self.post_generation = True
# Properties
self._truth_table = None
self.database_group = "Ionic_Conductivity"
self.analysis_name = "Nernst_Einstein_Ionic_Conductivity"
@call
def __call__(
self,
corrected: bool = False,
plot: bool = False,
data_range: int = 1,
export: bool = False,
species: list = None,
save: bool = True,
):
"""
Standard constructor
Parameters
----------
corrected : bool
If true, correct the output with the distinct diffusion
coefficient.
export : bool
If true, generate a csv file after the analysis.
plot : bool
if true, plot the output.
species : list
List of species on which to operate.
data_range : int
Data range to use in the analysis.
save : bool
if true, save the output.
"""
self.update_user_args(plot=plot, save=False, data_range=data_range, export=export)
self.corrected = corrected
self.data = self._load_data() # tensor_values to be read in
if species is None:
self.species = list(self.experiment.species)
else:
self.species = species
def _load_data(self):
"""
Load tensor_values from a yaml file
Returns
-------
tensor_values: dict
A dictionary of tensor_values stored in the yaml file
"""
test = self.experiment.export_property_data(
{"property": "Diffusion_Coefficients"}
)
return test
@property
def truth_table(self):
"""
Builds a truth table to communicate which tensor_values is available to the
analysis.
Returns
-------
truth_table : list
A truth table communication which tensor_values is available for the
analysis.
"""
if self._truth_table is None:
log.warning(
"No support for different data ranges! This method always picks the"
" first entry in the database!"
)
case_1 = self.experiment.export_property_data(
{
"property": "Diffusion_Coefficients",
"analysis": "Green_Kubo_Self_Diffusion_Coefficients",
}
)
case_2 = self.experiment.export_property_data(
{
"property": "Diffusion_Coefficients",
"analysis": "Green_Kubo_Distinct_Diffusion_Coefficients",
}
)
case_3 = self.experiment.export_property_data(
{
"property": "Diffusion_Coefficients",
"analysis": "Einstein_Self_Diffusion_Coefficients",
}
)
case_4 = self.experiment.export_property_data(
{
"property": "Diffusion_Coefficients",
"analysis": "Einstein_Distinct_Diffusion_Coefficients",
}
)
truth_table = [
list(map(operator.not_, [not case_1, not case_2])),
list(map(operator.not_, [not case_3, not case_4])),
]
self._truth_table = truth_table
return self._truth_table
def _nernst_einstein(self, diffusion_information: list):
"""
Calculate the Nernst-Einstein ionic conductivity
Parameters
----------
diffusion_information : list
A list of dictionaries loaded from the SQL properties database.
Returns
-------
Nernst-Einstein Ionic conductivity of the experiment in units of S/cm
"""
# evaluate the prefactor
numerator = self.experiment.number_of_atoms * (elementary_charge**2)
denominator = (
boltzmann_constant
* self.experiment.temperature
* (self.experiment.volume * self.experiment.units.volume)
)
prefactor = numerator / denominator
conductivity = 0.0
uncertainty = 0.0
for item in diffusion_information:
log.debug(f"Analysing: {item}")
diffusion_coefficient = item.data_dict[0].x
diffusion_uncertainty = item.data_dict[0].uncertainty
species = item.subjects[0].subject
charge_term = self.experiment.species[species].charge[0] ** 2
mass_fraction_term = (
self.experiment.species[species].n_particles
/ self.experiment.number_of_atoms
)
conductivity += diffusion_coefficient * charge_term * mass_fraction_term
uncertainty += diffusion_uncertainty * charge_term * mass_fraction_term
data_range = item.data_range
return [prefactor * conductivity, prefactor * uncertainty, data_range]
def _corrected_nernst_einstein(
self, self_diffusion_information: list, distinct_diffusion_information: list
):
"""
Calculate the corrected Nernst-Einstein ionic conductivity
Parameters
----------
self_diffusion_information : dict
dictionary containing information about self diffusion
distinct_diffusion_information : dict
dictionary containing information about distinct diffusion
Returns
-------
Corrected Nernst-Einstein ionic conductivity in units of S/cm
"""
# evaluate the prefactor
numerator = self.experiment.number_of_atoms * (elementary_charge**2)
denominator = (
boltzmann_constant
* self.experiment.temperature
* (self.experiment.volume * self.experiment.units.volume)
)
prefactor = numerator / denominator
conductivity = 0.0
uncertainty = 0.0
for item in self_diffusion_information:
diffusion_coefficient = item["data"]
diffusion_uncertainty = item["uncertainty"]
species = item["Subject"]
charge_term = self.experiment.species[species].charge[0] ** 2
mass_fraction_term = (
self.experiment.species[species].n_particles
/ self.experiment.number_of_atoms
)
conductivity += diffusion_coefficient * charge_term * mass_fraction_term
uncertainty += diffusion_uncertainty * charge_term * mass_fraction_term
data_range = item["data_range"]
for item in distinct_diffusion_information:
diffusion_coefficient = item["data"]
diffusion_uncertainty = item["uncertainty"]
constituents = item["Subject"].split("_")
charge_term = (
self.experiment.species[constituents[0]].charge[0]
* self.experiment.species[constituents[1]].charge[0]
)
mass_fraction_term = (
self.experiment.species[constituents[0]].n_particles
/ self.experiment.number_of_atoms
) * (
self.experiment.species[constituents[1]].n_particles
/ self.experiment.number_of_atoms
)
conductivity += diffusion_coefficient * charge_term * mass_fraction_term
uncertainty += diffusion_uncertainty * charge_term * mass_fraction_term
return [prefactor * conductivity, prefactor * uncertainty, data_range]
def _run_nernst_einstein(self):
"""
Process truth table and run all possible nernst-einstein calculations
Returns
-------
"""
ne_table = [self.truth_table[0][0], self.truth_table[1][0]]
if ne_table[0]:
input_data = [
self.experiment.export_property_data(
{
"property": "Diffusion_Coefficients",
"analysis": "Green_Kubo_Self_Diffusion_Coefficients",
"subjects": [species[0] for species in self.species],
}
)
]
data = self._nernst_einstein(input_data)
properties = {
"Property": self.database_group,
"Analysis": "Green_Kubo_Nernst_Einstein_Ionic_Conductivity",
"Subject": ["System"],
"data_range": data[2],
"data": [{"x": data[0], "uncertainty": data[1]}],
}
self._update_properties_file(properties)
if ne_table[1]:
input_data = [
self.experiment.export_property_data(
{
"property": "Diffusion_Coefficients",
"analysis": "Einstein_Self_Diffusion_Coefficients",
"subjects": [species[0] for species in self.species],
}
)
]
data = self._nernst_einstein(input_data)
properties = {
"Property": self.database_group,
"Analysis": "Einstein_Nernst_Einstein_Ionic_Conductivity",
"Subject": ["System"],
"data_range": data[2],
"data": [{"x": data[0], "uncertainty": data[1]}],
}
self._update_properties_file(properties)
if not any(ne_table):
ValueError(
"There is no values to analyse, please run a diffusion calculation to"
" proceed"
)
def _run_corrected_nernst_einstein(self):
"""
Process truth table and run all possible nernst-einstein calculations
Returns
-------
Updates the experiment database_path
"""
cne_table = [self.truth_table[0][1], self.truth_table[1][1]]
if cne_table[0]:
input_self = [
self.experiment.export_property_data(
{
"property": "Diffusion_Coefficients",
"analysis": "Green_Kubo_Self_Diffusion_Coefficients",
"subjects": [species],
}
)[0]
for species in self.species
]
input_distinct = [
self.experiment.export_property_data(
{
"property": "Diffusion_Coefficients",
"analysis": "Green_Kubo_Distinct_Diffusion_Coefficients",
"subjects": [species],
}
)[0]
for species in self.species
]
data = self._corrected_nernst_einstein(input_self, input_distinct)
properties = {
"Property": self.database_group,
"Analysis": "Green_Kubo_Corrected_Nernst_Einstein_Ionic_Conductivity",
"Subject": ["System"],
"data_range": data[2],
"data": [{"x": data[0], "uncertainty": data[1]}],
}
self._update_properties_file(properties)
if cne_table[1]:
input_self = [
self.experiment.export_property_data(
{
"property": "Diffusion_Coefficients",
"analysis": "Einstein_Self_Diffusion_Coefficients",
"subjects": [species],
}
)[0]
for species in self.species
]
input_distinct = [
self.experiment.export_property_data(
{
"property": "Diffusion_Coefficients",
"analysis": "Einstein_Distinct_Diffusion_Coefficients",
"subjects": [species],
}
)[0]
for species in self.species
]
data = self._corrected_nernst_einstein(input_self, input_distinct)
properties = {
"Property": self.database_group,
"Analysis": "Einstein_Corrected_Nernst_Einstein_Ionic_Conductivity",
"Subject": ["System"],
"data_range": data[2],
"data": [{"x": data[0], "uncertainty": data[1]}],
}
self._update_properties_file(properties)
[docs] def run_post_generation_analysis(self):
"""
Run the analysis
"""
self._run_nernst_einstein()
if self.corrected:
self._run_corrected_nernst_einstein()