pyscses 0.9.3.3

PYthon Space-Charge Site Explicit Solver

pyscses - PYthon Space-Charge Site-Explicit Solver

pyscses is a Python module that implements a site-explicit, one-dimensional Poisson-Boltzmann solver, used for modelling ionic space charge properties in solid materials. Space charge properties such as electrostatic potential, charge density and charge carrier distributions over the space charge region can be calculated using the Poisson-Boltzmann equation from the input of defect segregation energies and atomically resolved charge carrier positions. The grain boundary resistivity and activation energy can be calculated by extending the model using the calculated charge carrier distributions. pyscses also accounts for different approximations typically assumed when space charge formation is considered. These approximations include site explicit vs. continuum modelling, Mott-Schottky (single mobile defect species) and Gouy-Chapman (all defect species mobile) conditions, and whether the charge of the non-defective species should be considered. Full mathematical derivations, definitions and example code can be found in the userguide.

API documentation can be found here

Source code is available as a git repository at https://github.com/bjmorgan/pyscses/tree/master/pyscses

Installation

The simplest way to install pyscses is to use pip to install from PyPI

pip install pyscses

Alternatively, you can download the latest release from GitHub, and install directly:

cd pyscses
pip install -e .

which installs an editable (-e) version of pyscses in your userspace.

Or clone the latest version from GitHub with

git clone [e-mail]:bjmorgan/pyscses.git

and install the same way.

cd pyscses
pip install -e .

Tests

The directory tests/test_notebooks contains a set of Jupyter notebooks with specified outputs, that can be run to test code functionality. The test notebooks can be found on GitHub here.

There are four directories with varying conditions. In each there is a Jupyter notebook which can be run. The input for the calculations is stored in the input_data directory and the output for the calculations will be stored in the generated_data directory and can be compared to the verified data in the expected_outputs directory. A list of the input parameters used in the notebooks is reiterated in each of the four test folders in the input_parameters file.

Unit tests

Limited unit tests are contained in the top tests directory. These can be run using

cd tests
python -m unittest discover

Automated unit testing of the latest commit happens here.

Documentation

Once installed, the pyscses code is imported into, and run using a Jupyter notebook. An overview of the capabilities of pyscses, along with example code for running the code and varying the simulation conditions can be found in

pyscses/userguides/userguide.ipynb

or the Jupyter notebook can be found on github here .

API documentation is available here .

Scientific context

In polycrystalline solid materials, grain boundaries and interfaces exist separating different crystalline domains. The structural distortion at these interfaces causes segregation of charge carriers to, or away from the grain boundary core. Due to this, the grain boundary core carries a net charge which causes the depletion or accumulation of charge carriers in the regions adjacent, known as space charge regions. Due to the variation on charge carrier concentrations, the ionic conductivity of the material can be strongly affected by the presence of grain boundaries.

Citing pyscses

This code can be cited as:

Wellock, Georgina L., & Morgan, Benjamin J. (2019). pyscses - a PYthon Space-Charge Site-Explicit Solver Zenodo. http://doi.org/10.5281/zenodo.2536867

BibTeX

@misc{wellock_georgina_l_2019_2536901,
  author       = {Wellock, Georgina L. and
                  Morgan, Benjamin J.},
  title        = {{pyscses - a PYthon Space-Charge Site-Explicit 
                   Solver}},
  month        = jan,
  year         = 2019,
  doi          = {10.5281/zenodo.2536901},
  url          = {https://doi.org/10.5281/zenodo.2536867}
}