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"description": "# `pyscses` - PYthon Space-Charge Site-Explicit Solver\n\n\n\n[](http://joss.theoj.org/papers/803ed6dd19f453819bdd3ed9ceadf3b3)\n[](https://badge.fury.io/py/pyscses)\n[](https://zenodo.org/badge/latestdoi/90385184)\n[](https://pyscses.readthedocs.io/en/latest/?badge=latest)\n\n`pyscses` is a Python package for modelling ionic space-charges in solid electrolytes. Its primary use is to calculate equilibrium distributions of point-charge atomic defects within one-dimensional “Poisson-Boltzmann”-like mean-field models. These calculations take as inputs a set of defect site positions, within a specific crystal structure, and the associated defect segregation energies. `pyscses` can also be used to calculate ionic transport properties (space-charge resistivities and activation energies) for these equilibrium defect distributions.\n\nOne approach to modelling space-charge formation in solid electrolytes is to consider defects as ideal point-charges embedded in a continuum dielectric, and to calculate equilibrium defect distributions by solving mean-field “Poisson-Boltzmann”-like equations. While numerical solutions to the 1D Poisson-Boltzmann equation are relatively simple to implement, published results are typically obtined using private closed-source codes, making it difficult to reproduce results or to test the effect of different approximations included in specific models. ``pyscses`` provides an open-source Python package for modelling space-charge formation in solid electrolytes, within a 1D Poisson-Boltzmann-like formalism.We are currently using ``pyscses`` in our own research into space-charge formation in solid electrolytes for fuel cells and lithium-ion batteries, and hope that this open-source resource will support reproducible research practices in future studies in this area \\[[1](#SandveEtAl_PLoSComputBiol2013)\\].\n\n``pyscses`` implements a range of numerical models within the general scheme of solving a modified Poisson-Boltzmann equation on a 1D grid:\n- Continuum (regular grid) and site-explicit (irregular grid) models.\n- Periodic and Dirichlet boundary conditions.\n- “Mott-Schottky” and “Gouy-Champman” condtions. These are implemented by setting the mobilities of different defect species. In the case of Mott-Schottky conditions, all but one defect species have a mobility of zero.\n- Inclusion of “lattice”site charges to account for non-defective species in the crystal structure.\n\nProperties that can be calculated include:\n- Defect mole fractions.\n- Charge density.\n- Electrostatic potential.\n- Parallel and perpendicular grain boundary resistivities \\[[2](#HwangEtAl_JElectroceram1999)\\].\n- Grain boundary activation energies \\[[3](#Kim_PhysChemChemPhys2016)\\].\n\nFull mathematical derivations, definitions and example code can be found in the [userguide](https://github.com/bjmorgan/pyscses/blob/master/userguides/notebooks/userguide.ipynb). \n\nA more detailed overview of the code and its capabilities, and of the scientific context of modelling space-charge regions in solids, are given in the [JOSS paper](http://joss.theoj.org/papers/803ed6dd19f453819bdd3ed9ceadf3b3).\n\n## Documentation\n### Userguides\nIntroductory userguides are contained in the [userguide](https://github.com/bjmorgan/pyscses/blob/master/userguides/README.md). Each guide is presented as a [Jupyter notebook](http://jupyter-notebook.readthedocs.io/en/latest/#). The userguides cover the theory behind the Poisson-Boltzmann solver, how to set up a Jupyter notebook to run a calculation and examples of running the calculation under different approximations. These examples include site-explicit versus continuum models, Mott-Schottky (single mobile defect species) and Gouy-Chapman (all defect species mobile) conditions, and running the solver on real data.\n\nThese userguides can also be found in the directory:\n```\npyscses/userguides/userguide.ipynb\n```\nFor online viewing of these userguides, we recommend using [nbviewer](https://nbviewer.jupyter.org). The links below open nbviewer versions of each userguide notebook.\n- [Theory](https://nbviewer.jupyter.org/github/bjmorgan/pyscses/blob/master/userguides/notebooks/Theory.ipynb)\n- [Setting up the notebook](https://nbviewer.jupyter.org/github/bjmorgan/pyscses/blob/master/userguides/notebooks/Setting_up.ipynb)\n- [Running the calculation](https://nbviewer.jupyter.org/github/bjmorgan/pyscses/blob/master/userguides/notebooks/Running.ipynb)\n- [Example 1 - continuum vs. site explicit and boundary conditions](https://nbviewer.jupyter.org/github/bjmorgan/pyscses/blob/master/userguides/notebooks/Ex_1_BC.ipynb)\n- [Example 2 - Mott-Schottky vs. Gouy-Chapman conditions](https://nbviewer.jupyter.org/github/bjmorgan/pyscses/blob/master/userguides/notebooks/Ex_2_MSGC.ipynb)\n- [Example 3 - Calculating the grain boundary resistivity and activation energy](https://nbviewer.jupyter.org/github/bjmorgan/pyscses/blob/master/userguides/notebooks/Ex_3_Res.ipynb)\n- [Example 4 - Comparison with “experimental” values](https://nbviewer.jupyter.org/github/bjmorgan/pyscses/blob/master/userguides/notebooks/Ex_4_MSapp.ipynb)\n- [Example 5 - Using the pyscses with real data](https://nbviewer.jupyter.org/github/bjmorgan/pyscses/blob/master/userguides/notebooks/Ex_5_real_data.ipynb)\n- [Example 6 - Storing the output data](https://nbviewer.jupyter.org/github/bjmorgan/pyscses/blob/master/userguides/notebooks/Ex_6_store_data.ipynb)\n\n### API Documentation\nAPI documentation can be found [here](https://pyscses.readthedocs.io/en/latest/)\n\n## Installation\nSource code is available as a git repository at [https://github.com/bjmorgan/pyscses/tree/master/pyscses](https://github.com/bjmorgan/pyscses/tree/master/pyscses)\n\nThe simplest way to install `pyscses` is to use `pip` to install from [PyPI](https://pypi.org/project/pyscses/)\n```\npip install pyscses\n```\n\nAlternatively, you can download the latest release from [GitHub](https://github.com/bjmorgan/pyscses/releases), and install directly:\n```\ncd pyscses\npip install -e .\n```\nwhich installs an editable (-e) version of pyscses in your userspace.\n\nOr clone the latest version from [GitHub](https://github.com/bjmorgan/pyscses/releases) with\n```\ngit clone git@github.com:bjmorgan/pyscses.git\n```\nand install the same way.\n```\ncd pyscses\npip install -e .\n```\n## Tests\n\nThe 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](https://github.com/bjmorgan/pyscses/tree/master/tests/test_notebooks).\n\nThere 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. \n\n### Unit tests\n\nLimited unit tests are contained in the top `tests` directory. These can be run using\n```\ncd tests\npython -m unittest discover\n```\nAutomated unit testing of the latest commit happens [here](https://travis-ci.org/bjmorgan/pyscses/).\n\n## Contributing\n\n### Bugs reports and feature requests\n\nIf you think you have found a bug, please report it on the [Issue Tracker](https://github.com/bjmorgan/pyscses/issues). This is also the place to propose ideas for new features or ask questions about the design of pyscses. Poor documentation is considered a bug, but please be as specific as possible when asking for improvements.\n\n### Code contributions\n\nWe welcome your help in improving and extending the package with your own contributions. This is managed through GitHub pull requests; for external contributions we prefer the \"fork and pull\" workflow, while core developers use branches in the main repository:\n\n- First open an [Issue](https://github.com/bjmorgan/pyscses/issues) to discuss the proposed contribution. This discussion might include how the changes fit pyscses' scope and a general technical approach.\n- Make your own project fork and implement the changes there. Please keep your code style compliant with PEP8.\n- Open a [pull request](https://github.com/bjmorgan/pyscses/pulls) to merge the changes into the main project. A more detailed discussion can take place there before the changes are accepted.\n\n## Citing `pyscses`\n\nThis code can be cited as:\n\nWellock, Georgina L., & Morgan, Benjamin J. (2019). *pyscses - a PYthon Space-Charge Site-Explicit Solver* Zenodo. http://doi.org/10.5281/zenodo.2536867\n\n### BibTeX\n\n```\n@misc{wellock_georgina_l_2019_2536901,\n author = {Wellock, Georgina L. and\n Morgan, Benjamin J.},\n title = {{pyscses - a PYthon Space-Charge Site-Explicit \n Solver}},\n month = jan,\n year = 2019,\n doi = {10.5281/zenodo.2536901},\n url = {https://doi.org/10.5281/zenodo.2536867}\n}\n```\n\n# References\n1. [Sandve, G. K., Nekrutenko, A., Taylor, J., & Hovig, E. (2013). Ten simple rules for reproducible computational research. PLoS Comput. Biol., 9(10), e1003285\u20134.](https://dx.doi.org/10.1371/journal.pcbi.1003285)\n1. [Hwang, J.-H., McLachlan, D. S., & Mason, T. O. (1999). Brick layer model analysis of nanoscale-to-microscale cerium dioxide. *J. Electroceram.*, **3** (1), 7\u201316.](https://dx.doi.org/10.1023/A:1009998114205)\n1. [Kim, S., & Maier, J. (2002). On the conductivity mechanism of nanocrystalline ceria. *J. Electrochem. Soc.*, **149**(10), J73\u2013J83.](https://dx.doi.org/10.1149/1.1507597)\n\n\n\n",
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