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Merge pull request #166 from WMD-group/readme_updates
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Update README.md
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AntObi authored Sep 11, 2023
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![python version](https://img.shields.io/pypi/pyversions/smact)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![PyPi](https://img.shields.io/pypi/v/smact)](https://pypi.org/project/SMACT/)
[![Conda](https://anaconda.org/conda-forge/smact/badges/version.svg)](https://anaconda.org/conda-forge/smact)
[![GitHub issues](https://img.shields.io/github/issues-raw/WMD-Group/SMACT)](https://github.com/WMD-group/SMACT/issues)
![dependencies](https://img.shields.io/librariesio/release/pypi/smact)
[![CI Status](https://github.com/WMD-group/SMACT/actions/workflows/ci.yml/badge.svg)](https://github.com/WMD-group/SMACT/actions/workflows/ci.yml)
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- The code also has some tools for manipulating common crystal lattice types:
- Common crystal structure types can be built using the [builder module](https://smact.readthedocs.io/en/latest/smact.builder.html)
- Lattice parameters can be quickly estimated using ionic radii of the elements for various common crystal structure types using the [lattice_parameters module](https://smact.readthedocs.io/en/latest/smact.lattice_parameters.html).
- The [lattice module](https://smact.readthedocs.io/en/latest/smact.lattice.html) and [distorter module](https://smact.readthedocs.io/en/latest/smact.distorter.html) rely on the [Atomic Simulation Environment](https://wiki.fysik.dtu.dk/ase/) and can be used to generate unique atomic substitutions on a given crystal structure.
- The [lattice module](https://smact.readthedocs.io/en/latest/smact.lattice.html) and [distorter module](https://smact.readthedocs.io/en/latest/smact.distorter.html) rely on the [Atomic Simulation Environment](https://wiki.fysik.dtu.dk/ase/) and can be used to generate unique atomic substitutions on a given crystal structure.
- The [structure prediction](https://smact.readthedocs.io/en/latest/smact.structure_prediction.html) module can be used to predict the structure of hypothetical compositions using species similarity measures.
- The [dopant prediction](https://smact.readthedocs.io/en/latest/smact.dopant_prediction.html) module can be used to facilitate high-throughput predictions of p-type and n-type dopants of multicomponent solids.

List of modules
-------
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* **lattice_parameters.py** Estimation of lattice parameters for various lattice types using covalent/ionic radii.
* **distorter.py** A collection of functions for enumerating and then
substituting on inequivalent sites of a sub-lattice.
* **oxidation_states.py**: Used for predicting the likelihood of species coexisting in a compound based on a statistical model.
* **structure_prediction**: A submodule which contains a collection of tools for facilitating crystal structure predictions via ionic substitutions
* **dopant_prediction**: A submodule which contains a collections of tools for predicting n-type and p-type dopants.

Requirements
------------
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pip install smact

SMACT is also available via conda through the conda-forge channel on Anaconda Cloud:

```
conda install -c conda-forge smact
```

Alternatively, the very latest version can be installed using:

pip install git+git://github.com/WMD-group/SMACT.git
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