Semiconducting Materials from Analogy and Chemical Theory (SMACT) is a collection of rapid screening tools that uses data about chemical elements.
- Documentation: https://smact.readthedocs.io/en/latest/
- Examples folder: https://github.com/WMD-group/SMACT/tree/master/examples
If you torture the data enough, nature will always confess - Roland Coase (from 'How should economists choose?')
There is a strong demand for functional materials across a wide range of technologies. The motivation can include cost reduction, performance enhancement, or to enable a new application. Data collections such as the Materials Project, NREL Materials Database and the Open Quantum Materials Database are valuable resources, but they largely cover the properties of known compounds as calculated using high-level quantum mechanical theories.
We have developed low-cost procedures for screening hypothetical materials in SMACT. This framework can be used for simple calculations on your own computer. SMACT follows a top-down approach where a set of element combinations is generated and then screened using rapid chemical filters. It can be used as part of a multi-technique workflow or to feed machine learning models for materials.
SMACT's features are accessed through Python scripts, importing classes and functions as needed. The best place to start is looking at the docs, which highlight some simple examples of how these classes and functions can be used. Extended examples are available in our examples folder.
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At the core of SMACT are Element and Species (element in a given oxidation state) classes that have various properties associated with them.
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The various oxidation states that are accessible to each element are included in their properties.
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Element compositions can be screened through based on the heuristic filters of charge neutrality and electronegativity order. This is handled using the screening module and this publication describes the underlying theory. An example procedure is outlined in the docs and further examples can be found in the counting examples subfolder.
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Further filters can be applied to generated lists of compositions in order to screen for particular properties. These properties are either intrinsic properties of elements or are calculated for compositions using the properties module. For example:
- A use case is shown in this publication, in which 160,000 chemical compositions are screened based on optical band gap calculated using the solid-state energy scale.
- The oxidation_states module can be used to filter out compositions containing metals in unlikely oxidation states according to a data-driven model.
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Compositions can also be filtered based on sustainability via the abundance of elements in the Earth's crust or via the HHI scale.
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Compositions can easily be converted for use in Pymatgen or for representation to machine learning algorithms (see "next steps" in this example).
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The code also has some tools for manipulating common crystal lattice types:
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Common crystal structure types can be built using the builder module
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Lattice parameters can be quickly estimated using ionic radii of the elements for various common crystal structure types using the lattice_parameters module.
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The lattice module and distorter module rely on the Atomic Simulation Environment and can be used to generate unique atomic substitutions on a given crystal structure.
- smact library containing:
- __init__.py Contains the core
ElementandSpeciesclasses. - data_loader.py Handles the loading of external data used to initialise the core
smact.Elementandsmact.Speciesclasses. - screening.py Used for generating and applying filters to compositional search spaces.
- properties.py A collection of tools for estimating useful properties based on composition.
- lattice.py Given the sites, multiplicities and possible oxidation states at those sites, this reads from the database and generates all possible stoichiometeries.
- builder.py Builds some common lattice structures, given the chemical composition.
- 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.
- __init__.py Contains the core
The main language is Python 3 and has been tested using Python 3.6+. Basic requirements are Numpy and Scipy. The Atomic Simulation Environment (ASE), spglib, and pymatgen are also required for many components.
The latest stable release of SMACT can be installed via pip which will automatically setup other Python packages as required:
pip install smact
Alternatively, the very latest version can be installed using:
pip install git+git://github.com/WMD-group/SMACT.git
For developer installation SMACT can be installed from a copy of the source repository (https://github.com/wmd-group/smact); this will be preferred if using experimental code branches.
To clone the project from Github and make a local installation:
git clone https://github.com/wmd-group/smact.git
cd smact
pip install --user -e .
With -e pip will create links to the source folder so that that changes to the code will be immediately reflected on the PATH.
Python code and original data tables are licensed under the GNU General Public License (GPL) v3.
The following files have their own licenses: data/elements.txt is from the OpenBabel project and licensed under the GPL v2, which is included in the parent folder.
Please use the Issue Tracker to report bugs or request features in the first instance. While we hope that most questions can be answered by searching the docs, we welcome new questions on the issue tracker, especially if they helps us improve the docs! For other queries about any aspect of the code, please contact Dan Davies by e-mail: [email protected].
We are always looking for ways to make SMACT better and more useful to the wider community; contributions are very welcome. Please use the "Fork and Pull" workflow to make contributions and stick as closely as possible to the following:
- Code style should comply with PEP8 where possible. Google's house style is also helpful, including a good model for docstrings.
- Please use comments liberally when adding nontrivial features, and take the chance to clean up other people's code while looking at it.
- Add tests wherever possible, and use the test suite to check if you broke anything.
Testing modules should be pass/fail and wrapped into tests/test.py.
Run the tests using python -m smact.tests.test -v.
(The final -v is optional and adds more detail to the output.)
We also use integrated testing on Github via travis.