Introduction#
smact is a collection of tools and examples for “low-fi” screening of
potential semiconducting materials through the use of chemical
rules.
smact uses a combination of heuristics and models derived from data to
rapidly search large areas of chemical space. This combination of methods
allows smact to identify new materials for applications such as photovoltaics,
water splitting and thermoelectrics.
Features of smact include:
Chemical elements with associated properties
Filters for oxidation states and charge balancing
Structure prediction from chemical composition
Composition probability prediction
Install#
The package is available via pip install smact.
License and citation#
smact is distributed under an MIT license.
To cite the theory of smact please use:
To cite the code of smact please use:
Studies using smact#
Read more about smact in our publications:
Computational screening of all stoichiometric inorganic materials
Materials discovery by chemical analogy: role of oxidation states in structure prediction
This approach is inspired by the work of Harrison [1] and Pamplin [2]. The work is an active project in the Materials Design Group.
We are also developing a set of Jupyter Notebook examples here.