Easily extensible Python package for running Structure-Informed Prediction of Formation Energy using Neural Networks (SIPFENN)
Project description
pySIPFENN
This repository contains py(Structure-Informed Prediction of Formation Energy using Neural Networks) software package allowing efficient predictions of the energetics of atomic configurations. The underlying methodology and implementation is given in
- Adam M. Krajewski, Jonathan W. Siegel, Jinchao Xu, Zi-Kui Liu, Extensible Structure-Informed Prediction of Formation Energy with improved accuracy and usability employing neural networks, Computational Materials Science, Volume 208, 2022, 111254 (https://doi.org/10.1016/j.commatsci.2022.111254)
While functionalities are similar to the software released along the paper, this package contains improved methods for featurizing atomic configurations. Notably, all of them are now written completely in Python, removing reliance on Java and making extensions of the software much easier thanks to improved readability. A fuller description of capabilities is given at PSU Phases Research Lab webpage under phaseslab.com/sipfenn.
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