pySIPFENN
py (S tructure - I nformed P rediction of F ormation E nergy using N eural N etworks) software package allows 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)
Note
This project is under active development.