Peering inside the black box - Learning the relevance of many-body functions in Neural Network potentials (Data and Codes)

This repository contains all the data and scripts necessary for reproducing the results of "Peering inside the black box: Learning the relevance of many-body functions in Neural Network potentials" by Klara Bonneau, Jonas Lederer, Clark Templeton, David Rosenberger, Lorenzo Giambagli, Klaus-Robert Müller and Cecilia Clementi. Details on content of the directories and usage instructions are provided in the main README. Acknowledgements:We gratefully acknowledge funding from the Deutsche ForschungsgemeinschaftDFG (SFB/TRR 186, Project A12; SFB 1114, Projects B03, B08, and A04; SFB 1078, Project C7), the National Science Foundation (PHY-2019745), the Einstein Foundation Berlin (Project 0420815101), the German Ministry for Education and Research (BMBF) project FAIME 01IS24076, and the computing time provided on the supercomputer Lise at NHR@ZIB as part of the NHR infrastructure. K.R.M. was in part supported by the BMBF under grants 01IS14013A-E, 01GQ1115, 01GQ0850, 01IS18025A, 031L0207D, and 01IS18037A, and by the Institute of Information \& Communications Technology Planning \& Evaluation (IITP) grants funded by the Korean government (MSIT) No. 2019-0-00079, Artificial Intelligence Graduate School Program, Korea University and No. 2022-0-00984, Development of Artificial Intelligence Technology for Personalized Plug-and-Play Explanation and Verification of Explanation.