Peering inside the black box - Learning the relevance of many-body functions in Neural Network potentials (Data and Codes)
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.
- Freie Universität Berlin Germany
- Technical University of Berlin Germany
Explainable AI, Neural Network Potentials, Molecular Dynamics, Coarse-Graining
Explainable AI, Neural Network Potentials, Molecular Dynamics, Coarse-Graining
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!