Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments

DOI

Code and documentation for the improved Gaussian Moments Neural Network (GM-NN). An updated version can be found on GitLab

Basic instructions for installing and running the software can be found in the README.md file.

Identifier
DOI https://doi.org/10.18419/darus-2136
Related Identifier IsCitedBy https://doi.org/10.1021/acs.jctc.1c00527
Metadata Access https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/darus-2136
Provenance
Creator Zaverkin, Viktor ORCID logo; Holzmüller, David ORCID logo; Steinwart, Ingo ORCID logo; Kästner, Johannes ORCID logo
Publisher DaRUS
Contributor Kästner, Johannes
Publication Year 2021
Funding Reference DFG EXC 2075 - 390740016 ; European Commission info:eu-repo/grantAgreement/EC/H2020/646717 ; German Academic Scholarship Foundation
Rights MIT License; info:eu-repo/semantics/openAccess; https://spdx.org/licenses/MIT.html
OpenAccess true
Contact Kästner, Johannes (Universität Stuttgart)
Representation
Resource Type Dataset
Format text/x-python; application/octet-stream; image/png; chemical/x-xyz; text/plain; charset=UTF-8; application/x-msdownload; text/plain; charset=US-ASCII; text/css; text/markdown; text/plain
Size 2826; 89; 19848; 127; 2127; 22697; 2130; 322; 2131; 698402; 1348; 14240; 1591; 369; 397053; 192; 463; 1137; 0; 1438; 22811; 2414; 1097; 764; 638; 47; 9845; 358; 5746; 11574; 33811; 234; 716; 3754; 84; 10521; 65047; 67234; 8723; 1733; 13679; 7907; 5514
Version 1.0
Discipline Chemistry; Natural Sciences; Physics