Dataset for Machine Learning Time Propagators for Time-Dependent Density Functional Theory Simulations

DOI

This repository contains the dataset supporting the paper "Machine Learning Time Propagators for Time-Dependent Density Functional Theory Simulations" by Karan Shah and Attila Cangi. It comprises time-dependent density functional theory (TDDFT) simulations of one-dimensional diatomic molecules under laser excitation. The data is used to train and evaluate autoregressive Fourier Neural Operator (FNO) models that serve as ML time propagators for electron density evolution.

Identifier
DOI https://doi.org/10.14278/rodare.3994
Related Identifier IsSupplementTo https://doi.org/10.48550/arXiv.2508.16554
Related Identifier IsIdenticalTo https://www.hzdr.de/publications/Publ-41882
Related Identifier IsPartOf https://doi.org/10.14278/rodare.3993
Related Identifier IsPartOf https://rodare.hzdr.de/communities/rodare
Metadata Access https://rodare.hzdr.de/oai2d?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:rodare.hzdr.de:3994
Provenance
Creator Shah, Karan ORCID logo; Cangi, Attila (ORCID: 0000-0001-9162-262X)
Publisher Rodare
Publication Year 2025
Rights Creative Commons Attribution 4.0 International; Open Access; https://creativecommons.org/licenses/by/4.0/legalcode; info:eu-repo/semantics/openAccess
OpenAccess true
Contact https://rodare.hzdr.de/support
Representation
Language English
Resource Type Dataset
Version 2025_09_24
Discipline Natural Sciences; Physics