September 24, 2025 Dataset Open Access

Shah, Karan; Cangi, Attila

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{
  "datePublished": "2025-09-24", 
  "@id": "https://doi.org/10.14278/rodare.3994", 
  "version": "2025_09_24", 
  "url": "https://rodare.hzdr.de/record/3994", 
  "name": "Dataset for Machine Learning Time Propagators for Time-Dependent Density Functional Theory Simulations", 
  "keywords": [
    "Physics-informed machine learning", 
    "TDDFT", 
    "RT-TDDFT", 
    "Fourier Neural Operators"
  ], 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "@type": "Dataset", 
  "@context": "https://schema.org/", 
  "distribution": [
    {
      "@type": "DataDownload", 
      "contentUrl": "https://rodare.hzdr.de/api/files/566b0bde-31f5-43c5-b2ae-e5c1bd32ed58/Archive.zip", 
      "fileFormat": "zip"
    }
  ], 
  "identifier": "https://doi.org/10.14278/rodare.3994", 
  "description": "<p>This repository contains the dataset supporting the paper &quot;Machine Learning Time Propagators for Time-Dependent Density Functional Theory Simulations&quot; 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&nbsp;time propagators for electron density evolution.</p>", 
  "sameAs": [
    "https://www.hzdr.de/publications/Publ-41882"
  ], 
  "inLanguage": {
    "name": "English", 
    "@type": "Language", 
    "alternateName": "eng"
  }, 
  "creator": [
    {
      "name": "Shah, Karan", 
      "@id": "https://orcid.org/0000-0002-5480-2880", 
      "@type": "Person", 
      "affiliation": "CASUS, HZDR"
    }, 
    {
      "name": "Cangi, Attila", 
      "@id": "https://orcid.org/0000-0001-9162-262X", 
      "@type": "Person", 
      "affiliation": "CASUS, HZDR"
    }
  ]
}