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

Shah, Karan; Cangi, Attila

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{
  "abstract": "<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>", 
  "DOI": "10.14278/rodare.3994", 
  "publisher": "Rodare", 
  "title": "Dataset for Machine Learning Time Propagators for Time-Dependent Density Functional Theory Simulations", 
  "issued": {
    "date-parts": [
      [
        2025, 
        9, 
        24
      ]
    ]
  }, 
  "author": [
    {
      "family": "Shah, Karan"
    }, 
    {
      "family": "Cangi, Attila"
    }
  ], 
  "version": "2025_09_24", 
  "type": "dataset", 
  "id": "3994", 
  "language": "eng"
}
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Publication date:
September 24, 2025
DOI:
10.14278/rodare.3994

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10.14278/rodare.3994

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Keyword(s):
Physics-informed machine learning TDDFT RT-TDDFT Fourier Neural Operators
Related identifiers:
Identical to:
https://www.hzdr.de/publications/Publ-41882
Supplement to:
10.48550/arXiv.2508.16554
Communities:
License (for files):
Creative Commons Attribution 4.0 International

Versions

Version 2025_09_24 10.14278/rodare.3995 Sep 24, 2025
Version 2025_09_24 10.14278/rodare.3994 Sep 24, 2025
Cite all versions? You can cite all versions by using the DOI 10.14278/rodare.3993. This DOI represents all versions, and will always resolve to the latest one. Read more.

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