Dataset Open Access
Dataset for Machine Learning Time Propagators for Time-Dependent Density Functional Theory Simulations
Shah, Karan;
Cangi, Attila
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{
"version": "2025_09_24",
"id": "3994",
"publisher": "Rodare",
"language": "eng",
"abstract": "<p>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.</p>",
"DOI": "10.14278/rodare.3994",
"issued": {
"date-parts": [
[
2025,
9,
24
]
]
},
"title": "Dataset for Machine Learning Time Propagators for Time-Dependent Density Functional Theory Simulations",
"type": "dataset",
"author": [
{
"family": "Shah, Karan"
},
{
"family": "Cangi, Attila"
}
]
}
370
13
views
downloads
| All versions | This version | |
|---|---|---|
| Views | 370 | 113 |
| Downloads | 13 | 5 |
| Data volume | 11.3 GB | 4.4 GB |
| Unique views | 288 | 95 |
| Unique downloads | 11 | 5 |
- Publication date:
- September 24, 2025
- DOI:
-
- 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