Dataset Open Access
Dataset for Machine Learning Time Propagators for Time-Dependent Density Functional Theory Simulations
Shah, Karan;
Cangi, Attila
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.
Files
(872.4 MB)
| Name | Size | |
|---|---|---|
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Archive.zip
md5:add8ba22592192c879fb63ac4b46f1b7 |
872.4 MB | Download |
1,246
66
views
downloads
| All versions | This version | |
|---|---|---|
| Views | 1,246 | 490 |
| Downloads | 66 | 23 |
| Data volume | 57.6 GB | 20.1 GB |
| Unique views | 1,139 | 469 |
| Unique downloads | 55 | 21 |
- 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