Dataset Open Access
Inverting the Kohn-Sham equations with physics-informed machine learning
Martinetto, Vincent;
Shah, Karan;
Cangi, Attila;
Pribram-Jones, Aurora
This data repository contains the datasets used in the paper "Inverting the Kohn-Sham equations with physics-informed machine learning".
It contains the data generation scripts, datasets for the systems used in the paper (Single Well - 1D atom, Double Well - 1D diatomic molecule) and output potentials generated by the physics-informed machine learning models (physics-informed neural networks and Fourier neural operators).
| Name | Size | |
|---|---|---|
|
piml_ks_inversion.zip
md5:ec731fb92bb05c17a45b9d6bf2b1f530 |
155.1 MB | Download |
| All versions | This version | |
|---|---|---|
| Views | 500 | 500 |
| Downloads | 51 | 51 |
| Data volume | 7.9 GB | 7.9 GB |
| Unique views | 442 | 442 |
| Unique downloads | 47 | 47 |
- Publication date:
- February 1, 2024
- DOI:
-
- Keyword(s):
- density functional theory machine learning
- Related identifiers:
- Identical to:
https://www.hzdr.de/publications/Publ-38725 - Supplement to:
10.48550/arXiv.2312.15301 - Communities:
- License (for files):
- Creative Commons Attribution 4.0 International
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Cite as
Martinetto, Vincent, Shah, Karan, Cangi, Attila, & Pribram-Jones, Aurora. (2024). Inverting the Kohn-Sham equations with physics-informed machine learning [Data set]. Rodare. http://doi.org/10.14278/rodare.2720