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).

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piml_ks_inversion.zip
md5:ec731fb92bb05c17a45b9d6bf2b1f530 		155.1 MB Download
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Publication date:
February 1, 2024
DOI:
10.14278/rodare.2720

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10.14278/rodare.2720

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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
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License (for files):
Creative Commons Attribution 4.0 International

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Version 1 10.14278/rodare.2720 Feb 1, 2024
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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
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