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papersTODAY 04:00 UTC

arXiv paper applies neural networks to real-space charge density, generalization

A new arXiv preprint examines using neural networks to represent ground-state electron charge density in real space. The work is motivated by the Hohenberg-Kohn theorem, which holds that ground-state density encodes all ground-state information about a many-electron system. The authors also study how well such learned models generalize.

arXivelectron-charge-densitygeneralizationhohenberg-kohn-theoremneural-networks

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