Artificial neural networks for the kinetic energy functional of non-interacting fermions
Ghasemi SA, Kühne TD (2021)
Publication Type: Journal article
Publication year: 2021
Journal
Book Volume: 154
Article Number: 074107
Journal Issue: 7
DOI: 10.1063/5.0037319
Abstract
A novel approach to find the fermionic non-interacting kinetic energy functional with chemical accuracy using machine learning techniques is presented. To that extent, we apply machine learning to an intermediate quantity rather than targeting the kinetic energy directly. We demonstrate the performance of the method for three model systems containing three and four electrons. The resulting kinetic energy functional remarkably accurately reproduces self-consistently the ground state electron density and total energy of reference Kohn-Sham calculations with an error of less than 5 mHa. This development opens a new avenue to advance orbital-free density functional theory by means of machine learning.
Involved external institutions
Germany (DE)How to cite
APA:
Ghasemi, S.A., & Kühne, T.D. (2021). Artificial neural networks for the kinetic energy functional of non-interacting fermions. Journal of Chemical Physics, 154(7). https://doi.org/10.1063/5.0037319
MLA:
Ghasemi, S. Alireza, and Thomas D. Kühne. "Artificial neural networks for the kinetic energy functional of non-interacting fermions." Journal of Chemical Physics 154.7 (2021).
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