Towards the construction of an accurate kinetic energy density functional and its functional derivative through physics-informed neural networks

Luis Rincón, Luis E. Seijas, Rafael Almeida, F. Javier Torres

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

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Resumen

One of the primary obstacles in the development of orbital-free density functional theory is the lack of an accurate functional for the Kohn-Sham non-interacting kinetic energy, which, in addition to its accuracy, must also render a good approximation for its functional derivative. To address this critical issue, we propose the construction of a kinetic energy density functional throught physical- informed neural network, where the neural network’s loss function is designed to simultaneously reproduce the atom’s shell structures, and also, an analytically calculated functional derivative. As a proof-of-concept, we have tested the accuracy of the kinetic energy potential by optimizing electron densities for atoms from Li to Xe.

Idioma originalInglés
Número de artículo061001
PublicaciónJournal of Physics Communications
Volumen7
N.º6
DOI
EstadoPublicada - jun. 2023

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