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CENT2: Improved charge equilibration via neural network technique

Khajehpasha ER, Finkler JA, Kühne TD, Ghasemi SA (2022)

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Publication Type: Journal article

Publication year: 2022

Journal

Book Volume: 105

Article Number: 144106

Journal Issue: 14

DOI: 10.1103/PhysRevB.105.144106

Abstract

The high computational cost of first-principles electronic structure methods together with the successful applications of machine learning (ML) techniques in atomistic simulations resulted in a surge of interest in ML-based interatomic potentials. Despite great progress in the field, there remain some challenges to be solved such as the best way of incorporating long-range interactions, as well as nonlocal charge transfer. The first generation of the charge equilibration via neural network technique (CENT) was a major step forward in concurrently taking into account both aforementioned points. Within structure prediction methods, it turned out to be a powerful tool in discovering novel polymorphs of ionic systems. On the other hand, the method is not expected to be appropriate for multicomponent systems with reference data sets in which some or all elements are subject to varying oxidation states. Here, we present the second generation of CENT, with multiple improvements to the original variant that lead to a more accurate treatment of electrostatic interactions. To do this, it aims at reproducing the electric potential function, which is directly related to the charge distribution, rather than only considering total energies. In addition, a charge-free term is added to correct for the difference between the reference energies and those obtained with the energy functional of CENT. Moreover, the Green's function within the Hartree energy is modified to substantially shield interactions from charges in the neighborhood of each point. Also, the charge density is split into ionic and electronic parts, which allows for a better approximation of the electron density. The utility of this method is examined for magnesium oxide clusters, and multiple comparisons with the first generation are made, demonstrating that much more physical electrostatic interactions can be expected from the second generation of CENT.

Involved external institutions

How to cite

APA:

Khajehpasha, E.R., Finkler, J.A., Kühne, T.D., & Ghasemi, S.A. (2022). CENT2: Improved charge equilibration via neural network technique. Physical Review B, 105(14). https://doi.org/10.1103/PhysRevB.105.144106

MLA:

Khajehpasha, Ehsan Rahmatizad, et al. "CENT2: Improved charge equilibration via neural network technique." Physical Review B 105.14 (2022).

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