Incorporating Coulomb interactions with fixed charges in moment tensor potentials and equivariant tensor network potentials

In this work, we incorporate long-range electrostatic interactions in the form of the Coulomb model with fixed charges into the functional form of short-range machine-learning interatomic potentials (MLIPs), particularly in the moment tensor potential and equivariant tensor network potential. We show that the explicit incorporation of the Coulomb interactions with fixed charges leads to a significant reduction of energy fitting errors, namely, more than four times, of short-range MLIPs trained on organic dimers of charged molecules. Furthermore, with our long-range models, we demonstrate a significant improvement in the prediction of the binding curves of the organic dimers of charged molecules. Finally, we show that the results calculated with MLIPs are in good correspondence with those obtained with density functional theory for organic dimers of charged molecules.

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Publication Details

Journal
The Journal of Chemical Physics
Published
2026-02-12
DOI
https://doi.org/10.1063/5.0295402
Primary Topic
Machine Learning in Materials Science
Type
article
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article

Incorporating Coulomb interactions with fixed charges in moment tensor potentials and equivariant tensor network potentials

Alexander V. Shapeev, Nikita Rybin, Max Hodapp, Dmitry Korogod et al.
The Journal of Chemical Physics
Machine Learning in Materials Science
article

Incorporating Coulomb interactions with fixed charges in moment tensor potentials and equivariant tensor network potentials

Alexander V. Shapeev, Nikita Rybin, Max Hodapp, Dmitry Korogod, Olga Chalykh, Ivan S. Novikov
article en

Abstract

In this work, we incorporate long-range electrostatic interactions in the form of the Coulomb model with fixed charges into the functional form of short-range machine-learning interatomic potentials (MLIPs), particularly in the moment tensor potential and equivariant tensor network potential. We show that the explicit incorporation of the Coulomb interactions with fixed charges leads to a significant reduction of energy fitting errors, namely, more than four times, of short-range MLIPs trained on organic dimers of charged molecules. Furthermore, with our long-range models, we demonstrate a significant improvement in the prediction of the binding curves of the organic dimers of charged molecules. Finally, we show that the results calculated with MLIPs are in good correspondence with those obtained with density functional theory for organic dimers of charged molecules.

The Journal of Chemical PhysicsVol. 164(6)
National Research University Higher School of Economics (RU), Skolkovo Institute of Science and Technology (RU), Moscow Institute of Physics and Technology (RU), Odesa I. I. Mechnikov National University (UA), Materials Center Leoben (Austria) (AT), Institute of Biochemical Physics NM Emanuel (RU)
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Machine Learning in Materials Science
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Incorporating Coulomb interactions with fixed charges in moment tensor potentials and equivariant tensor network potentials — Alexander V. Shapeev, Nikita Rybin, et al. · The Journal of Chemical Physics (2026) | TGRS Research Map | TGRS