Interatomic potentials for ionic liquids: A comparative case study

Dmitry O. Zakiryanov

doi:10.15826/elmattech.2026.5.081

Interatomic potentials for ionic liquids: A comparative case study

Dmitry O. Zakiryanov

Abstract

Among all atomistic simulation approaches, empirical potentials deliver unparalleled efficiency of simulations. However, due to the simplicity of energy expressions, special attention should be given to the assessment of their validity and the fitting procedure. This paper reports a comparison of property-fitting pair models, force-fitting pair models, and deep learning potentials. We present a property-oriented fitting scheme for the development of pair potentials for ionic melts. Only basic, fast-computing properties are involved, which allows for the efficient fitting of the potentials through the iterative molecular dynamics simulations. As a case study, an alkali halide and a rare-earth halide were chosen. The force-fitting pair potentials were developed to compare the performance of the fitting schemes. The accuracy of the fitted potentials was assessed using a set of benchmark physicochemical properties calculated with the respective machine learning potentials. While the pair potential fitted via force-matching showed inconsistent performance, the property-fitting scheme turned out to be generally more reliable. The suggested fitting strategy can be used to address large-scale simulation challenges while maintaining an acceptable level of accuracy.

Keywords

molecular dynamics; machine learning; pair potentials; molten salts; ionic liquids

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References

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DOI: https://doi.org/10.15826/elmattech.2026.5.081

Copyright (c) 2026 Dmitry O. Zakiryanov

This work is licensed under a Creative Commons Attribution 4.0 International License.

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