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arXiv · 2501.11297

Machine-Learned Bond-Order Potential for Exploring the Configuration Space of Carbon

Ikuma Kohata·Kaoru Hisama·Keigo Otsuka·Shigeo Maruyama
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Abstract

Construction of transferable machine-learning interatomic potentials with a minimal number of parameters is important for their general applicability. Here, we present a machine-learning interatomic potential with the functional form of the bond-order potential for comprehensive exploration over the configuration space of carbon. The physics-based design of this potential enables robust and accurate description over a wide range of the potential energy surface with a small number of parameters. We demonstrate the versatility of this potential through validations across various tasks, including phonon dispersion calculations, global structure searches for clusters, phase diagram calculations, and enthalpy-volume mappings of local minima structures. We expect that this potential can contribute to the discovery of novel carbon materials.

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Ikuma Kohata, Kaoru Hisama, Keigo Otsuka, Shigeo Maruyama. 2025-12-05. Machine-Learned Bond-Order Potential for Exploring the Configuration Space of Carbon. https://doi.org/10.1103/73gp-bm46

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