Generalizing Abell-Tersoff bond-order potential with explicit high-order many-body correlations for robust extrapolation of potential energy surfaces

[2608.22933] Generalizing Abell-Tersoff bond-order potential with explicit high-order many-body correlations for robust extrapolation of potential energy surfaces Skip to main content Search Submit Donate Log in Physics > Chemical Physics arXiv:2608.22933 (physics) [Submitted on 24 Aug 2026 ( v1 ), last revised 27 Aug 2026 (this version, v3)] Title: Generalizing Abell-Tersoff bond-order potential with explicit high-order many-body correlations for robust extrapolation of potential energy surfaces Authors: Ikuma Kohata View PDF HTML (experimental) Abstract: Machine-learning interatomic potentials enable accurate and efficient atomistic simulations, yet their reliability for out-of-distribution configurations far beyond the training domain remains a significant challenge. Here, we introduce a semiparametric interatomic potential based on a generalization of the Abell--Tersoff bond-order potential, incorporating a chemically informed functional form and explicit high-order many-body correlations to address this challenge. The model is trained and evaluated on various datasets of silicon, carbon, water, and small molecules, achieving interpolation accuracy comparable to existing MLIP models while exhibiting improved extrapolation to unseen configurations, including those at high pressures and temperatures. These results provide insights into the design of specific inductive biases for reliable extrapolation in interatomic potentials. inductive biases that can be used to control extrapolation in machine-learning interatomic potentials. Comments: 23 pages, 17 figures, 8 tables Subjects: Chemical Physics (physics.chem-ph) ; Disordered Systems and Neural Networks (cond-mat.dis-nn); Materials Science (cond-mat.mtrl-sci) Cite as: arXiv:2608.22933 [physics.chem-ph] (or arXiv:2608.22933v3 [physics.chem-ph] for this version) https://doi.org/10.48550/arXiv.2608.22933 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Ikuma Kohata [ view email ] [v1] Mon, 24 Aug 2026 08:07:22 UTC (8,676 KB) [v2] Wed, 26 Aug 2026 06:12:22 UTC (10,346 KB) [v3] Thu, 27 Aug 2026 06:10:11 UTC (10,349 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: physics.chem-ph < prev | next > new | recent | 2026-08 Change to browse by: cond-mat cond-mat.dis-nn cond-mat.mtrl-sci physics References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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