A Fast, Accurate, and Reactive Equivariant Foundation Potential
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915611671527424 |
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| author | Ko, Tsz Wai Liu, Runze Mishra, Adesh Rohan Yu, Zihan Qi, Ji Ong, Shyue Ping |
| author_facet | Ko, Tsz Wai Liu, Runze Mishra, Adesh Rohan Yu, Zihan Qi, Ji Ong, Shyue Ping |
| contents | Electrostatics govern charge transfer and reactivity in materials. Yet, most foundation potentials (FPs) either do not explicitly model such interactions or pay a prohibitive scaling penalty to do so. Here, we introduce charge-equilibrated TensorNet (QET), an equivariant, charge-aware architecture that attains linear scaling with system size via an analytically solvable charge-equilibration scheme. We demonstrate that a trained QET FP matches state-of-the-art FPs on standard materials property benchmarks but delivers qualitatively different predictions in systems dominated by charge transfer. The QET FP reproduces the correct structure and density of the NaCl-CaCl2 ionic liquid, which charge-agnostic FPs miss. We further show that a fine-tuned QET captures reactive processes at the Li/Li6PS5Cl solid-electrolyte interface and supports simulations under applied electrochemical potentials. These results remove a fundamental constraint in the atomistic simulation of accurate electrostatics at scale and establish a general, data-driven framework for charge-aware FPs with transformative applications in energy storage, catalysis, and beyond. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_07249 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Fast, Accurate, and Reactive Equivariant Foundation Potential Ko, Tsz Wai Liu, Runze Mishra, Adesh Rohan Yu, Zihan Qi, Ji Ong, Shyue Ping Materials Science Chemical Physics Electrostatics govern charge transfer and reactivity in materials. Yet, most foundation potentials (FPs) either do not explicitly model such interactions or pay a prohibitive scaling penalty to do so. Here, we introduce charge-equilibrated TensorNet (QET), an equivariant, charge-aware architecture that attains linear scaling with system size via an analytically solvable charge-equilibration scheme. We demonstrate that a trained QET FP matches state-of-the-art FPs on standard materials property benchmarks but delivers qualitatively different predictions in systems dominated by charge transfer. The QET FP reproduces the correct structure and density of the NaCl-CaCl2 ionic liquid, which charge-agnostic FPs miss. We further show that a fine-tuned QET captures reactive processes at the Li/Li6PS5Cl solid-electrolyte interface and supports simulations under applied electrochemical potentials. These results remove a fundamental constraint in the atomistic simulation of accurate electrostatics at scale and establish a general, data-driven framework for charge-aware FPs with transformative applications in energy storage, catalysis, and beyond. |
| title | A Fast, Accurate, and Reactive Equivariant Foundation Potential |
| topic | Materials Science Chemical Physics |
| url | https://arxiv.org/abs/2511.07249 |