An SO(3)-equivariant reciprocal-space neural potential for long-range interactions
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866914409754918912 |
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| author | Zhang, Lingfeng Cui, Taoyong Zhou, Dongzhan Bai, Lei Zhang, Sufei Rossi, Luca Su, Mao Ouyang, Wanli Heng, Pheng-Ann |
| author_facet | Zhang, Lingfeng Cui, Taoyong Zhou, Dongzhan Bai, Lei Zhang, Sufei Rossi, Luca Su, Mao Ouyang, Wanli Heng, Pheng-Ann |
| contents | Long-range electrostatic and polarization interactions play a central role in molecular and condensed-phase systems, yet remain fundamentally incompatible with locality-based machine-learning interatomic potentials. Although modern SO(3)-equivariant neural potentials achieve high accuracy for short-range chemistry, they cannot represent the anisotropic, slowly decaying multipolar correlations governing realistic materials, while existing long-range extensions either break SO(3) equivariance or fail to maintain energy-force consistency. Here we introduce EquiEwald, a unified neural interatomic potential that embeds an Ewald-inspired reciprocal-space formulation within an irreducible SO(3)-equivariant framework. By performing equivariant message passing in reciprocal space through learned equivariant k-space filters and an equivariant inverse transform, EquiEwald captures anisotropic, tensorial long-range correlations without sacrificing physical consistency. Across periodic and aperiodic benchmarks, EquiEwald captures long-range electrostatic behavior consistent with ab initio reference data and consistently improves energy and force accuracy, data efficiency, and long-range extrapolation. These results establish EquiEwald as a physically principled paradigm for long-range-capable machine-learning interatomic potentials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_18389 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An SO(3)-equivariant reciprocal-space neural potential for long-range interactions Zhang, Lingfeng Cui, Taoyong Zhou, Dongzhan Bai, Lei Zhang, Sufei Rossi, Luca Su, Mao Ouyang, Wanli Heng, Pheng-Ann Chemical Physics Artificial Intelligence Long-range electrostatic and polarization interactions play a central role in molecular and condensed-phase systems, yet remain fundamentally incompatible with locality-based machine-learning interatomic potentials. Although modern SO(3)-equivariant neural potentials achieve high accuracy for short-range chemistry, they cannot represent the anisotropic, slowly decaying multipolar correlations governing realistic materials, while existing long-range extensions either break SO(3) equivariance or fail to maintain energy-force consistency. Here we introduce EquiEwald, a unified neural interatomic potential that embeds an Ewald-inspired reciprocal-space formulation within an irreducible SO(3)-equivariant framework. By performing equivariant message passing in reciprocal space through learned equivariant k-space filters and an equivariant inverse transform, EquiEwald captures anisotropic, tensorial long-range correlations without sacrificing physical consistency. Across periodic and aperiodic benchmarks, EquiEwald captures long-range electrostatic behavior consistent with ab initio reference data and consistently improves energy and force accuracy, data efficiency, and long-range extrapolation. These results establish EquiEwald as a physically principled paradigm for long-range-capable machine-learning interatomic potentials. |
| title | An SO(3)-equivariant reciprocal-space neural potential for long-range interactions |
| topic | Chemical Physics Artificial Intelligence |
| url | https://arxiv.org/abs/2603.18389 |