Polarizable atomic multipoles for learning long-range electrostatics
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911655055589376 |
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| author | Kim, Dongjin King, Daniel S. Park, Yoonjae Savoj, Roya Hamel, Sebastien Wang, Xiaoyu Cheng, Bingqing |
| author_facet | Kim, Dongjin King, Daniel S. Park, Yoonjae Savoj, Roya Hamel, Sebastien Wang, Xiaoyu Cheng, Bingqing |
| contents | Long-range electrostatics and polarization remain central obstacles to extending machine learning interatomic potentials (MLIPs) to ionic, polar, and interfacial systems. Here, we introduce a semi-local framework for learning electrostatics from energies and forces using polarizable atomic multipoles. Local equivariant descriptors predict environment-dependent latent monopoles, dipoles, and quadrupoles, while residual non-local charge transfer and polarization are captured by non-self-consistent linear response in induced charges and dipoles. Across four diverse benchmarks and four short-range MLIP architectures, the multipole hierarchy and response terms systematically improve potential energy surface accuracy, with the largest gains in systems where long-range effects are essential. More importantly, the learned latent variables recover physically meaningful electrical responses: accurate Born effective charge tensors, emergent polarizabilities, infrared spectra in close agreement with experiments, and semi-quantitative Raman spectra for bulk water and hybrid MAPbI$_3$ perovskite. This systematically improvable, physically transparent framework enables MLIPs trained on standard energy and force labels to predict polarization-sensitive observables. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_05746 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Polarizable atomic multipoles for learning long-range electrostatics Kim, Dongjin King, Daniel S. Park, Yoonjae Savoj, Roya Hamel, Sebastien Wang, Xiaoyu Cheng, Bingqing Materials Science Machine Learning Chemical Physics Computational Physics Long-range electrostatics and polarization remain central obstacles to extending machine learning interatomic potentials (MLIPs) to ionic, polar, and interfacial systems. Here, we introduce a semi-local framework for learning electrostatics from energies and forces using polarizable atomic multipoles. Local equivariant descriptors predict environment-dependent latent monopoles, dipoles, and quadrupoles, while residual non-local charge transfer and polarization are captured by non-self-consistent linear response in induced charges and dipoles. Across four diverse benchmarks and four short-range MLIP architectures, the multipole hierarchy and response terms systematically improve potential energy surface accuracy, with the largest gains in systems where long-range effects are essential. More importantly, the learned latent variables recover physically meaningful electrical responses: accurate Born effective charge tensors, emergent polarizabilities, infrared spectra in close agreement with experiments, and semi-quantitative Raman spectra for bulk water and hybrid MAPbI$_3$ perovskite. This systematically improvable, physically transparent framework enables MLIPs trained on standard energy and force labels to predict polarization-sensitive observables. |
| title | Polarizable atomic multipoles for learning long-range electrostatics |
| topic | Materials Science Machine Learning Chemical Physics Computational Physics |
| url | https://arxiv.org/abs/2605.05746 |