Polarizable atomic multipoles for learning long-range electrostatics
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 e...
| Authors | Yoonjae Park, Dongjin Kim, Daniel S. King et al. |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 7 May 2026 |
| Last updated at source | 1 September 2026 |
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