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arXiv Machine Learning By Yoonjae Park, Dongjin Kim, Daniel S. King, Nam H. {\DJ}\`ao, Roya Savoj, Sebastien Hamel, Xiaoyu Wang, Bingqing Cheng

Polarizable atomic multipoles for learning long-range electrostatics

Read the original on arXiv Machine Learning →

The paper presents a semi‑local framework that learns long‑range electrostatics and polarization for machine‑learning interatomic potentials using polarizable atomic multipoles. Local equivariant descriptors predict environment‑dependent monopoles, dipoles, and quadrupoles, while a non‑self‑consistent linear response captures residual charge transfer and polarization. Across multiple benchmarks and MLIP architectures, this multipole hierarchy systematically improves potential energy surface accuracy and yields physically meaningful electrical responses, including accurate Born effective charges, infrared and Raman spectra, and surface‑specific vibrational signatures.

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