English(EN) Polarizable atomic multipoles for learning long-range electrostatics

AI模型利用可极化原子多极矩学习长程静电学

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-08 04:00

研究人员开发了一个新的机器学习原子间势（MLIPs）框架，解决了长程静电学和极化问题。该方法使用可极化原子多极矩来预测环境依赖的潜在单极矩、偶极矩和四极矩，同时通过线性响应捕获非局域电荷转移和极化。该框架在各种基准测试中显示出更高的准确性，并使MLIPs能够预测对极化敏感的可观测值。 AI

影响引入了一种改进MLIPs的新颖方法，有可能提高其在预测离子、极性和界面系统性质方面的准确性。

排序理由这是一篇详细介绍机器学习原子间势新科学框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Dongjin Kim, Daniel S. King, Yoonjae Park, Roya Savoj, Sebastien Hamel, Xiaoyu Wang, Bingqing Cheng · 2026-05-08 04:00

用于学习长程静电学的可极化原子多极矩

arXiv:2605.05746v1 Announce Type: cross Abstract: 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 elec…