可极化的原子多极子用于学习长程静电学
Polarizable atomic multipoles for learning long-range electrostatics
- Department of Chemistry, UC Berkeley, California 94720, United States（加州大学伯克利分校化学系）
- Bakar Institute of Digital Materials for the Planet, UC Berkeley, California 94720, United States（为地球的数字材料巴卡研究所，加州大学伯克利分校）
- Lawrence Livermore National Laboratory, Livermore, CA, USA（劳伦斯利弗莫尔国家实验室）
- Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, 94720, United States（劳伦斯伯克利国家实验室化学科学部）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种半局部框架，利用可极化的原子多极子学习静电学，通过多极子层次和响应项提升势能面精度，尤其在长程效应关键系统中表现突出，恢复了物理意义的电响应。
AI中文摘要：
长程静电学和极化仍是扩展机器学习相互作用势能（MLIPs）到离子、极性和界面系统的主要障碍。本文介绍了一种半局部框架，利用可极化的原子多极子从能量和力中学习静电学。局部等变描述符预测环境依赖的潜变量单极子、偶极子和四极子，而残差非局部电荷转移和极化通过非自洽线性响应在诱导电荷和偶极子中被捕获。在四个多样化的基准和四个短程MLIP架构中，多极子层次和响应项系统性地提高了势能面的准确性，最大的收益出现在长程效应至关重要的系统中。更重要的是，学习到的潜变量恢复了物理意义的电响应：准确的Born有效电荷张量、涌现的极化率、与实验一致的红外光谱，以及半定量的水和混合MAPbI3钙钛矿的拉曼光谱。这种系统性可改进、物理透明的框架使训练于标准能量和力标签的MLIPs能够预测极化敏感的观测量。
英文摘要：
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, physically meaningful electrical responses emerge without direct supervision. The learned latent multipoles yield accurate Born effective charge tensors and infrared spectra in close agreement with experiments. The induced-dipole extension introduces new capabilities: it predicts polarizabilities and thereby enables semi-quantitative Raman spectra for bulk water and hybrid MAPbI$_3$ perovskite, as well as the essential features of the surface-specific vibrational sum-frequency generation spectrum at the water-air interface. In ferroelectric HfO$_2$, the predicted electrical response also captures LO-TO splitting and polarization switching. This systematically improvable, physically transparent framework enables MLIPs trained on standard energy and force labels to predict polarization-sensitive observables.
