Long-range electrostatics in atomistic machine learning: a physical perspective
Università degli Studi di Modena e Reggio Emilia（摩德纳与雷焦艾米利亚大学）；CNR-NANO S3（意大利国家研究委员会纳米研究所S3）；Delft University of Technology（代尔夫特理工大学）；Climate Safety and Security Centre, TU Delft The Hague Campus（代尔夫特理工大学海牙校区气候安全与中心）；Scuola Internazionale Superiore di Studi Avanzati (SISSA)（高等国际研究学院）；Sorbonne Université（索邦大学）；CNRS（法国国家科学研究中心）
Federico Grasselli, Kevin Rossi, Stefano de Gironcoli, Andrea Grisafi
The inclusion of long-range electrostatics in atomistic machine learning (ML) is receiving increasing attention for achieving quantum-mechanical accuracy in predicting a wide range of molecular and material properties. However, there is still no general prescription for how long-range physical effects should be incorporated into a model while preserving the well-established locality principles underlying most transferable ML representations. Here, we provide a physical perspective on this problem by discussing how distinct contributions to a system's electrostatics can be captured through different learning paradigms. Specifically, we distinguish between local charge models, which rely either on explicit charge-density decompositions or implicit auxiliary variables, and models in which a notion of nonlocality is deliberately introduced, either via self-consistent procedures or by using nonlocal descriptors and learning architectures. We further address the related aspect of incorporating finite-field effects through coupling to the system's polarization, relevant for applications involving an external electric bias. We conclude by discussing implications for simulations of electrochemical interfaces, where long-range electrostatics are essential to capture the interplay between charge redistribution, interfacial dynamics, and ionic screening, as well as for ionic transport phenomena, which, although less explored, appear far less sensitive to their inclusion.
qNEP: A highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulations
College of Physical Science and Technology, Bohai University（渤海大学物理科学与技术学院）；Suzhou Laboratory（苏州实验室）；Chalmers University of Technology（查尔姆斯理工大学）；Department of Materials Science and Engineering, Westlake University（西湖大学材料科学与工程系）；School of Science, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）理学院）；Shenzhen Key Laboratory of Micro/Nano-Porous Functional Materials (SKLPM), Department of Materials Science and Engineering, Southern University of Science and Technology（南方科技大学材料科学与工程系微/纳孔功能材料深圳市重点实验室）；Department of Materials Science and Engineering, City University of Hong Kong（香港城市大学材料科学与工程系）
Distilling latent electrostatics from foundation machine learning interatomic potentials
Department of Chemistry, UC Berkeley（加州大学伯克利分校化学系）；Bakar Institute of Digital Materials for the Planet, UC Berkeley（伯克利大学数字材料研究所）；Chemical Sciences Division, Lawrence Berkeley National Laboratory（伯克利国家实验室化学科学部）
A molecular perspective on coordination, screening, and emergent length scales in lithium electrolytes
Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CEA（法国原子能和替代能源委员会）；IRIG-MEM-LSim；Univ. Savoie Mont Blanc（萨瓦蒙布朗大学）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔国立理工学院）；LEPMI；CEA, LITEN（CEA-LITEN）；Laboratory of Physical Chemistry, Department of Chemistry, University of Ioannina（约阿尼纳大学化学系物理化学实验室）
Long-range electrostatics for machine learning interatomic potentials is easier than we thought
Department of Chemistry, UC Berkeley, California 94720, United States（加州大学伯克利分校化学系）；Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California, 94720, United States（伯克利国家实验室化学科学部）；Bakar Institute of Digital Materials for the Planet, UC Berkeley, California 94720, United States（为地球数字化材料研究所）；The Institute of Science and Technology Austria, Am Campus 1, 3400 Klosterneuburg, Austria（奥地利科学与技术研究所）
Learning charges and long-range interactions from energies and forces
UC Berkeley（加州大学伯克利分校）；Bakar Institute of Digital Materials for the Planet, UC Berkeley（加州大学伯克利分校行星数字材料巴卡尔研究所）；The Institute of Science and Technology Austria（奥地利科学技术研究所）
