Wan Kaiwei, He Jianxin, Shi Xinghua
Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology, Chinese Academy of Sciences, Beijing, 100190, China.
University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing, 100049, China.
Adv Mater. 2024 May;36(22):e2305758. doi: 10.1002/adma.202305758. Epub 2023 Nov 30.
The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and interfaces bestow them with various exceptional properties. These properties, however, also introduce difficulties for both experimental and computational studies. The advent of machine learning interatomic potential (MLIP) addresses some of the limitations associated with empirical force fields, presenting a valuable avenue for accurate simulations of these surfaces/interfaces of nanomaterials. Central to this approach is the idea of capturing the relationship between system configuration and potential energy, leveraging the proficiency of machine learning (ML) to precisely approximate high-dimensional functions. This review offers an in-depth examination of MLIP principles and their execution and elaborates on their applications in the realm of nanomaterial surface and interface systems. The prevailing challenges faced by this potent methodology are also discussed.
纳米材料表面和界面固有的不连续性及独特的维度属性赋予了它们各种优异特性。然而，这些特性也给实验研究和计算研究带来了困难。机器学习原子间势（MLIP）的出现解决了一些与经验力场相关的局限性，为精确模拟这些纳米材料的表面/界面提供了一条有价值的途径。这种方法的核心思想是捕捉系统构型与势能之间的关系，利用机器学习（ML）的能力精确逼近高维函数。本综述深入探讨了MLIP原理及其实现，并阐述了它们在纳米材料表面和界面系统领域的应用。还讨论了这种强大方法面临的主要挑战。
