Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials—A Review

• Published in: Advanced materials (Weinheim) Vol. 36; no. 22; pp. e2305758 - n/a
• Main Authors: Wan, Kaiwei, He, Jianxin, Shi, Xinghua
• Format: Journal Article
• Language: English
• Published: Germany Wiley Subscription Services, Inc 01.05.2024
• Subjects: force filed; Machine learning; molecular dynamics; Nanomaterials; physical chemistry; Potential energy; surface and interface; physical chemistry; machine learning; molecular dynamics; surface and interface; force filed
• ISSN: 0935-9648; 1521-4095
• DOI: 10.1002/adma.202305758

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. Machine learning interatomic potentials (MLIPs) have emerged as a groundbreaking tool for the precise simulation of surfaces and interfaces in nanomaterials. This review delineates the fundamental principles, practical implementations, and multifaceted applications of MLIPs for examining the surface and interfacial dynamics of nanoscale materials. Additionally, it addresses the current challenges facing this potent computational methodology.