Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Atomic‐scale modeling and understanding of materials have made remarkable progress, but they are still fundamentally limited by the large computational cost of explicit electronic‐structure methods such as density‐functional theory. This Progress Report shows how machine learning (ML) is currently enabling a new degree of realism in materials modeling: by “learning” electronic‐structure data, ML‐based interatomic potentials give access to atomistic simulations that reach similar accuracy levels but are orders of magnitude faster. A brief introduction to the new tools is given, and then, applications to some select problems in materials science are highlighted: phase‐change materials for memory devices; nanoparticle catalysts; and carbon‐based electrodes for chemical sensing, supercapacitors, and batteries. It is hoped that the present work will inspire the development and wider use of ML‐based interatomic potentials in diverse areas of materials research. Machine‐learning‐driven simulations are emerging as a powerful addition to the toolkit of materials modeling, giving atomic‐scale insight that would otherwise be inaccessible. This Progress Report provides a brief introduction to the new methods, highlights a few example applications in the broad field of materials science, and looks toward the future.