Machine Learning Interatomic Potentials as Emerging Tools for Materials Science.

Abstract

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.Academy of Finland under project #310574. The authors are thankful for generous allocation of computational resources on the ARCHER UK National Supercomputing Service (EPSRC grants EP/K014560/1 and EP/P022596/1) and by CSC ‐ IT Center for Science, Finland, which supported some of the work discussed herein. V.L.D. and M.A.C. are grateful for mutual HPC‐Europa3 exchange visits (funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No. 730897), during one of which this manuscript was finalized