Premium
Machine Learning Interatomic Potentials as Emerging Tools for Materials Science
Author(s) -
Deringer Volker L.,
Caro Miguel A.,
Csányi Gábor
Publication year - 2019
Publication title -
advanced materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 10.707
H-Index - 527
eISSN - 1521-4095
pISSN - 0935-9648
DOI - 10.1002/adma.201902765
Subject(s) - materials science , interatomic potential , nanotechnology , density functional theory , electronic structure , atomic units , supercapacitor , scale (ratio) , nanoparticle , work (physics) , computer science , molecular dynamics , electrode , computational chemistry , physics , thermodynamics , condensed matter physics , chemistry , quantum mechanics , electrochemistry
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.