Machine Learning Interatomic Potentials as Emerging Tools for Materials Science
By Volker L. Deringer, Miguel A. Caro, Gábor Csányi. Advances in Materials, 2019
882 citations recorded by the source corpus.
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.
Subfields: Electrical and Electronic Engineering, Materials Chemistry, Renewable Energy, Sustainability and the Environment
View Paper: https://www.semanticscholar.org/paper/7b6dc4d999d1067ef9d442576264a42574888ed9
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In Electrical and Electronic Engineering
In Materials Chemistry
Citations in the corpus
- Machine-Learning Interatomic Potentials for Materials Science (2021, background): “L potentials that begin to generate new knowledge of physics nd/or materials [24] .”