CUED Publications database

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Deringer, VL and Caro, MA and Csanyi, G (2019) Machine Learning Interatomic Potentials as Emerging Tools for Materials Science. Advanced Materials. e1902765-. ISSN 0935-9648 (Unpublished)

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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.

Item Type: Article
Uncontrolled Keywords: amorphous solids atomistic modeling big data force fields molecular dynamics
Divisions: Div C > Applied Mechanics
Depositing User: Cron Job
Date Deposited: 13 Sep 2019 20:22
Last Modified: 04 Mar 2021 04:06
DOI: 10.1002/adma.201902765