Journal
ADVANCED MATERIALS
Volume 31, Issue 46, Pages -Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/adma.201902765
Keywords
amorphous solids; atomistic modeling; big data; force fields; molecular dynamics
Categories
Funding
- Leverhulme Early Career Fellowship
- Isaac Newton Trust
- Academy of Finland [310574]
- EPSRC grants [EP/K014560/1, EP/P022596/1]
- European Union [730897]
- CSC - IT Center for Science, Finland
- EPSRC [EP/P022596/1, EP/K014560/1] Funding Source: UKRI
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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.
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