Machine Learning Interatomic Potentials as Emerging Tools for Materials Science
University of Cambridge · Aalto University
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;…
Citation impact
- FWCI
- 23.40
- Percentile
- 100%
- References
- 185
Authors
3- VLVolker L. DeringerCorresponding
University of Cambridge
- MAMiguel A. Caro
Aalto University
- GCGábor Cśanyi
University of Cambridge
Topics & keywords
- Materials science
- Interatomic potential
- Nanotechnology
- Density functional theory
- Electronic structure
- Atomic units
- Supercapacitor
- Scale (ratio)