Machine learning for interatomic potential models
Abstract
The use of supervised machine learning to develop fast and accurate interatomic potential models is transforming molecular and materials research by greatly accelerating atomic-scale simulations with little loss of accuracy. Three years ago, Jörg Behler published a perspective in this journal providing an overview of some of the leading methods in this field. In this perspective, we provide an updated discussion of recent developments, emerging trends, and promising areas for future research in this field. We include in this discussion an overview of three emerging approaches to developing machine-learned interatomic potential models that have not been extensively discussed in existing reviews: moment tensor potentials, message-passing networks, and symbolic regression.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 05, 2020
- Source ID
- 10.1063/1.5126336
Entities
People
- Alberto Hernandez
- Chuhong Wang
- Tim Mueller
Organizations
- Johns Hopkins University
- Office of Naval Research