.png){kind=icon}
.png){kind=icon}
Return
Machine learning for interatomic potential models
.png){kind=icon}
OA
AI
DOI：10.1063/1.5126336.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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, Jorg 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. Published under license by AIP Publishing.
Keywords:
REACTIVE FORCE-FIELD
EMBEDDED-ATOM METHOD
SCALING DFT CALCULATIONS
THERMAL-CONDUCTIVITY
MOLECULAR-DYNAMICS
ACCURATE
PARAMETERS
SIMULATIONS
UNCERTAINTY
PRINCIPLES
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.1
Papers:
7.2W
Citations:
23.2W