Journal
PHYSICAL REVIEW MATERIALS
Volume 3, Issue 2, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevMaterials.3.023804
Keywords
-
Categories
Funding
- NNSFC [91130005]
- ONR [N00014-13-1-0338]
- NSFC [U1430237]
- DOE [DE-SC0019394]
- National Science Foundation of China [11501039, 11871110, 91530322]
- National Key Research and Development Program of China [2016YFB0201200, 2016YFB0201203]
- Science Challenge Project [JCKY2016212A502]
- Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund [U1501501]
Ask authors/readers for more resources
An active learning procedure called deep potential generator (DP-GEN) is proposed for the construction of accurate and transferable machine learning-based models of the potential energy surface (PES) for the molecular modeling of materials. This procedure consists of three main components: exploration, generation of accurate reference data, and training. Application to the sample systems of Al, Mg, and Al-Mg alloys demonstrates that DP-GEN can produce uniformly accurate PES models with a minimal number of reference data.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available