Journal
PHYSICAL REVIEW LETTERS
Volume 114, Issue 9, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevLett.114.096405
Keywords
-
Categories
Funding
- Rio Tinto Centre for Advanced Mineral Recovery at Imperial College, London
- European Commission ADGLASS FP7 Project
- EPSRC HEmS Grant [EP/L014742/1]
- King's College London
- EPSRC [EP/L027682/1]
- Office of Science of the U.S. Department of Energy [DE-AC02-06CH11357]
- Engineering and Physical Sciences Research Council [EP/L027682/1, EP/L014742/1] Funding Source: researchfish
- EPSRC [EP/L014742/1, EP/L027682/1] Funding Source: UKRI
Ask authors/readers for more resources
We present a molecular dynamics scheme which combines first-principles and machine-learning (ML) techniques in a single information-efficient approach. Forces on atoms are either predicted by Bayesian inference or, if necessary, computed by on-the-fly quantum-mechanical (QM) calculations and added to a growing ML database, whose completeness is, thus, never required. As a result, the scheme is accurate and general, while progressively fewer QM calls are needed when a new chemical process is encountered for the second and subsequent times, as demonstrated by tests on crystalline and molten silicon.
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