期刊
FARADAY DISCUSSIONS
卷 211, 期 -, 页码 31-43出版社
ROYAL SOC CHEMISTRY
DOI: 10.1039/c8fd00055g
关键词
-
资金
- National Natural Science Foundation of China [11534003, 11604117, 11404128, 61775081]
- Science Challenge Project [TZ2016001]
- National Key Research and Development Program of China [2016YFB0201200]
- Program for JLU Science and Technology Innovative Research Team
Ab initio structure prediction methods have been nowadays widely used as powerful tools for structure searches and materials discovery. However, they are generally restricted to small systems owing to the heavy computational cost of the underlying density functional theory (DFT) calculations in structure optimizations. In this work, by combining a state-of-art machine learning (ML) potential with our in-house developed CALYPSO structure prediction method, we developed two acceleration schemes for the structure prediction of large systems, in which a ML potential is pre-constructed to fully replace DFT calculations or trained in an on-the-fly manner from scratch during the structure searches. The developed schemes have been applied to medium- and large-sized boron clusters, both of which are challenging cases for either the construction of ML potentials or extensive structure searches. Experimental structures of B-36 and B-40 clusters can be readily reproduced, and the putative global minimum structure for the B-84 cluster is proposed, where the computational cost is substantially reduced by approximate to 1-2 orders of magnitude if compared with full DFT-based structure searches. Our results demonstrate a viable route for structure prediction in large systems via the combination of state-of-art structure prediction methods and ML techniques.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据