Artificial neural network potential for gold clusters
Ling-Zhi Cao(曹凌志)1,2, Peng-Ju Wang(王鹏举)3, Lin-Wei Sai(赛琳伟)4, Jie Fu(付洁)1,2, †, and Xiang-Mei Duan(段香梅)1,2,, ‡
1 School of Physical Science and Technology, Ningbo University, Ningbo 315211, China 2 Laboratory of Clean Energy Storage and Conversion, Ningbo University, Ningbo 315211, China 3 Key Laboratory of Materials Modification by Laser, Ion and Electron Beams, Ministry of Education, Dalian University of Technology, Dalian 116024, China 4 College of Science, Hohai University, Changzhou 213022, China
In cluster science, it is challenging to identify the ground state structures (GSS) of gold (Au) clusters. Among different search approaches, first-principles method based on density functional theory (DFT) is the most reliable one with high precision. However, as the cluster size increases, it requires more expensive computational cost and becomes impracticable. In this paper, we have developed an artificial neural network (ANN) potential for Au clusters, which is trained to the DFT binding energies and forces of 9000 AuN clusters (11 ≤ N ≤ 100). The root mean square errors of energy and force are 13.4 meV/atom and 0.4 eV/Å, respectively. We demonstrate that the ANN potential has the capacity to differentiate the energy level of Au clusters and their isomers and highlight the need to further improve the accuracy. Given its excellent transferability, we emphasis that ANN potential is a promising tool to breakthrough computational bottleneck of DFT method and effectively accelerate the pre-screening of Au clusters’ GSS.
Received: 10 August 2020
Revised: 14 September 2020
Accepted manuscript online: 15 October 2020
Fund: the National Natural Science Foundation of China (Grant Nos. 11804175, 11874033, 11804076, and 91961204) and the K.C. Wong Magna Foundation in Ningbo University.
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