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Artificial neural network potential for gold clusters
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作者 ling-zhi cao Peng-Ju Wang +2 位作者 Lin-Wei Sai Jie Fu Xiang-Mei Duan 《Chinese Physics B》 SCIE EI CAS CSCD 2020年第11期86-91,共6页
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 r... 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 Au N clusters(11 ≤ N ≤ 100). The root mean square errors of energy and force are 13.4 meV/atom and 0.4 eV/A, 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. 展开更多
关键词 empirical potential artificial neural network gold cluster FIRST-PRINCIPLES
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