Artificial neural network potential for Au20 clusters based on the first-principles. (27th April 2022)

• Record Type: Journal Article
• Authors: Cao, Lingzhi; Guo, Yibo; Han, Wenhua; Xu, Wenwu; Sai, Linwei; Fu, Jie
• Is Part Of: Journal of physics. Volume 34:Number 17(2022)
• Publication Date: 2022-04-27
• Subjects: artificial neural network -- gold cluster -- ground state structure -- global optimization; Condensed matter -- Periodicals; Matière condensée -- Périodiques; Vaste stoffen; Vloeistoffen; Natuurkunde; Electronic journals; Computer network resources; 530.4105
• DOI: 10.1088/1361-648X/ac4f7d
• Languages: English
• ISSNs: 0953-8984

Abstract

Abstract: The search of ground-state structures (GSSs) of gold (Au) clusters is a formidable challenge due to the complexity of potential energy surface (PES). In this work, we have built a high-dimensional artificial neural network (ANN) potential to describe the PES of Au20 clusters. The ANN potential is trained through learning the GSS search process of Au20 by the combination of density functional theory (DFT) method and genetic algorithm. The root mean square errors of energy and force are 7.72 meV atom −1 and 217.02 meV Å −1, respectively. As a result, it can find the lowest-energy structure (LES) of Au20 clusters that is consistent with previous results. Furthermore, the scalability test shows that it can predict the energy of smaller size Au16–19 clusters with errors less than 22.85 meV atom −1, and for larger size Au21–25 clusters, the errors are below 36.94 meV atom −1 . Extra attention should be paid to its accuracy for Au21–25 clusters. Applying the ANN to search the GSSs of Au16–25, we discover two new structures of Au16 and Au21 that are not reported before and several candidate LESs of Au16–18 . In summary, this work proves that an ANN potential trained for specific size clusters could reproduce the GSS search process by DFT and be applied in the GSS search of smaller size clusters nearby. Therefore, we claim that building ANN potential based on DFT data is one of the most promising ways to effectively accelerate the GSS pre-screening of clusters.