Artificial neural network potential for gold clusters

doi:10.1088/1674-1056/abc15d

Abstract

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/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.

Keywords: empirical potential artificial neural network gold cluster first-principles

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.

Corresponding Authors: ^†Corresponding author. E-mail: fujie@nbu.edu.cn ^‡Corresponding author. E-mail: duanxiangmei@nbu.edu.cn

Cite this article:

Ling-Zhi Cao(曹凌志), Peng-Ju Wang(王鹏举), Lin-Wei Sai(赛琳伟), Jie Fu(付洁), and Xiang-Mei Duan(段香梅) Artificial neural network potential for gold clusters 2020 Chin. Phys. B 29 117304

Fig. 1.

Schematic diagram of an ANN model (N₀–N₁–⋯–N_n).

Table 1.

ANN architecture complexity test. Root mean square error (RMSE) and mean absolute error (MAE) are the values after 1000 iterations.

Fig. 2.

Comparison of binding energy and force calculated by ANN and DFT.

Fig. 3.

Calculated binding energies of Au_{11 – 80} clusters by different approaches.

Fig. 4.

Comparison of binding energies of Au₂₀, Au₄₀, Au₆₀, and Au₈₀ cluster isomers by ANN and DFT.

Fig. 5.

Binding energies and relative binding energies of Au₂₇ isomers by ANN and DFT.

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