A machine learning based deep potential for seeking the low-lying candidates of Al clusters

P Tuo; X B Ye; B C Pan

doi:10.1063/5.0001491

A machine learning based deep potential for seeking the low-lying candidates of Al clusters

J Chem Phys. 2020 Mar 21;152(11):114105. doi: 10.1063/5.0001491.

Authors

P Tuo¹, X B Ye², B C Pan¹

Affiliations

¹ Key Laboratory of Strongly-Coupled Quantum Matter Physics, Department of Physics, University of Science and Technology of China, Hefei, Anhui 230026, People's Republic of China.
² Hefei National Laboratory for Physical Sciences at Microscale, University of Science and Technology of China, Hefei, Anhui 230026, People's Republic of China.

PMID: 32199435
DOI: 10.1063/5.0001491

Abstract

A Machine-Learning based Deep Potential (DP) model for Al clusters is developed through training with an extended database including ab initio data of both bulk and several clusters in only 6 CPU/h. This DP model has good performance in accurately predicting the low-lying candidates of Al clusters in a broad size range. Based on our developed DP model, the low-lying structures of 101 different sized Al clusters are extensively searched, among which the lowest-energy candidates of 69 sized clusters are updated. Our calculations demonstrate that machine-learning is indeed powerful in generating potentials to describe the interaction of atoms in complex materials.