Machine learning potential aided structure search for low-lying candidates of Au clusters

Abstract

A machine learning (ML) potential for Au clusters is developed through training on a dataset including several different sized clusters. This ML potential accurately covers the whole configuration space of Au clusters in a broad size range, thus expressing a good performance in search of their global minimum energy structures. Based on our potential, the low-lying structures of 17 different sized Au clusters are identified, which shows that small sized Au clusters tend to form planar structures while large ones are more likely to be stereo, revealing the critical size for the two-dimensional (2D) to three-dimensional (3D) structural transition. Our calculations demonstrate that ML is indeed powerful in describing the interaction of Au atoms and provides a new paradigm on accelerating the search of structures.