Graph attention network for global search of atomic clusters: A case study of Ag n ( n = 14−26) clusters

Due to coexistence of huge number of structural isomers, global search for the ground-state structures of atomic clusters is a challenging issue. The difficulty also originates from the computational cost of ab initio methods for describing the potential energy surface. Recently, machine learning techniques have been widely utilized to accelerate materials discovery and molecular simulation. Compared to the commonly used artificial neural network, graph network is naturally suitable for clusters with flexible geometric environment of each atom. Herein we develop a cluster graph attention network (CGANet) by aggregating information of neighboring vertices and edges using attention mechanism, which can precisely predict the binding energy and force of silver clusters with root mean square error of 5.4 meV/atom and mean absolute error of 42.3 meV/Å, respectively. As a proof-of-concept, we have performed global optimization of medium-sized Ag n clusters ( n = 14−26) by combining CGANet and genetic algorithm. The reported ground-state structures for n = 14−21, have been successfully reproduced, while entirely new lowest-energy structures are obtained for n = 22−26. In addition to the description of potential energy surface, the CGANet is also applied to predict the electronic properties of clusters, such as HOMO energy and HOMO-LUMO gap. With accuracy comparable to ab initio methods and acceleration by at least two orders of magnitude, CGANet holds great promise in global search of lowest-energy structures of large clusters and inverse design of functional clusters.