GMMLP: An Efficient Software for Searching the Global-Minimum of Clusters Accelerated by Using the Machine Learning Potentials.

Searching for the global-minimum (GM) structure of clusters is a fundamental challenge in computational chemistry, as the potential energy surface (PES) of clusters exhibits a vast number of local minima that increase exponentially with cluster size. This work presents GMMLP (Global-Minimum Search of Clusters Accelerated by Machine Learning Potentials), an efficient software package developed for identifying the GM structures of clusters. GMMLP integrates the atom-in-molecules neural network potential (AIMNet2) with an improved genetic algorithm (GA), leveraging the high accuracy of AIMNet2 trained at the ωB97M-D3/def2-TZVPP level of theory and the global search capability of the optimized GA. To validate GMMLP, benchmark tests were performed on nine types of clusters, including (CH2O)n, (CH3NH2)n, (CH3OH)n, (CH4)n, (H2O)n, (H2SO4)n, (HNO3)n, (NH3)n, and [CO(NH2)2]n (n = 1-10). Computational results show that GMMLP efficiently explores the PES, searching a total of 9869 isomers across all benchmarked clusters with a total wall time of 39,055.14 s (~10.8 h). The average computational time per isomer ranges from 0.22 s for (CH4)n to 10.01 s for (H2SO4)n, demonstrating remarkable efficiency. Additionally, the evolution of relative energy and optimized structures of low-lying isomers are analyzed to illustrate the reliability of the search process. GMMLP provides a powerful tool for cluster research, enabling fast and accurate GM structure identification for a wide range of clusters, which is crucial for understanding cluster properties and their applications in chemistry, materials science, and related fields.