(406f) Accelerated Prediction of Atomically Precise Cluster Structures Using on-the-Fly Machine Learning

The chemical and structural properties of atomically precise nanoclusters are of great interest in numerous applications, but predicting the structures of the clusters can be computationally expensive. In this work, we present a procedure that can rapidly predict low-energy nanocluster structures by combining a genetic algorithm and an interatomic potential model actively learned on-the-fly. A pool-based genetic algorithm is implemented to efficiently sample the configuration space, and moment tensor potentials are used to rapidly relax and evaluate the energies of predicted clusters. The resulting procedure significantly accelerates the process of identifying low-energy cluster structures and is demonstrated on both bare and ligated clusters. The predicted lowest-energy nanoclusters are compared with the lowest energy structures reported in literature to validate this methodology. This workflow provides a feasible way to systematically predict low-energy structures for nanoclusters at a large scale, which can greatly facilitate the discovery and design of novel nanomaterials for a wide range of applications.