Accelerated Structural Optimization for the Supported Metal System Based on Hybrid Approach Combining Bayesian Optimization with Local Search

Numerous systematic methods have been developed to search for the global minimum of the potential energy surface, which corresponds to the optimal atomic structure. However, the majority of them still demand a substantial computing load due to the relaxation process that is embedded as an inner step inside the algorithm. Here, we propose a hybrid approach that combines Bayesian optimization (BO) and a local search that circumvents the relaxation step and efficiently finds the optimum structure, particularly in supported metal systems. The hybridization strategy combining the capabilities of BO's effective exploration and the local search's fast convergence expedites structural search. In addition, the formulation of physical constraints regarding the materials system and the feature of screening structure similarity enhance the computational efficiency of the proposed method. The proposed algorithm is demonstrated in two supported metal systems, showing the potential of the proposed method in the field of structural optimization.