Modeling CoCu Nanoparticles Using Neural Network-Accelerated Monte Carlo Simulations.

The correct description of catalytic reactions happening on bimetallic particles is not feasible without proper accounting of the segregation process. In this study, we tried to shed light on the structure of large CoCu particles, for which quite controversial results were published before. However, density functional theory (DFT) is challenging to be directly used for the systematic study of nanometer-sized particles. Therefore, we constructed a neural network-based potential and further applied it to the Monte Carlo simulations for the description of the segregation phenomenon. The resulting approach shows high efficiency and can be used in systems with thousands of atoms. The accuracy and transferability of the model to other sizes and compositions make this methodology useful for solving segregation problems.