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.