Machine Learning Molecular Dynamics Shows Anomalous Entropic Effect on Catalysis through Surface Pre‐melting of Nanoclusters

Abstract Due to the superior catalytic activity and efficient utilization of noble metals, nanocatalysts are extensively used in the modern industrial production of chemicals. The surface structures of these materials are significantly influenced by reactive adsorbates, leading to dynamic behavior under experimental conditions. The dynamic nature poses significant challenges in studying the structure–activity relations of catalysts. Herein, we unveil an anomalous entropic effect on catalysis via surface pre‐melting of nanoclusters through machine learning accelerated molecular dynamics and free energy calculation. We find that due to the pre‐melting of shell atoms, there exists a non‐linear variation in the catalytic activity of the nanoclusters with temperature. Consequently, two notable changes in catalyst activity occur at the respective temperatures of melting for the shell and core atoms. We further study the nanoclusters with surface point defects, i.e. vacancy and ad‐atom, and observe significant decrease in the surface melting temperatures of the nanoclusters, enabling the reaction to take place under more favorable and milder conditions. These findings not only provide novel insights into dynamic catalysis of nanoclusters but also offer new understanding of the role of point defects in catalytic processes.