Microkinetics of CO2 hydrogenation to methanol on In2O3-supported Ni-In clusters

Highly dispersed Ni-In clusters supported on In 2 O 3 are promising catalysts for CO 2 hydrogenation to methanol, yet the role of cluster composition in determining activity and selectivity remains underexplored. In this study, we combine density functional theory (DFT) and microkinetic modeling to investigate how the composition of Ni n In 8-n clusters (n = 0–7) on the In 2 O 3 (111) surface influences catalytic behavior. Stable cluster structures are identified using genetic algorithms and a neural network potential trained on DFT data. The extent of metal-support interaction varies with composition, affecting cluster morphology: In-rich clusters feature Ni atoms embedded and surrounded by In, while Ni-rich clusters expose more Ni atoms at the surface. We analyze reaction mechanisms for two representative clusters, Ni 2 In 6 (In-rich) and Ni 6 In 2 (Ni-rich). Microkinetic simulations reveal that Ni 6 In 2 facilitates methanol formation via hydrogenation of CO 2 adsorbed in oxygen vacancies by Ni-H species. However, at elevated temperatures, CO formation dominates due to reduced Ni-H coverage. In contrast, Ni 2 In 6 shows high CO selectivity, attributed to the scarcity of surface Ni atoms, which raises barriers for formate hydrogenation relative to direct CO 2 dissociation. These findings highlight the critical influence of cluster composition and structure on catalytic performance, offering insights for the rational design of selective CO 2 hydrogenation catalysts. • NN-GA approach identifies stable Ni–In clusters on In₂O₃ for CO₂ hydrogenation. • Cluster composition dictates methanol vs CO selectivity in CO₂ hydrogenation. • Ni-rich clusters favor methanol via CO hydrogenation; In-rich clusters yield CO. • Microkinetic modeling links surface H coverage to temperature-dependent selectivity. • Machine learning accelerates catalyst design for CO₂-to-methanol conversion.