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In this study, we introduce a training protocol for developing machinelearning force fields (MLFFs), capable of accurately determining energybarriers in catalytic reaction pathways. The protocol is validated on theextensively explored hydrogenation of carbon dioxide to methanol over indiumoxide. With the help of active learning, the final force field obtains energybarriers within 0.05 eV of Density Functional Theory. Thanks to thecomputational speedup, not only do we reduce the cost of routine in-silicocatalytic tasks, but also find a 40\% reduction in the previously establishedrate-limiting step. Furthermore, we illustrate the importance offinite-temperature effects and compute free energy barriers. Thetransferability of the protocol is demonstrated on the experimentally relevant,yet unexplored, top-layer reduced indium oxide surface. The ability of MLFFs toenhance our understanding of extensively studied catalysts underscores the needfor fast and accurate alternatives to direct ab-intio simulations.

Accurate Energy Barriers for Catalytic Reaction Pathways: An Automatic Training Protocol for Machine Learning Force Fields