Dry reforming of methane on doped Ni nanoparticles: Feature-assisted optimizations and ranking of doping metals for direct activations of CH₄ and CO₂

As a vital energy resource and raw material for many industrial products, syngas (CO and H₂) is of great significance. Dry reforming of methane (DRM) is an important approach to producing syngas (with a hydrogen-to-carbon-monoxide ratio of 1:1 in principle) from methane and carbon dioxide, with a lower operational cost as compared to other reforming techniques. However, many pure metallic catalysts used in DRM face deactivation issues due to coke formation or sintering of the metal particles. A systematic search for highly efficient metallic catalysts, which reduce the reaction barriers for the rate-determining steps and resist carbon deposition, is urgently needed. Nickel is a typical low-cost transition metal for activating the C–H bond in methane. In this work, we applied a two-step workflow to search for nickel-based bimetallic catalysts with doping metals M (M-Ni) by combining density functional theory (DFT) calculations and machine learning (ML). We focus on the two critical steps in DRM—CH₄ and CO₂ direct activations. We used DFT and slab models for the Ni(111) facet to explore the relevant reaction pathways and constructed a data set containing structural and energetic information for representative M-Ni systems. We used this dataset to train ML models with chemical-knowledge-based features and predicted CH₄ and CO₂ dissociation energies and barriers, which revealed the composition–activity relationships of the bimetallic catalysts. We also used these models to rank the predicted catalytic performance of candidate systems to demonstrate the applicability of ML for catalyst screening. We emphasized that ML ranking models would be more valuable than regression models in high-throughput screenings. Finally, we used our trained model to screen 12 unexplored M-Ni systems and showed that the DFT-computed energies and barriers are very close to the ML-predicted values for top candidates, validating the robustness of the trained model.