(641g) Advancing Automated Mechanism Generation for Electrocatalytic CO2 Reduction: Multi-Metallic Catalysis and Improved Reactor Simulations

The pressing challenge of climate change has intensified research into sustainable CO₂ conversion. Electrocatalytic CO₂ reduction provides a viable route to transform renewable electricity into chemical energy, yielding carbon-neutral fuels and chemical feedstocks. However, designing efficient catalysts and optimizing reaction conditions require a deep understanding of intricate reaction networks, often involving hundreds of species and thousands of elementary steps. This study advances automated reaction mechanism generation for electrocatalytic CO₂ reduction by integrating quantum chemistry data, improved kinetic models, and transport effects into the Reaction Mechanism Generator (RMG).

Building on our prior work, we integrated ab initio thermodynamics and kinetics from density functional theory (DFT) calculations to improve the predictions of reaction pathways and activation barriers. To explore new catalytic materials, we introduced a Cu-Sn alloy surface into RMG and investigated its performance in electrocatalytic CO₂ reduction, focusing on its ability to potentially enhance selectivity toward valuable products such as ethanol. This study represents the first implementation of multi-metallic alloy catalysis within RMG, expanding its capabilities beyond single-metal surfaces. The Cu-Sn alloy system serves as a case study to assess ethanol production potential, with results benchmarked against Cu(111), Ag(111), predicted Cu-Sn performance from literature, and experimental data.

To better capture the complexity of electrochemical reaction environments, we developed a novel kinetics model incorporating potential-dependent effects and transport limitations, allowing for more accurate reaction rate predictions under operating conditions. Furthermore, a new reactor model was implemented, integrating a transport interface to simulate mass transfer at the electrode-electrolyte boundary, bridging the gap between mechanistic studies and real-world electrocatalytic systems. These enhancements collectively improve the predictive power of automated mechanism generation on catalyst performance and reaction dynamics. More broadly, these developments enhance RMG’s applicability to electrochemical systems, providing a valuable tool for accelerating the discovery and optimization of electrocatalysts in sustainable energy applications.