Accelerating Electrocatalyst Innovation: High-Throughput Automated Microkinetic Modeling

Abstract

To reduce energy-related emissions, we must create chemicals from carbon dioxide with renewable energy, instead of from petroleum. Designing such a process needs computer models that can describe all the chemical reactions that are happening, including those driven by electrochemistry. There may be thousands of such reactions, so we must build a tool to find them automatically. Our reaction mechanism generator for electrocatalysis will create detailed kinetic models for many electrochemical processes, but for this initial project we are targeting the reduction of carbon dioxide to produce propanol, a useful hydrocarbon product. The kinetic models will be used to simulate an electrochemical reactor, then analyzed both technically and economically to determine which catalyst materials and reactor designs are best. This will allow new catalyst materials to be discovered using powerful computers, instead of using very time-consuming and expensive experiments.Building on the state-of-the-art open-source Reaction Mechanism Generator (RMG) software [1,2], we are creating the first automated mechanism generator for electrochemical reactions on a catalyst. Supplied with a feedstock, catalyst, and conditions, it will propose a detailed kinetic model comprising hundreds of intermediate species and reactions. Reactor simulations will predict product yields as a function of feed, concentrations, and potential, allowing conditions to be optimized and the trade-off between conversion and selectivity to be investigated. In turn this could inform a detailed technoeconomic analysis, enabling catalyst materials to be screened in silico on an economic basis. Meanwhile sensitivity analysis of the simulations will select estimated parameters for refinement with DFT calculations.We must first extend RMG to allow charged species, and implement proton coupled electron transfer (PCET) reactions. We will at first use the computational hydrogen electrode model to determine the Gibbs energy of a species (and hence reaction) as a function of chemical potential. Potential-dependent kinetics will be estimated using a charge transfer coefficient model. In preliminary work with a framework like this we have constructed a reaction mechanism for carbon dioxide reduction on Cu(111) with 37 species and 292 reactions (23 of them electrochemical) [3]. The model discovered many key electrochemical pathways to reduce CO2 to experimentally observed products such as methane, methanol, formic acid, ethylene, and ethanol. Acknowledgements The information, data, or work presented herein was funded in part by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0001786. The authors thank Dr. David Farina Jr. for his significant contributions to the proof-of-concept.