Theoretical High-Throughput Screening of Single-Atom CO2 Electroreduction Catalysts to Methanol Using Active Learning

Abstract

Industrial decarbonization is critical for achieving net-zero goals. The carbon dioxide electrochemical reduction reaction (CO₂RR) is a promising approach for converting CO₂ into high-value chemicals, offering the potential for decarbonizing industrial processes toward a sustainable, carbon-neutral future. However, developing CO₂RR catalysts with high selectivity and activity remains a challenge due to the complexity of finding such catalysts and the inefficiency of traditional computational or experimental approaches. Here, we present a methodology integrating density functional theory (DFT) calculations, deep learning models, and an active learning strategy to rapidly screen high-performance catalysts. The proposed methodology is then demonstrated on graphene-based single-atom catalysts for selective CO₂ electroreduction to methanol. First, we conduct systematic binding energy calculations for 3045 single-atom catalysts to identify thermodynamically stable catalysts as the design space. We then use a graph neural network, fine-tuned with a specialized adsorption energy database, to predict the relative activity and selectivity of the candidate catalysts. An autonomous active learning framework is used to facilitate the exploration of designs. After six learning cycles and 2180 adsorption calculations across 15 intermediates, we develop a surrogate model that identifies four novel catalysts on the Pareto front of activity and selectivity. Our work demonstrates the effectiveness of leveraging a domain foundation model with an active learning framework and holds potential to significantly accelerate the discovery of high-performance CO₂RR catalysts.