Exploring Challenging C2+ Products During CO2 Reduction via Machine Learning Acceleration

Currently, producing some C₂₊ products by CO₂ reduction is still challenging such as ethane, ketene, etc. Based on the first-principles machine learning technique, the systematic explorations of reaction mechanisms for the challenging formation of ethane, ketene, ethyne and dimethyl ether for the first time, revealing underlying factors that affect the reaction pathways and product selectivity are successfully realized.

Abstract

Although CO₂ reduction reaction (CO₂RR) has achieved significant progress in past years, the C₂₊ products are mainly limited to a few products, while many other products have rarely been reported in experiments with a limited understanding of the underlying mechanisms. Accordingly, in this work, machine learning (ML)-based theoretical investigations is conducted to uncover the reaction mechanisms for the conversion to challenging C₂₊ products (C₂H₆, CH₃OCH₃, CH₂CO, and C₂H₂) during CO₂RR on graphdiyne-supported atomic catalysts (GDY-ACs) with well-defined active sites. Using the first-principles machine learning (FPML) predictions, key factors limiting the diversity of C₂₊ products are identified. The conversions to C₂H₆ are mainly hindered by large rate-determining step (RDS) barriers (>4 eV). The formation of CH₂CO meets the competitive reactions due to similar reaction pathways with C₂H₆, which also undergoes further hydrogenation easily to other C₂₊ products. The CH₃OCH₃ formation is hindered by large dehydration barriers caused by steric hindrance induced by the neighboring adsorption of C₁ intermediates. FPML predictions also reveal the significance of binding configuration parameters in realizing efficient and accurate predictions. This work offers not only important references to the low selectivity of specific C₂₊ products but also critical theoretical insights into CO₂RR mechanisms.