High-throughput screening of single-atom catalysts on 1 T-TMD for highly active and selective CO2 reduction reaction: Computational and machine learning insights

Abstract

The study of transition metals and various possible surface compositions has sparked great interest in low-cost materials, which exhibit high activity and selectivity in catalysis. While various single-atom catalysts loaded on transition metal dichalcogenide (TMD) substrates with excellent CO₂ reduction performance have been identified, the relationship between catalytic activity and the intrinsic properties of TMD single-atom catalysts remains unclear. Hence, a high-throughput first-principle computational approach is proposed to screen 24 transition metals anchored on 8 TMD monolayers to determine their catalytic activity in CO₂RR. The results show that Fe@CoS₂, Pt@TiTe₂ and Co@CoS₂ exhibit exceptional performances with low CO₂RR limiting-potentials of −0.045 eV, 0.75 eV, and 0.54 eV, respectively, showcasing selective pathways towards formic acid (HCOOH), methane (CH₄), and methanol (CH₃OH). Employing the Sure Independence Screening and Sparsifying Operator method(SISSO), key descriptors linking the performance of single-atom catalysts with their intrinsic features are identified, providing insights for the discovery of superior CO₂RR catalysts. Moreover, it was observed that a feature of the anchored single atom, the difference between covalent radius and atomic radius (CR-R), is associated with multiple crucial reaction steps, exhibiting a strong linear relationship with the charge transfer of *COOH. This work not only identifies promising CO₂RR catalysts but also establishes a predictive framework for screening catalysts based on their intrinsic properties, paving the way for future advancements in CO₂ reduction research.