(Invited) Computational Insights into Cathode Electrocatalyst Design for Metal-CO 2 Batteries

Carbon dioxide (CO₂) is a major greenhouse gas contributing significantly to global warming and environmental imbalance. Among emerging energy technologies aimed at addressing this challenge, metal–CO₂ batteries have garnered considerable attention due to their unique ability to harness green electricity through CO2 consumption. In particular, magnesium–CO₂ (Mg–CO₂) batteries hold great promise owing to magnesium's high volumetric capacity, natural abundance, and low cost. Despite their potential, the practical realization of Mg–CO₂ batteries is hindered by several critical challenges, including sluggish CO₂ reduction reaction kinetics, poor reversibility during charge–discharge cycles, and high charge overpotentials. Addressing these obstacles necessitates the rational design of efficient cathode electrocatalysts, guided by fundamental insights into reaction mechanisms and interfacial processes. We employ first-principles density functional theory (DFT) simulations to design and evaluate cathode electrocatalysts aimed at overcoming the sluggish kinetics of CO₂ reduction by unraveling the underlying interfacial mechanisms. Our investigation spans a broad spectrum of catalytic materials, including metal oxides, carbides, and two-dimensional layered MXenes, with a particular focus on how their electronic structures influence catalytic activity. We also explore the potential of atomically dispersed metal atoms—commonly referred to as single-atom catalysts (SACs)—in modulating CO₂ reduction chemistry. Through detailed analysis of reaction free energy profiles, we study the entire CO₂ reduction pathway and quantify charge and discharge overpotentials. To complement our atomistic insights, we integrate machine learning (ML) techniques to uncover the structure–activity relationships and inform the rational design of next-generation electrocatalysts for high-performance metal–CO₂ batteries.