Machine learning-guided two-dimensional single-atom catalysts for electrochemical CO₂ utilisation

The electrochemical CO₂ reduction reaction (CO₂RR) offers a promising route to convert greenhouse gas emissions into value-added fuels and chemicals, yet achieving high product selectivity while suppressing the competing hydrogen evolution reaction (HER) remains a central challenge. Two-dimensional (2D) material-supported single-atom catalysts (SACs) address this challenge through atomically defined coordination environments that enable precise tuning of the critical *COOH and *CO adsorption energies, which strongly influence CO₂ activation and product selectivity, while the competing adsorption of *H governs HER suppression. This review examines the convergence of 2D-SAC design and machine learning (ML) for CO₂RR catalyst discovery. We first outline the mechanistic foundations of the CO₂RR together with comparative insights from the HER and OER and then discuss the structural and electronic principles governing single-atom stabilisation on nitrogen-rich carbon, transition metal dichalcogenide, and MXene supports. Recent catalytic advances are assessed with the CO₂RR as the primary focus, including proximity-engineered systems that promote C-C coupling and multicarbon product formation. Finally, emerging ML frameworks are critically reviewed, covering electronic, geometric, and spectroscopic descriptor design, data source strategies, and high-throughput screening and inverse-design workflows. Operando ML integration has revealed the dynamic evolution of active sites under reaction conditions, highlighting the need to incorporate electrochemical-environment and reaction-condition-dependent structural information into future training datasets. Key priorities identified for the field are large-scale CO₂RR-specific SAC database construction, product- and condition-specific descriptor development, and closure of the prediction-synthesis-validation feedback loop.