Machine learning-driven design of single-atom catalysts for carbon dioxide valorization to high-value chemicals: a review of photocatalysis, electrocatalysis, and thermocatalysis

Abstract

The pressing need for carbon-neutral technologies has driven extensive research into photocatalytic, electrocatalytic, and thermocatalytic CO₂ reduction, with highly efficient single-atom catalysts (SACs) due to their atomically dispersed active sites, tunable coordination environments, and well-defined electronic structures. Recent advances in SACs have demonstrated enhanced activity, selectivity and stability through rational design strategies incorporating transition-metal-based single-atom sites, nitrogen-coordinated frameworks, and perovskite-, graphene-, or MOF-supports. Mechanistically, SACs facilitate CO₂ activation via optimized CO₂ adsorption, electronic-state modulation and selective stabilization of key intermediates, thus promoting tailored product formation. Despite significant progress, challenges remain in understanding the precise electronic effects governing intermediate binding and selectivity and suppressing metal aggregation under operando conditions. This review systematically integrates experimental findings with machine learning (ML)-assisted first-principles calculations, deep learning (DL) frameworks, and density functional theory (DFT) modeling to refine the performances of SACs. ML-driven Bayesian optimization accelerates catalyst discovery by correlating the synthesis parameters with reaction kinetics and thermodynamics. High-throughput experimental validation combined with multi-technique characterization elucidates the structure–activity relationships, providing insights into the electron transfer dynamics, coordination tuning, and catalytic site evolution. The integration of active learning algorithms enables self-optimizing SACs, dynamically adjusting synthesis and reaction conditions for superior selectivity and faradaic efficiency. By bridging predictive modeling with experimental validation, this review presents a comprehensive framework for the rational design of next-generation SACs, paving the way for high-efficiency conversion of CO₂ into valuable chemicals. The synergy between AI-driven catalyst discovery and mechanistic elucidation represents a paradigm shift toward viable and selective CO₂ valorization strategies.