Data-Driven Synthesis Prediction of Single-Atom Catalysts for Decarbonization and Pollution Abatement Using a Transformer-Based Framework

The practical deployment of single-atom catalysts (SACs) still faces significant challenges due to the complex, non-linear relationship between bulk synthesis parameters and the resulting atomic-scale active sites, which often requires extensive empirical optimization. We develop SACFormer, a multi-task deep learning framework, to predict the active-site configurations of graphitic carbon nitride (g-C 3 N 4 )-supported SACs directly from reactant SMILES and synthesis conditions. Trained on a manually curated dataset of 191 synthesis entries and enhanced by SMILES enumeration, SACFormer achieves a joint prediction accuracy of 87.64% for the metal center, coordinating element, and coordination number. Furthermore, we perform model uncertainty quantification to investigate the underlying mechanisms. Our analysis identifies a temperature window (450–600 °C) that favors the formation of active metastable sites, providing a strategic “transition area” regime that can inform catalyst engineering for pollution abatement (e.g., advanced oxidation processes). Conversely, our analysis indicates that, for decarbonization applications (e.g., CO 2 reduction) that require specific selectivity, high-temperature annealing (>650 °C) or macrocyclic precursors are predicted to favor thermodynamic stability and stabilize the metal centers by retaining the coordination environment of the precursor. These results offer data-driven synthesis guidelines and predictive phase diagrams, highlighting how forward prediction frameworks can clarify synthesis–structure relationships.