Artificial Intelligence Guided Single Atom Catalysts: From Precise Design to Industrial‐Grade Preparation

Abstract

Abstract Single‐atom catalysts (SACs) have transformed heterogeneous catalysis via atomic‐level active site control, but bridging atomic‐scale design with structurally consistent, scalable manufacturing remains challenging. This review documents artificial intelligence (AI)‐driven advances in SACs research, covering rational design, dynamic characterization, and scalable synthesis. It highlights integrating physics‐based simulations with machine learning (ML) models. ML leverages coordination‐sensitive descriptors to predict catalytic performance and guide inverse design. Emerging techniques such as graph neural network (GNN) enhanced operando X‐ray absorption spectroscopy (XAS) are featured, decoding real‐time atomic coordination dynamics. A closed‐loop “design‐validate‐manufacture” framework is discussed, integrating digital twins with autonomous robotic synthesis to ensure structural fidelity from models to industrial production and minimize coordination distortion during scaling. Addressing challenges like small data learning and industrial structural inconsistencies, the review proposes a unified framework for atomic precision manufacturing of SACs, outlining an intelligent roadmap for next‐generation energy and environmental applications.