AI in single-atom catalysts: a review of design and applications

Abstract

Single-atom catalysts (SACs) have emerged as a research frontier in catalytic materials, distinguished by their unique atom-level dispersion, which significantly enhances catalytic activity, selectivity, and stability. SACs demonstrate substantial promise in electrocatalysis applications, such as fuel cells, CO₂ reduction, and hydrogen production, due to their ability to maximize utilization of active sites. However, the development of efficient and stable SACs involves intricate design and screening processes. In this work, artificial intelligence (AI), particularly machine learning (ML) and neural networks (NNs), offers powerful tools for accelerating the discovery and optimization of SACs. This review systematically discusses the application of AI technologies in SACs development through four key stages: (1) Density functional theory (DFT) and ab initio molecular dynamics (AIMD) simulations: DFT and AIMD are used to investigate catalytic mechanisms, with high-throughput applications significantly expanding accessible datasets; (2) Regression models: ML regression models identify key features that influence catalytic performance, streamlining the selection of promising materials; (3) NNs: NNs expedite the screening of known structural models, facilitating rapid assessment of catalytic potential; (4) Generative adversarial networks (GANs): GANs enable the prediction and design of novel high-performance catalysts tailored to specific requirements. This work provides a comprehensive overview of the current status of AI applications in SACs and offers insights and recommendations for future advancements in the field.

Keywords

Single-atom catalysts / AI / machine learning

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