Deep Learning-Enabled Rational Design of Perovskite-Based Photocatalysts for Efficient Organic Transformations

This paper presents a novel deep learning (DL)-driven approach for rational design of perovskite-based photocatalysts to achieve efficient organic transformations. By integrating first-principles calculations and advanced DL models, we establish a framework that interprets the structure-activity relationships of single-crystal perovskite catalysts. This enables accelerated discovery of optimal catalyst structures for specific reactions. Our method demonstrates superior performance in predicting catalytic activities compared to traditional approaches, providing a powerful tool for the development of next-generation photocatalysts.