Abstract
Deep learning enables the rational design of high - performance perovskite photocatalysts for organic reactions. We build deep learning models that can accurately predict the performance of perovskite photocatalysts based on their structural and compositional features. Through iterative model training and optimization, we are able to design perovskite materials with enhanced catalytic efficiency, providing a powerful solution for the challenges in organic reaction catalysis
