Abstract
This study delves into the untapped potential of deep learning in the rational design of perovskite photocatalysts for organic reactions. By integrating data - driven algorithms with material science knowledge, a novel framework is developed. It analyzes vast amounts of perovskite structural and catalytic data, enabling the identification of key factors influencing catalytic performance. This approach not only accelerates the design process but also paves the way for the discovery of highly efficient perovskite photocatalysts for various organic transformations.
