Harnessing Deep Learning for the Rational Design of Perovskite Photocatalysts in Organic Synthesis

This paper focuses on harnessing the capabilities of deep learning for the rational design of perovskite photocatalysts in organic synthesis. By constructing a comprehensive dataset of perovskite materials and their synthesis - relevant catalytic properties, we train deep learning models to understand the complex relationships between structure and function. The resulting models can predict the performance of new perovskite photocatalysts, facilitating the development of optimized catalysts for a wide range of organic synthesis reactions.