Rational Design of Perovskite Photocatalysts with the Aid of Deep Learning for Organic Transformations

This work demonstrates the use of deep learning to assist in the rational design of perovskite photocatalysts for organic transformations. We combine deep learning techniques with experimental and computational data on perovskite materials. The deep - learning - assisted design approach allows for a more systematic exploration of the perovskite structure - activity space, leading to the discovery of novel photocatalyst designs that can drive organic reactions more efficiently.