Abstract
Deep learning emerges as a revolutionary tool in the rational design of perovskite photocatalysts. In this research, we explore how deep learning algorithms can analyze intricate patterns within perovskite material data. By doing so, we are able to break away from traditional design limitations, enabling the creation of a new generation of perovskite photocatalysts with enhanced catalytic performance. This represents a significant shift in the field of photocatalyst design.
