High throughput computation and machine learning are new technologies to further accelerate the discovery of effective metal-free photocatalysts . For instance, Bai et al. developed a set of high-throughput techniques that integrate both experiment and computation, thus allowing for the investigation of a large number of potential polymer photocatalysts . By establishing the structure-property relationships of conjugated polyelectrolytes and using structural features that determine properties as descriptors in machine learning , Wan et al. have predicted structures beyond the database as photocatalysts for water splitting. Although high throughput DFT calculations and machine learning provide a highly efficient way to assist the discovery of potential polymers for photocatalysis , challenges lie in the considering of more properties beyond optical gaps in the screening of overall composition space .