From the perspective of experimental practice: High-throughput computational screening in photocatalysis
Yunxuan Zhao, Junyu Gao, Xuanang Bian, Han Tang, Tierui Zhang
Abstract
Photocatalysis, a critical strategy for harvesting sunlight to address energy demand and environmental concerns, is underpinned by the discovery of high-performance photocatalysts, thereby how to design photocatalysts is now generating widespread interest in boosting the conversion efficiency of solar energy. In the past decade, computational technologies and theoretical simulations have led to a major leap in the development of high-throughput computational screening strategies for novel high-efficiency photocatalysts. In this viewpoint, we started with introducing the challenges of photocatalysis from the view of experimental practice, especially the inefficiency of the traditional “trial and error” method. Subsequently, a cross-sectional comparison between experimental and high-throughput computational screening for photocatalysis is presented and discussed in detail. On the basis of the current experimental progress in photocatalysis, we also exemplified the various challenges associated with high-throughput computational screening strategies. Finally, we offered a preferred high-throughput computational screening procedure for photocatalysts from an experimental practice perspective (model construction and screening, standardized experiments, assessment and revision), with the aim of a better correlation of high-throughput simulations and experimental practices, motivating to search for better descriptors.