首页|From the perspective of experimental practice:High-throughput computational screening in photocatalysis

From the perspective of experimental practice:High-throughput computational screening in photocatalysis

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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 effi-ciency 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.Sub-sequently,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 pho-tocatalysts 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.

PhotocatalysisHigh-throughput computational screeningPhotocatalystTheoretical simulationsExperiments

Yunxuan Zhao、Junyu Gao、Xuanang Bian、Han Tang、Tierui Zhang

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Key Laboratory of Photochemical Conversion and Optoelectronic Materials,Technical Institute of Physics and Chemistry,Chinese Academy of Sciences,Beijing,100190,China

Center of Materials Science and Optoelectronics Engineering,University of Chinese Academy of Sciences,Beijing,100049,China

Institute for AI Industry Research(AIR),Tsinghua University,Beijing,100084,China

National Key Projects for Fundamental Research and Development of China国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金国家自然科学基金DNL Cooperation Fund,CASCAS Project for Young Scientists in Basic Research

2021YFA150080351825205521201050022210220222088102U22A20391DNL202016YSBR-004

2024

10.1016/j.gee.2023.05.008

绿色能源与环境(英文)

绿色能源与环境(英文)

CSTPCD
ISSN：
年,卷(期)：2024.9(1)
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