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Liu et al. J Mater Inf 2024;4:33 Journal of
DOI: 10.20517/jmi.2024.48
Materials Informatics

Review Open Access

Transformative strategies in photocatalyst design:
merging computational methods and deep learning

1,#
1,#
4,*
1
2,#
3,*
Jianqiao Liu , Liqian Liang , Boru Su , Di Wu , Yuequ Zhang , Jianzhao Wu , Ce Fu 1,*
1
College of Information Science and Technology, Dalian Maritime University, Dalian 116026, Liaoning, China.
2
College of Environmental Science and Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China.
3
Department of Molecular Pharmacology, Groningen Research Institute of Pharmacy, University of Groningen, Groningen 9713
AV, The Netherlands.
4
College of Marine Equipment and Mechanical Engineering, Jimei University, Xiamen 361021, Fujian, China.
#
Authors contributed equally.
*
Correspondence to: Dr. Yuequ Zhang, Department of Molecular Pharmacology, Groningen Research Institute of Pharmacy,
University of Groningen, Antonius Deusinglaan 1, Groningen 9713 AV, The Netherlands. E-mail: y.zhang@rug.nl; Dr. Jianzhao
Wu, College of Marine Equipment and Mechanical Engineering, Jimei University, Yinjiang Road 185, Jimei District, Xiamen
361021, Fujian, China. E-mail: wujz@jmu.edu.cn; Prof. Ce Fu, College of Information Science and Technology, Dalian Maritime
University, Linghai Road 1, Ganjingzi District, Dalian 116026, Liaoning, China. E-mail: fuce_dlmu@sina.com
How to cite this article: Liu J, Liang L, Su B, Wu D, Zhang Y, Wu J, Fu C. Transformative strategies in photocatalyst design:
merging computational methods and deep learning. J Mater Inf 2024;4:33. https://dx.doi.org/10.20517/jmi.2024.48
Received: 23 Sep 2024 First Decision: 30 Oct 2024 Revised: 6 Dec 2024 Accepted: 10 Dec 2024 Published: 31 Dec 2024
Academic Editors: Fuquan Bai, Hao Li Copy Editor: Ting-Ting Hu Production Editor: Ting-Ting Hu

Abstract
Photocatalysis is a unique technology that harnesses solar energy through in-situ processes, operating without the
need for external energy inputs. It is integral to advancing environmental, energy, chemical, and carbon-neutral
objectives, promoting the dual goals of pollution control and carbon reduction. However, the conventional
approach to photocatalyst design faces challenges such as inefficiency, high costs, and low success rates,
highlighting the need for integrating modern technologies and seeking new paradigms. Here, we demonstrate a
comprehensive overview of transformative strategies in photocatalyst design, combining computational materials
science with deep learning technologies. The review covers the fundamental principles of photocatalyst design,
followed by a comprehensive examination of computational methods and the workflow for deep-learning-assisted
design. Deep learning approaches are extensively reviewed, focusing on the discovery of novel photocatalysts,
microstructure design, property optimization, novel design approaches, application exploration, and mechanistic
insights into photocatalysis. Finally, we highlight the synergy between multidimensional computation and deep
learning, while discussing the challenges and future directions in photocatalyst development. This review offers a
comprehensive summary of deep-learning-assisted photocatalyst design, offering transformative insights that not

© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0
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