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Liu et al. J Mater Inf 2024;4:33 https://dx.doi.org/10.20517/jmi.2024.48 Page 27 of 33

modifications - such as surface engineering, doping, or structural optimization - to achieve optimal
[162]
performance . Addressing these limitations often involves iterative cycles of computational predictions
[163]
followed by experimental validation to refine photocatalyst properties . Another critical challenge
involves scaling these technologies from laboratory settings to industrial applications. Photocatalysts that
demonstrate exceptional performance on a small scale often encounter significant obstacles in maintaining
efficiency and stability during scale-up. Overcoming these scale-up challenges requires advancements in
reactor design, synthesis methodologies, and engineering practices to ensure economically feasible
production processes. Moreover, the successful commercialization of photocatalyst technologies
necessitates a multidisciplinary approach. Issues such as stability, selectivity, and overall efficiency cannot be
fully addressed within a single discipline. Effective commercialization requires collaboration among
chemists, materials scientists, chemical engineers, and industry professionals to bridge the gap between
laboratory research and market-ready technologies, ultimately facilitating the transition to commercially
viable photocatalytic solutions. In conclusion, although challenges remain, rapid progress in computational
tools, coupled with targeted experimental efforts and interdisciplinary collaboration, suggests a promising
future for photocatalyst design and application. With ongoing innovation, these technologies hold
substantial potential for contributing to sustainable energy and environmental applications.

CONCLUSIONS
In conclusion, the integration of deep learning with photocatalyst design has transformed the field,
unlocking new strategies for optimizing photocatalytic systems. This review highlights how computational
methods combined with deep learning have significantly advanced the discovery of novel photocatalysts,
refined microstructure design, optimized functional properties, and provided deeper mechanistic insights,
driving innovations in environmental remediation, energy conversion, and chemical processes.
Furthermore, the synergy between multidimensional computational models and deep learning has enabled
more precise predictions, efficient experimental validation, and greater scalability in photocatalytic
applications. Despite these advancements, challenges remain in areas such as dataset quality, model
interpretability, and the seamless integration of multiscale modeling. Addressing these challenges will be
crucial for further breakthroughs, guiding the future design of high-performance photocatalysts and
fostering the development of sustainable technologies.

DECLARATIONS
Authors’ contributions
Conceptualization: Liu J, Su B, Liang L
Methodology: Liu J, Su B, Liang L
Software: Wu D
Writing - original draft: Liu J, Su B, Liang L
Writing - review and editing: Zhang Y, Wu J, Fu C
Supervision and funding acquisition: Fu C

Availability of data and materials
Not applicable.

Financial support and sponsorship
This work is supported by the Liaoning Applied Fundamental Research Project (Grant Nos. 2022JH2/
101300158 and 2023JH2/101300014) and the Fundamental Research Funds for the Central Universities
(Grant Nos. 3132024243 and 3132023506).