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Table 2. International efforts in deep learning-based photocatalyst design
Researcher Institution Country/Region
Yang Liu, Zhenhui Kang Soochow University China P.R.
Yong-Bin Zhuang, Jun Cheng Xiamen University China P.R.
Yunjin Yu, Yadong Wei Shenzhen University China P.R.
Ting Ren, Xin Li Institute of Electrical Engineering, CAS China P.R.
Jianmei Yuan, Yuliang Mao Xiangtan University China P.R.
Hui-Ming Cheng, Gang Liu Institute of Metal Research, CAS China P.R.
Jinlan Wang Southeast University China P.R.
David A. Winkler, Rachel A. Caruso Royal Melbourne Institute of Technology Australia
Geoffrey A. Ozin University of Toronto Canada
Xiaoying Zhuang, Alexander Shapeev Leibniz University Hannover Germany
Andrew I. Cooper University of Liverpool Great Britain
Zhenhua Pan, Kenji Katayama Chuo University Japan
Alicja Mikolajczyk Fahrenheit Universities Poland
Ramazan Yildirim Bogazici University Türkiye
Xiong Yu University System of Ohio United States

renewable energy generation and environmental sustainability. This categorization illustrates how deep
learning fosters innovation across fundamental research and practical applications in photocatalysis.

High-impact journals have been instrumental in shaping the field, with several leading publications
contributing significantly to the dissemination of cutting-edge research. As depicted in Figure 2C, Applied
Catalysis B-Environment and Energy has emerged as a key platform. Chemical Reviews and ACS Applied
Materials and Interfaces have also made substantial contributions, reinforcing the importance of this
interdisciplinary approach. Additionally, journals such as Advanced Energy Materials and Journal of the
American Chemical Society have been central to promoting advances in deep-learning-enhanced
photocatalyst design, further solidifying the role of this technology in the future of materials discovery and
development.

The photocatalyst design has seen substantial progress in materials, applications, mechanistic
understanding, algorithms, and computility, as shown in Figure 3. Early photocatalysts, such as TiO and
2
ZnO, have evolved into more advanced systems, including nanomaterials, heterojunctions, quantum dots,
and 1D/2D structures, greatly expanding their functionality. Initial applications focused on environmental
remediation, specifically organic pollutant degradation and heavy metal reduction, but have since
diversified to include CO reduction, hydrogen production, sterilization, and nitrogen fixation, reflecting
2
the growing versatility of photocatalysis in addressing global issues. Mechanistic insights have deepened,
allowing precise control over electron-hole pair dynamics, band structures, and surface reactions, which has
led to significant improvements in stability and efficiency. Concurrently, advancements in deep learning
algorithms have enabled more accurate predictions of photocatalytic behavior, accelerating the discovery
and optimization of photocatalytic systems. Additionally, enhanced computility, driven by breakthroughs in
GPUs and parallel computing, has allowed researchers to simulate complex processes and manage large
datasets, pushing the boundaries of what can be achieved in photocatalyst design. The interplay between
deep learning and photocatalyst development is driving rapid innovation, where new machine learning
models refine material discovery and mechanistic insights, while advances in photocatalysis provide rich
data that fuel further algorithmic evolution. This dynamic feedback is accelerating progress toward the next
generation of high-performance photocatalysts, with far-reaching implications for environmental and