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Page 18 of 33 Liu et al. J Mater Inf 2024;4:33 https://dx.doi.org/10.20517/jmi.2024.48

experimental conditions. These features were then reduced to 20 through an initial filtering step, focusing
on eliminating redundancies. Subsequently, advanced selection techniques such as Max Relevance Min
Redundancy (mRMR) and embedded feature selection were applied, ultimately narrowing the set to 7-9 key
features that significantly enhanced model accuracy while minimizing overfitting. The BPANN model
achieved the highest prediction accuracy for hydrogen production, while GBR showed the best performance
for bandgap prediction. Ultimately, 14 promising perovskite photocatalysts were identified from 30,000
candidates, and two online web servers were developed to share the prediction models, allowing public
access for future research. Huang et al. applied machine learning techniques to address the complex task of
selecting optimal cocatalysts for BiVO photoanodes in photoelectrochemical systems, as exhibited in
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[127]
Figure 7B . The approach involved training multi-layer perceptron neural networks and tree-based
ensemble models to predict high-performance cocatalyst-photoanode combinations. The random forest
model achieved a classification accuracy of 96.30%, with cocatalyst type and preparation method identified
as the most influential factors affecting photocatalytic performance. Additionally, the study utilized SHAP
to derive heuristic rules for guiding the selection of promising cocatalyst/photoanode systems.

There are also some successful cases in novel photocatalyst design accelerated by deep learning. Machine
learning has been applied to the design of TiO -coated glass for improved NOx removal efficiency .
[128]
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[129]
[130]
Additionally, core-shell Au-silica nanoparticles , lead-free Bi-based perovskites , and g-C N -based
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single-atom photocatalysts have been identified through deep-learning approaches. Choudhary et al.
[131]
developed the InterMat framework, which combines DFT and GNN to predict band offsets in
[132]
semiconductor interfaces, enabling the rapid screening of over 1.4 trillion potential interfaces . This large-
scale model, which is publicly available, offers an efficient tool for discovering novel photocatalysts. The
application of deep learning in the discovery of novel photocatalysts has significantly accelerated the
identification and design of high-performance photocatalytic systems, demonstrating its transformative
impact across various applications.
Microstructure design of photocatalysts
The microstructure of photocatalysts is critical in determining their efficiency, as it directly affects light
absorption, charge separation, and surface activity. Microstructures such as heterojunctions provide
significant advantages by promoting more efficient charge separation and transfer at interfaces, which in
turn enhances overall photocatalytic performance. Guevarra et al. employed a deep learning-based materials
[133]
structure-property factorization (MSPF) to accelerate the design of photocatalyst structures [Figure 8].
This approach utilized Deep Reasoning Networks (DRNets) for phase mapping and matrix factorization for
modeling key properties, revealing synergistic interactions between BiVO -like and Cu-based phases that
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significantly enhanced photocatalytic performance. The study highlights the role of deep learning in
advancing structural innovation and optimizing complex photocatalytic systems.

Building on recent advances, further efforts have utilized deep learning models to explore complex
structural modifications. Jiang et al. developed an interpretable CNN to predict active sites involved in TiO 2
[134]
-mediated photocatalytic degradation of organic contaminants . The model employed EfficientNet to
extract critical structural features and utilized Grad-CAM to highlight molecular regions most influential in
determining degradation rates. Similarly, Zhou et al. applied graph CNNs (GCNNs) to establish a structure-
property relationship for nanoporous cobalt zirconate, predicting ammonia yield from photocatalytic
nitrogen fixation based on structural parameters such as pore volume and surface area . Therefore, the
[135]
integration of deep learning in microstructural design has significantly advanced the optimization of
photocatalyst structures, leading to enhanced performance across various systems, including
heterostructures and perovskite oxides.