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combine different computational methods, multiscale simulations that link phenomena across different
levels of resolution, and multimodal analyses that incorporate diverse data types to provide a more holistic
understanding of intricate processes. Multimodal and multiscale computational models have been
[155]
successfully applied in areas such as estimating permeate flux in dynamic membranes and predicting
wettability evolution on laser-textured surfaces . These methods have also advanced the understanding of
[156]
[157]
surface-enhanced spectroscopy and photocatalysis [158,159] , providing detailed insights into reaction
mechanisms and kinetics. In photocatalyst design, such approaches hold the potential to optimize structures
and performance by integrating atomic-scale simulations with larger environmental models, enabling more
efficient and scalable solutions for real-world applications.

Loh et al. explored the integration of multiscale physics and machine learning to tackle the challenges of
[160]
photocatalyst-photoreactor synergy in solar fuel production [Figure 13]. By leveraging physics-informed
neural networks (PINN), this approach captured complex, multiscale phenomena such as charge-carrier
dynamics and solar energy collection, offering a deeper understanding of material-photoreactor
interactions. The study exemplified how combining multidimensional computation with deep learning
could optimize reactor performance and drive advancements in scalable solar fuel technology.
de Oliveira et al. combined CFD, photon fate simulations, and machine learning to optimize continuous-
[161]
flow photocatalytic systems . By integrating these multidimensional computational techniques, the study
reduced computational costs while maintaining high accuracy in predicting photon distribution and
reaction rates. This framework highlighted the potential of combining CFD and machine learning to
streamline the design and scaling of advanced photocatalytic reactors. The combination of
multidimensional computational models and deep learning has demonstrated significant potential in
optimizing photocatalytic systems, improving both predictive accuracy and computational efficiency.
Looking ahead, this integration holds immense promise for accelerating the development of advanced
photocatalysts and reactors, driving transformative progress in cutting-edge technological innovations and
global challenges.

CHALLENGES AND FUTURE PROSPECTS
The integration of deep learning with photocatalyst design has driven innovations across multiple
dimensions, including the discovery of novel photocatalysts, the optimization of microstructures and
properties, and the exploration of advanced approaches, applications, and mechanistic insights. The synergy
between deep learning and multidimensional computational models has facilitated more precise
predictions, cross-scale analyses, and enhanced reactor designs. These combined methodologies have
broadened the applications of photocatalysis, accelerating breakthroughs in environmental remediation,
energy conversion, and performance optimization. Despite these advancements, several critical challenges
remain to be addressed to fully unlock the potential of deep-learning-assisted photocatalyst design.

Dataset availability and quality
One of the fundamental challenges in deep-learning-assisted photocatalyst design is the acquisition of high-
quality, comprehensive datasets. Photocatalytic reactions involve complex interactions between factors such
as composition, structure, environmental conditions, and experimental setups, making it difficult to
compile standardized data across different studies. Existing datasets are often fragmented, with
inconsistencies in reporting, missing information, and limited experimental reproducibility. The scarcity of
large, labeled datasets also limits the training of robust deep learning models, reducing their ability to
generalize across diverse photocatalytic systems. Addressing these challenges requires the aggregation of
data from multiple sources and the development of uniform data collection protocols to ensure data quality
and consistency.