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Scalability and computing resources
The increasing complexity and size of deep learning models, especially large-scale models, used in
photocatalyst design require significant computational resources, presenting a challenge in managing vast
datasets and running high-dimensional simulations. As large models such as transformers and foundation
models become more prevalent, the demand for scalable computing infrastructures, such as high-
performance computing clusters and cloud-based systems, has become essential to handle the intense
computational workloads. Hardware advancements, particularly in the development of GPUs and TPUs, are
driving improvements in training speed and model efficiency. Moreover, the development of more efficient
architectures, along with advancements in large model fine-tuning, is reducing computational cost while
maintaining high predictive accuracy. Techniques such as model pruning, quantization, and efficient
distributed computing frameworks enable parallelization, allowing the processing of massive datasets across
multiple machines. These large models open the door for unprecedented accuracy in predictions, but the
hardware and algorithmic advancements will be critical to overcoming the computational bottlenecks
associated with their scale. The integration of these advancements is key for making large-scale
photocatalyst design feasible and scalable.

Reversal design of photocatalysts
Reversal design is a methodology that works backward from a desired property or performance target to
identify the optimal structures or configurations, offering a powerful approach in materials science. In
photocatalyst design, reversal design enables the identification of systems with optimized performance by
working backward from specific targets, such as light absorption or photocatalytic efficiency, to tailor
structural or compositional features. This methodology accelerates the discovery process by narrowing the
search space to systems that meet predefined performance metrics. Deep learning enhances reversal design
by using predictive models that efficiently map the relationship between structures and properties, allowing
rapid iteration toward optimal solutions. It can generate new candidates through generative models and
apply reinforcement learning to refine designs based on experimental feedback. With its ability to explore
vast design spaces, deep learning-driven reversal design is transforming the way photocatalysts are
discovered and developed.

Multiscale modeling and computation
Photocatalytic reactions inherently span multiple scales, from electron-level interactions to macroscopic
phenomena, posing significant challenges for comprehensive modeling. The integration of models that
describe different scales - such as atomic-scale electron dynamics, charge carrier transport, and bulk-phase
mass transfer - into a unified multiscale framework remains a critical hurdle in photocatalyst design.
Multiscale deep learning models provide an opportunity to bridge these gaps, enabling more accurate
performance predictions by incorporating insights from various scales. This requires combining quantum
simulations, molecular dynamics, and continuum models, which presents not only computational demands
but also algorithmic complexity. Successful advances in multiscale modeling could yield deeper insights into
how atomic and molecular processes influence macroscopic behavior, driving the development of more
efficient photocatalysts. Additionally, ensuring the interpretability and practical applicability of these
models is essential for their experimental validation. Addressing these challenges is a pivotal goal in
advancing both photocatalyst design and materials informatics.

Future directions amidst challenges
Despite their substantial advantages in reducing experimental costs and time, deep learning and other
computational methodologies present several inherent challenges. A primary challenge is the need for
further enhancement of computationally designed photocatalysts. While integrated computational
approaches can identify promising candidates, these photocatalysts frequently require additional