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Page 2 of 33 Liu et al. J Mater Inf 2024;4:33 https://dx.doi.org/10.20517/jmi.2024.48

only enhance the development of photocatalytic technologies but also expand the practical applications of
photocatalysis in various domains.

Keywords: Photocatalysis, materials design, computational method, deep learning, transformative strategy

INTRODUCTION
Photocatalysis offers a unique advantage by directly harnessing solar energy through in-situ processes ,
[1]
[2]
making it a highly sustainable technology that operates without external energy inputs . By utilizing
abundant and renewable sunlight, photocatalytic reactions significantly reduce reliance on fossil fuels and
minimize greenhouse gas emissions . One of the major environmental benefits of this technology is its
[3]
ability to degrade a wide range of pollutants, such as organic compounds and heavy metals , thus
[6]
[4,5]
promoting cleaner ecosystems. In the energy domain, it plays a pivotal role in solar fuel generation,
particularly through hydrogen production via water splitting, offering a clean and renewable energy
[7-9]
source . Additionally, it enhances chemical processes by enabling selective reactions with greater
efficiency, reducing the need for hazardous chemicals and lowering operational costs . With the global
[10]
focus on carbon neutrality, photocatalysis contributes to carbon reduction efforts by converting CO into
2
valuable fuels and chemicals, thus advancing the circular carbon economy . The key to unlocking the full
[11]
potential of this technology lies in the design of advanced photocatalysts, which optimize light absorption,
charge separation, and photocatalytic efficiency.

Traditional approaches to photocatalyst design are fraught with inefficiencies, high costs, and low success
rates, which significantly hinder progress in this field . These methods often depend on laborious trial-
[12]
and-error experimentation [13-15] , consuming substantial time and resources while offering limited predictive
power for real-world applications. Key photocatalytic properties, such as charge separation efficiency and
[16]
reaction kinetics [17,18] , remain difficult to optimize, resulting in suboptimal performance and limited
advancement. The high costs associated with synthesizing and characterizing photocatalysts further
compound these issues, limiting scalability and delaying the transition from laboratory research to
industrial implementation . Surface reactivity, adsorption dynamics, and charge transfer mechanisms [20,21]
[19]
are not well understood through conventional methods, hindering systematic improvements in reactivity
and stability. Consequently, many photocatalysts designed through traditional approaches fail to achieve the
required durability, selectivity, and efficiency needed for practical applications in energy conversion and
environmental remediation. To overcome these challenges, there is an urgent need to adopt advanced
methodologies, such as computational modeling and deep learning, to develop new paradigms that can
accelerate the discovery process and enhance the overall performance of photocatalytic systems.

The rise of computational science and deep learning has revolutionized materials design by offering
predictive power, accelerated discovery, and improved optimization capabilities . Advanced
[22]
computational techniques, such as density functional theory (DFT) [23,24] and molecular dynamics , offer
[25]
precise insights into electronic structures and reaction mechanisms that are challenging to capture through
experimental methods alone. High-throughput screening (HTS) enables the evaluation of vast libraries of
potential photocatalysts, facilitating the identification of promising candidates based on theoretical
performance metrics before engaging in resource-intensive experimental validation . Deep learning
[26]
algorithms , trained on extensive datasets of material properties and photocatalytic performance, reveal
[27]
intricate correlations between structural characteristics and photocatalytic activity, providing a data-driven
approach to more effective photocatalyst design. It further enables the prediction of photocatalyst behavior
under diverse environmental conditions, thereby advancing the development of durable and high-