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Page 2 of 31 Shu et al. J. Mater. Inf. 2025, 5, 36 https://dx.doi.org/10.20517/jmi.2025.13

INTRODUCTION
With the advancement of technology, optoelectronic materials have become increasingly important across
various fields due to their unique and efficient energy-photon-electron conversion properties. These
materials are fundamental to innovations in energy conversion, display technologies, environmental
remediation, and information processing. Their diverse applications drive the development of efficient, eco-
[1-4]
friendly, and intelligent systems . As shown in Figure 1, optoelectronic materials can efficiently
interconvert energy, photons, and electrons, playing a crucial role in multiple domains. First, these materials
[4,5]
have extensive applications in solar cells, facilitating solar energy conversion into clean electricity . For
example, perovskite solar cells achieve high optoelectronic conversion efficiency and offer low cost and
[6,7]
simple fabrication advantages, revealing tremendous commercial potential . Additionally, optoelectronic
materials are equally crucial in light-emitting devices, such as light emitting diodes (LEDs) and organic light
emitting diodes (OLEDs) . These devices provide essential support for modern display and lighting
[8,9]
technologies by efficiently converting photon-electron interconversion . In photocatalysis, optoelectronic
[10]
materials drive redox reactions by absorbing light energy, enabling applications in environmental
purification and clean energy synthesis . For instance, photocatalytic materials can decompose water and
[11]
remove organic pollutants under light irradiation, thus offering novel solutions for environmental
remediation and energy storage [12,13] . Moreover, photodetectors represent another critical application of
optoelectronic materials. With high sensitivity to light signals, they play an indispensable role in
communications and medical imaging . By swiftly responding to light signals and converting them into
[14]
electrical signals, photodetectors provide robust technological support for information transmission and
processing [15,16] . Therefore, optoelectronic materials exhibit vast application potential in clean energy
conversion, display technologies, environmental purification, and information technology. Their diverse
functionalities make them indispensable materials in advancing modern technology, meeting the demand
for efficient, green, and intelligent solutions across a broad range of fields.

In enhancing the performance of optoelectronic materials, precise control over their electronic structures
and achieving a balance between stability and cost-effectiveness are crucial. However, optoelectronic devices
commonly face multifaceted challenges, including structural and environmental instability, inefficiencies in
interfacial engineering, and performance bottleneck [17-20] . Addressing these multifaceted challenges demands
advanced materials design, precise interface engineering, and integrated ML-driven workflows to accelerate
the identification, optimization, and experimental validation of next-generation optoelectronic
materials [21,22] . The evolution of scientific research paradigms has progressed from empirical science and
theoretical science to computational science and data-driven science, as shown in Figure 2A. Traditional
methods for discovering new materials, such as empirical, theoretical, and density functional theory (DFT)-
based methods, have long held a significant position in materials science [23,24] . However, these methods have
limitations, including long development cycles, low efficiency, and high costs, making it challenging to meet
the demands of modern, rapidly advancing material science [25,26] . With significant advances in
computational power and numerical algorithms, data-driven science has emerged as a new research
paradigm for rationalizing novel optoelectronic materials . This paradigm emphasizes discovering
[27]
scientific laws by analyzing large datasets and fully utilizing technologies such as machine learning (ML) to
process complex data sets. Unlike traditional methods, data-driven science integrates knowledge from
multiple disciplines, including computer science, statistics, mathematics, and engineering, and is an
important branch of artificial intelligence (AI) [28-30] . Through ML techniques, computer algorithms can
automatically improve and adapt based on experience, enabling computers to make decisions and learn. ML
can leverage vast amounts of data and complex algorithms to accurately predict the properties and
behaviors of materials [31-33] . Recent studies highlight how the integration of ML and AI is transforming
materials discovery by enabling faster, more efficient, and autonomous prediction, design, and synthesis of