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Shu et al. J. Mater. Inf. 2025, 5, 36 https://dx.doi.org/10.20517/jmi.2025.13 Page 25 of 31

GNN-based model trained on data from the MP, which predicted over 380,000 new stable materials,
[152]
significantly expanding the known crystalline materials landscape . Furthermore, autonomous A-Lab of
Lawrence Berkeley National Laboratory synthesized 41 out of 58 AI-predicted materials within 17 days,
[153]
demonstrating the potential of combining AI-driven predictions with automated synthesis . Notably,
generative models such as MatterGen have emerged as powerful tools in materials design. MatterGen
employs a diffusion-based generative process to create stable and diverse inorganic crystals across the
periodic table. It can be fine-tuned to target specific property constraints, enabling the generation of
[154]
materials with desired chemistry, symmetry, and electronic properties . These developments underscore
the transformative impact of ML in accelerating the discovery and optimization of optoelectronic materials.
The convergence of AI with autonomous laboratory platforms, capable of performing automated material
synthesis and characterization, will enable the rapid development of materials with superior efficiency,
stability, and sustainability [155-157] . This integrated approach will accelerate the design of next-generation
optoelectronic devices, helping to meet the growing demand for energy-efficient technologies. Advances in
dataset standardization, model interpretability, and AI frameworks will play a pivotal role in shaping the
future of materials innovation.

In summary, ML transforms optoelectronic material design by offering rapid, high-accuracy predictions
across vast chemical spaces. Continued advancements in dataset standardization, model interpretability,
property-specific tuning, and hybrid ML-physics frameworks will foster a new generation of optoelectronic
materials optimized for efficiency, stability, and sustainability. The integration of ML with experimental and
AI holds promise for accelerating material innovation, ultimately contributing to developing next-
generation optoelectronic devices that meet the increasing demands for energy-efficient technologies.

DECLARATIONS
Authors’ contributions
Data analysis, interpretation and manuscript draft: Shu, Y.
Performed data acquisition and collected references: Li, R.; Lin, Y.; Han, S.; Zhou, J.
Provided revision, acquired funding and supervision: Miao, N.; Sun, Z.

Availability of data and materials
Not applicable.

Financial support and sponsorship
This work was supported by the National Natural Science Foundation of China (52222101).

Conflicts of interest
All authors declared that there are no conflicts of interest.

Ethical approval and consent to participate
Not applicable.

Consent for publication
Not applicable.

Copyright
© The Author(s) 2025.