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Shu et al. J. Mater. Inf. 2025, 5, 36 Journal of
DOI: 10.20517/jmi.2025.13
Materials Informatics

Review Open Access

Machine learning-enabled optoelectronic material
discovery: a comprehensive review

*
Yu Shu, Naihua Miao , Rize Li, Yucheng Lin, Siyu Han, Jian Zhou, Zhimei Sun *
School of Materials Science and Engineering, Beihang University, Beijing 100191, China.
* Correspondence to: Prof. Naihua Miao, Prof. Zhimei Sun, School of Materials Science and Engineering, Beihang University, No.
37 Xueyuan Road, Haidian District, Beijing 100191, China. E-mail: nhmiao@buaa.edu.cn; zmsun@buaa.edu.cn

How to cite this article: Shu, Y.; Miao, N.; Li, R.; Lin, Y.; Han, S.; Zhou, J.; Sun, Z. Machine learning-enabled optoelectronic
material discovery: a comprehensive review. J. Mater. Inf. 2025, 5, 36. https://dx.doi.org/10.20517/jmi.2025.13
Received: 14 Mar 2025 First Decision: 11 Apr 2025 Revised: 23 Apr 2025 Accepted: 12 May 2025 Published: 29 May 2025

Academic Editors: Baisheng Sa, Sergei Manzhos Copy Editor: Pei-Yun Wang Production Editor: Pei-Yun Wang

Abstract
The development of advanced optoelectronic materials constitutes a pivotal frontier in modern energy and
communication technologies, facilitating critical energy-photon-electron interconversion processes that underpin
sustainable energy infrastructures and high-performance electronic devices. However, the discovery and
optimization of novel optoelectronic materials face substantial hurdles arising from complicated structure-property
interdependencies, prohibitive development costs, and protracted innovation cycles. Conventional empirical
approaches and computational simulations usually exhibit limited efficacy in addressing the escalating demands for
materials with superior stability, economic viability, and customizable electronic properties. The integration of
machine learning (ML) with high-throughput screening has emerged as a transformative strategy to address these
challenges. By rapidly processing large multidimensional datasets and predicting critical material properties such
as electronic structure, thermodynamic stability, and charge transport behaviors, ML offers unprecedented
capabilities in the efficient and rational design of high-performance optoelectronic materials. This review provides a
comprehensive overview of cutting-edge ML-driven methodologies in efficient optoelectronic materials discovery
with emphasis on critical workflows, data integration strategies, and model frameworks. We also discuss the
challenges and prospects for ML applications, particularly in data standardization, model interpretability and
closed-loop experimental validation. We further propose the potential of artificial intelligence and autonomous
laboratories to build a powerful discovery pipeline to advance the development of high-performance optoelectronic
materials.

Keywords: Optoelectronic materials, machine learning, high-throughput calculation, materials design

© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0
International License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, sharing,
adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as
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