Machine learning-enabled optoelectronic material discovery: a comprehensive review

作者：Yu Shu, Naihua Miao, R. J. Li, Yucheng Lin, Siyu Han, Jian Zhou, Zhimei Sun · 发表于：Journal of Materials Informatics · 年份：2025 · DOI：10.20517/jmi.2025.13 · 被引用次数：22 · 研究领域：Machine Learning in Materials Science、Computational Drug Discovery Methods、Chalcogenide Semiconductor Thin Films

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, particularl...