This chapter surveys how machine learning is transforming perovskite research by addressing challenges in synthesis, characterization, and device optimization. It contributes clear workflows and case studies demonstrating structure–property mapping with reduced cost, enabling high-throughput screening, multi-scale prediction, composition–process optimization, and automated fabrication. It also highlights key challenges—data scarcity, heterogeneity, and interpretability—and outlines emerging opportunities in intelligent automation for next-generation optoelectronics.
Abstract: This chapter presents a comprehensive overview of how machine learning methods transform current perovskite research by addressing the inherent challenges of perovskite synthesis, characterization, and device optimization. Through detailed discussion of general workflows and typical case studies, the chapter demonstrates how machine learning methods achieve direct mapping of structure–property relationships over complex scenarios, with significant experimental and computational cost reduction, therefore enabling important research capabilities such as high-throughput screening, multi-scale property prediction, composition-process optimization, and automated fabrication. However, the use of machine learning in various perovskite research cases do share certain challenges such as data scarcity, heterogeneity, and model interpretability issues, that require prudent exercise. Lastly, the chapter highlights emerging opportunities for intelligent automation and next-generation optoelectronic development.
This work is published in the book: Semiconductors and Semimetals.