Machine learning for perovskite solar cells: a comprehensive review on opportunities and challenges for materials scientists

Abstract

Perovskite solar cells (PSCs) have emerged as a promising technology due to their tunable optoelectronic properties, low-cost fabrication and high efficiency. Despite this progress, key challenges such as long-term stability, large-scale manufacturability, and recyclability remain unsolved. Moreover, traditional methods for discovering new materials and optimizing device architectures rely on trial-and-error experiments. Machine learning (ML) offers powerful tools to address these bottlenecks by uncovering hidden patterns in data, accelerating discovery, and guiding rational design. However, the growing number of ML-driven studies in PSC research can be difficult to navigate, particularly for experimentalists and scientists without a computational background. To address this gap, this review is written with accessibility in mind, and it is structured to serve as a bridge between ML experts and the broader materials science community. We provide an overview of how ML can be applied to PSCs, from databases and data preprocessing to model training, evaluation, and interpretability. Advantages and limitations of different approaches are critically assessed, with emphasis on how dataset choice, algorithms, and metrics affect reliability. We conclude by outlining current challenges and open questions, as well as potential directions where the integration of ML with experimental and theoretical research could further advance the development of perovskite solar cells.