Machine Learning-Driven Advances in Perovskite Materials and Solar Cells

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Original abstract

Driven by advances in renewable energy technologies, research on perovskite optoelectronics has advanced rapidly across material exploration, device engineering, and intelligent integrated systems. Conventional trial-and-error experiments face inherent constraints in precisely regulating perovskite chemical compositions and microstructures, as well as in mitigating degradation in perovskite solar cells (PSCs). Artificial intelligence (AI) and the Internet of Things (IoT) have emerged as powerful tools for material discovery, synthetic condition design, and the prediction of perovskite fundamental properties and device outputs. This review systematically summarizes recent advances in machine learning (ML) implementations for PSC research, covering molecular-scale material screening, synthetic parameter optimization, performance forecasting, device architecture design, and system performance evaluation. We further elaborate on key obstacles hindering ML-assisted perovskite development, including insufficient operational stability, barriers to large-scale fabrication, and limited computational efficiency. Last, we outline promising research avenues and highlight the transformative capacity of ML to advance high-performance, manufacturable perovskite optoelectronic devices.

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