Machine Learning Enhanced Quantum Dot Solar Cells: Accelerating Material Discovery and Device Optimization

Engineering notes

Engineering notes will be added by the Power for Solar editorial team.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

The integration of quantum dots (QDs) into photovoltaic (PV) devices holds significant promise for surpassing conventional efficiency limits through phenomena like multiple excitation generation, intermediate band formation and spectral tuneability. However, the extensive parameter space encompassing materials, architectures and fabrication techniques poses a considerable bottleneck in both experimental and theoretical optimisation. This review explores the transformative role of machine learning (ML), with its inherent capability to discern patterns from complex datasets, in revolutionising the design and optimisation of quantum dot solar cells (QDSCs). We examine how ML accelerates the discovery of novel materials, enhances the precision of optical modelling and enables predictive simulations of device performance. Through an analysis of recent studies, we highlight existing challenges such as data scarcity and the need for model interpretability, while also proposing future directions for this dynamic multidisciplinary frontier.

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