The role of machine learning in organic solar cell research

Machine learning (ML) and artificial intelligence (AI) methods are emerging as promising technologies for enhancing the performance of low-cost photovoltaic (PV) cells in miniaturized electronic devices. Indeed, ML is set to significantly contribute to the development of more efficient and cost-effective solar cells. This systematic review offers an extensive analysis of recent ML techniques in designing novel solar cell materials and structures, highlighting their potential to transform the low-cost solar cell research and development landscape. In this presentation, a comprehensive view of the machine learning methods (e.g., Gaussian process regression (GPR), Bayesian optimization (BO), and deep neural networks (DNNs)) used for organic photovoltaic materials will be introduced. Basic steps to train ML models well be given. The research status with respect to the applications of machine learning in organic solar cells will be discussed. The presentation will conclude with insights on the challenges, prospects, and future directions of ML in low-cost solar cell research and development. Ultimately, the application of ML techniques in solar energy can revolutionize the industry and pave the way for a cleaner and more sustainable future.