Study on the optimization of large-area perovskite fabrication assisted by machine learning and their module performance
Perovskite solar cells have become a frontier research topic due to their low cost and significant advantages in solution processing.However,the scalability of fabricating large-area perovskite films re-mains a challenge,which hinders the development and commercialization of perovskite solar modules.De-veloping high-throughput fabrication techniques for perovskite films is an effective approach.In this study,machine learning high-throughput algorithms were used to explored the influence of coating param-eters on the preparation of large-area perovskite films prepared by blade coating in ambient air condition.Based on the machine learning results,we selected the optimal coating parameters and successfully fabri-cated 36 cm2 perovskite solar modules.The modules prepared in ambient air condition achieved a photoe-lectric conversion efficiency of 18.89％.To further enhance the performance of the modules,a layer of phenylethylammonium iodide was decorated on the surface of the perovskite film to reduce interface de-fects,and the efficiency of the corresponding modules reached up to 19.53％.The module still retaining 95％of the initial efficiency for 480 h under air condition.
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