发光学报2024，Vol.45Issue(3)：399-406,8.DOI:10.37188/CJL.20230309
机器学习辅助钙钛矿薄膜制备工艺优化及特征重要性评估
Machine Learning Assisted Optimization of Perovskite Thin Film Fabrication Process and Assessment of Feature Importance
摘要
Abstract
The efficiency of perovskite solar cells has been improved to 26.1%in just ten years,which is very close to the certification efficiency of crystalline silicon solar cells(26.81%).This demonstrates the significant po-tential for industrialization.Currently,efforts are still being made to further enhance the efficiency of perovskite solar cells.However,various inseparable factors affect the performance of perovskite solar cells during the device prepara-tion process.Traditional methods often rely on trial and error to optimize the preparation process,resulting in time-consuming procedures.Bayesian optimization,a global optimization algorithm,has achieved remarkable success in addressing artificial intelligence,s black box problem.In this work,the Bayesian optimization is employed to opti-mize four key process parameters involved in the perovskite layer:excess percentage of lead iodide(PbI2),anneal-ing temperature,annealing time,and vacuum extraction time.The costs of research and development have been sig-nificantly reduced,as well as the required time for such activities has also been shortened.The improvement was achieved through five rounds of experimental iterations and 34 sets of process conditions,ultimately resulting in the preparation of an inverse perovskite solar cell with a device efficiency rating of 23.56%.关键词
钙钛矿太阳能电池/机器学习/工艺优化/高效率Key words
perovskite solar cells/machine learning/process optimization/high efficiency分类
数理科学引用本文复制引用
弓箭,陈谦,李阳,马梦恩,马玉姣,吴绍航,刘冲,麦耀华..机器学习辅助钙钛矿薄膜制备工艺优化及特征重要性评估[J].发光学报,2024,45(3):399-406,8.基金项目
广东省重点领域研发计划项目(2019B010132004) （2019B010132004）
国家自然科学基金(62104082,62005099):广东省基础与应用基础研究基金(2022A1515010746,2022A1515011228) （62104082,62005099）
广州市科技计划(202201010458,202201010542)Supported by The Key Realm R&D Program of Guangdong Province(2019B010132004) （202201010458,202201010542）
National Natural Science Foundation of China(62104082,62005099) （62104082,62005099）
Guangdong Basic and Applied Basic Research Foundation(2022A1515010746,2022A1515011228) （2022A1515010746,2022A1515011228）
Science and Technology Program of Guangzhou(202201010458,202201010542) （202201010458,202201010542）
