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
弓箭 1陈谦 1李阳 1马梦恩 2马玉姣 2吴绍航 2刘冲 2麦耀华2
作者信息
- 1. 五邑大学 智能制造学部,广东 江门 529020
- 2. 暨南大学 物理与光电工程学院,广东 广州 510632
- 折叠
摘要
钙钛矿太阳能电池仅用十年左右的时间将效率提升至认证的26.1%,非常接近晶硅太阳能电池26.81%的认证效率,展现出巨大的产业化潜力.当前,钙钛矿太阳能电池器件效率还在提升,然而在器件制备过程中,钙钛矿太阳能电池的性能受到许多不可分割的因素影响,传统方法往往采用试错的方式来优化钙钛矿太阳能电池的制备工艺,花费了大量的时间.贝叶斯优化是一种全局优化算法,在解决人工智能的黑盒问题方面取得了很大的成功.本文利用贝叶斯优化算法对钙钛矿层涉及到的碘化铅(PbI2)过量百分比、退火温度、退火时间、真空萃取时间四个工艺参数进行优化选择,显著降低了研发成本,缩短了研发时间.通过五轮实验迭代,累计34组工艺条件,制备出了器件效率为23.56%的反型钙钛矿太阳能电池.
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引用本文复制引用
基金项目
广东省重点领域研发计划项目(2019B010132004)
国家自然科学基金(2022A1515010746)
国家自然科学基金(2022A1515011228)
广州市科技计划(202201010458)
广州市科技计划(202201010542)
出版年
2024
国家科技期刊平台
NSTL
万方数据