Machine learning empowers efficient design of ternary organic solar cells with PM6 donor  

作　　者：Kiran A.Nirmal Tukaram D.Dongale Santosh S.Sutar Atul C.Khot Tae Geun Kim 

机构地区：[1]School of Electrical Engineering,Korea University,Anam-ro 145,Seongbuk-gu,Seoul,Republic of Korea [2]Computational Electronics and Nanoscience Research Laboratory,School of Nanoscience and Biotechnology,Shivaji University,Kolhapur 416004,India [3]Yashwantrao Chavan School of Rural Development,Shivaji University,Kolhapur 416004,India

出　　处：《Journal of Energy Chemistry》2025年第1期337-347,共11页能源化学(英文版)

基　　金：National Research Foundation of Korea (NRF) grant (No. 2016R1A3B 1908249) funded by the Korean government。

摘　　要：Organic solar cells(OSCs) hold great potential as a photovoltaic technology for practical applications.However, the traditional experimental trial-and-error method for designing and engineering OSCs can be complex, expensive, and time-consuming. Machine learning(ML) techniques enable the proficient extraction of information from datasets, allowing the development of realistic models that are capable of predicting the efficacy of materials with commendable accuracy. The PM6 donor has great potential for high-performance OSCs. However, it is crucial for the rational design of a ternary blend to accurately forecast the power conversion efficiency(PCE) of ternary OSCs(TOSCs) based on a PM6 donor.Accordingly, we collected the device parameters of PM6-based TOSCs and evaluated the feature importance of their molecule descriptors to develop predictive models. In this study, we used five different ML algorithms for analysis and prediction. For the analysis, the classification and regression tree provided different rules, heuristics, and patterns from the heterogeneous dataset. The random forest algorithm outperforms other prediction ML algorithms in predicting the output performance of PM6-based TOSCs. Finally, we validated the ML outcomes by fabricating PM6-based TOSCs. Our study presents a rapid strategy for assessing a high PCE while elucidating the substantial influence of diverse descriptors.

关 键 词：Machine learning Ternary organic solarcells PM6 donor PCE 

分 类 号：O62[理学—有机化学]