Kranthiraja Kakaraparthi, Saeki Akinori
Department of Applied Chemistry, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan.
Innovative Catalysis Science Division, Institute for Open and Transdisciplinary Research Initiatives (ICS-OTRI), Osaka University, 1-1 Yamadaoka, Suita, Osaka 565-0871, Japan.
ACS Appl Mater Interfaces. 2022 Jun 29;14(25):28936-28944. doi: 10.1021/acsami.2c06077. Epub 2022 Jun 13.
Despite the progress in machine learning (ML) in terms of prediction of power conversion efficiency (PCE) in organic photovoltaics (OPV), the effectiveness of ML in practical applications is still lacking owing to the complex structure-property relationship. Therefore, verifying the potential of ML through experiments can amplify the use of ML models. Herein, we developed a new series of π-conjugated polymers comprising benzodithiophene and thiazolothiazole with fluorination and alkylthio chains (PBDTTzBO, PFSBDTTzBO, and PFBDTTzBO) for non-fullerene (NF) acceptors based on our random-forest ML model for OPVs. Notably, the order of the ML-predicted PCEs of these polymers with IT-4F (9.93, 11.35, and 11.47%) was in good agreement with their experimental PCEs (5.24, 7.35, and 10.30%). In contrast, an inverse correlation was observed between the predicted (9.20, 12.29, and 12.20%) and experimental (11.98, 1.57, and 6.53%) PCEs with Y6. Both the findings are interpreted in terms of surface morphology, transient photoconductivity, charge carrier mobility, polymer orientation, and miscibility, quantified by the Flory-Huggins parameters. Herein, we present an ML-assisted polymer design for high-performance non-fullerene organic photovoltaics (NFOPVs) and elucidate the importance of the subtle alterations in the morphology of NFOPVs.
尽管机器学习（ML）在预测有机光伏（OPV）的功率转换效率（PCE）方面取得了进展，但由于复杂的结构-性能关系，ML在实际应用中的有效性仍然不足。因此，通过实验验证ML的潜力可以扩大ML模型的应用。在此，基于我们针对OPV的随机森林ML模型，我们开发了一系列新的含苯并二噻吩和噻唑并噻唑且带有氟化和烷硫基链的π共轭聚合物（PBDTTzBO、PFSBDTTzBO和PFBDTTzBO），用于非富勒烯（NF）受体。值得注意的是，这些聚合物与IT-4F的ML预测PCE顺序（9.93%、11.35%和11.47%）与其实验PCE（5.24%、7.35%和10.30%）高度吻合。相比之下，对于与Y6的聚合物，预测PCE（9.20%、12.29%和12.20%）与实验PCE（11.98%、1.57%和6.53%）之间观察到负相关。这两个发现都根据表面形态、瞬态光电导率、电荷载流子迁移率、聚合物取向和混溶性进行了解释，这些通过弗洛里-哈金斯参数进行了量化。在此，我们展示了一种用于高性能非富勒烯有机光伏（NFOPV）的ML辅助聚合物设计，并阐明了NFOPV形态细微变化的重要性。
