Open‐circuit voltage loss and dielectric constants as new descriptors in machine learning study on organic photovoltaics. Issue 5 (9th November 2022)

• Record Type: Journal Article
• Authors: Yang, Bing; Zhang, Cai‐Rong; Wang, Yu; Zhao, Miao; Yu, Hai‐Yuan; Liu, Zi‐Jiang; Liu, Xiao‐Meng; Chen, Yu‐Hong; Wu, You‐Zhi; Chen, Hong‐Shan
• Is Part Of: International journal of quantum chemistry. Volume 123:Issue 5(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-11-09
• Subjects: dielectric constant -- machine learning -- molecular descriptors -- open‐circuit voltage loss -- organic photovoltaics; Quantum chemistry -- Periodicals; 541.28
• DOI: 10.1002/qua.27039
• Languages: English
• ISSNs: 0020-7608

Abstract

Abstract: Molecular descriptors are critical for determining the accuracy of machine learning (ML) study on organic photovoltaics (OPV). To unravel the complex relationship between molecular properties and device performance, on the basis of 510 donor‐acceptor pairs in OPV active layer, the open‐circuit voltage loss ( V OC‐loss ), dielectric constants of donor and acceptor (ε‐D and ε‐A) were firstly implemented into property descriptor set that includes 41 quantities totally. Then, the five ML algorithms were applied to compare the property descriptor sets with and without V OC‐loss, ε‐D and ε‐A (coded as new and old sets) in the prediction of photovoltaic parameters. The ML results of Pearson's correlation coefficient and the slope of regression lines indicate the performances of new molecular descriptor set are prevailing to that of old set. Furthermore, the Gini important analysis indicates that the ε‐D, ε‐A and V OC‐loss are very important parameters for determining device performance. Higher dielectric constants and lower V OC‐loss will be more beneficial to the performance of OPV devices. Abstract : The open‐circuit voltage loss ( V OC‐loss ), dielectric constants of donor and acceptor materials (ε‐D and ε‐A, respectively) were firstly implemented into molecular property descriptor set (MPDS). The machine‐learning algorithms random forest, extra trees regressor, gradient boosting regression tree, adaptive boosting and extreme gradient boosting were applied to compare the MPDS with and without V OC‐loss, ε‐D and ε‐A in photovoltaic parameters prediction.