Accelerating evaluation of the mobility of ionic liquid-modulated PEDOT flexible electronics using machine learning
文献类型：期刊论文
| 作者 | Ding, Wei-Lu2; Lu, Yumiao2; Peng, Xing-Liang3; Dong, Hao2; Chi, Wei-Jie4; Yuan, Xiaoqing2; Sun, Zhu-Zhu5; He, Hongyan1,2,6 |
| 刊名 | JOURNAL OF MATERIALS CHEMISTRY A
 |
| 出版日期 | 2021-11-23 |
| 卷号 | 9期号:45页码:25547-25557 |
| ISSN号 | 2050-7488 |
| DOI | 10.1039/d1ta08013j |
| 英文摘要 | PEDOT has been widely used in advanced electronics, and one of the keys to determine the performance is hole mobility. PEDOT commonly shows amorphous morphology ascribed to the flexibility of its backbone, giving rise to a wide difference in mobility. To boost the mobility, one generally introduces an ionic liquid (IL) to modulate the morphology to be more ordered. To estimate the mobility, one needs to do molecular dynamics (MD) simulations to acquire the abundant conformers, then to investigate the transfer integral (V-ij) via quantum mechanics (QM) calculations theoretically or via quantum Hall effect measurements experimentally. Here, with the help of machine learning (ML) technology (involving supervised learning algorithms of linear regression (LR), artificial neural network (ANN), random forest (RF), and gradient boosting decision tree (GBDT)), we can predict V-ij accurately compared to the routine MD -> QM method (for ANN and RF, R-2 > 0.9 and MAE = 10(-3) eV), while shortening the prediction time by 6 orders of magnitude. Generalization verification on an additional five IL-PEDOT cases confirms the predictive ability of the model. Then, the predicted V-ij was used to estimate the mobility. Finally, representative IL [EMIM][TFSI]-regulated PEDOT aqueous solutions with different concentrations were experimentally characterized by AFM and conductivity measurements, the conductivity being in line with the change tendency of the estimated mobility. This alternative ML model opens up new perspectives for ultrafast prediction of the mobility of IL-PEDOT in any morphology and can be transferred to other analogs before real device construction. |
| WOS关键词 | HOLE TRANSPORT ; MULTISCALE ; MORPHOLOGY ; CHEMISTRY ; POLYMERS |
| 资助项目 | National Natural Science Foundation of China[22008238] ; National Natural Science Foundation of China[21922813] ; National Natural Science Foundation of China[21890762] ; National Natural Science Foundation of China[21776278] ; National Natural Science Foundation of China[21673175] ; DNL Cooperation Fund, CAS[DNL 180202] ; Youth Innovation Promotion Association CAS[2017066] |
| WOS研究方向 | Chemistry ; Energy & Fuels ; Materials Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000716529600001 |
| 出版者 | ROYAL SOC CHEMISTRY |
| 资助机构 | National Natural Science Foundation of China ; DNL Cooperation Fund, CAS ; Youth Innovation Promotion Association CAS |
| 源URL | [http://ir.ipe.ac.cn/handle/122111/50938]  |
| 专题 | 中国科学院过程工程研究所 |
| 通讯作者 | Sun, Zhu-Zhu; He, Hongyan |
| 作者单位 | 1.Chinese Acad Sci, Innovat Acad Green Mfg, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Beijing Key Lab Ion Liquids Clean Process, Inst Proc Engn, CAS Key Lab Green Proc & Engn, Beijing 100190, Peoples R China 3.Tsinghua Univ, Dept Chem, MOE Key Lab Organ Optoelect & Mol Engn, Beijing 100084, Peoples R China 4.Singapore Univ Technol & Design, Singapore Sci & Math Cluster, Singapore 487372, Singapore 5.Heze Univ, Coll Phys & Elect Engn, Heze 274015, Peoples R China 6.Dalian Natl Lab Clean Energy, Dalian 116023, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ding, Wei-Lu,Lu, Yumiao,Peng, Xing-Liang,et al. Accelerating evaluation of the mobility of ionic liquid-modulated PEDOT flexible electronics using machine learning[J]. JOURNAL OF MATERIALS CHEMISTRY A,2021,9(45):25547-25557. |
| APA | Ding, Wei-Lu.,Lu, Yumiao.,Peng, Xing-Liang.,Dong, Hao.,Chi, Wei-Jie.,...&He, Hongyan.(2021).Accelerating evaluation of the mobility of ionic liquid-modulated PEDOT flexible electronics using machine learning.JOURNAL OF MATERIALS CHEMISTRY A,9(45),25547-25557. |
| MLA | Ding, Wei-Lu,et al."Accelerating evaluation of the mobility of ionic liquid-modulated PEDOT flexible electronics using machine learning".JOURNAL OF MATERIALS CHEMISTRY A 9.45(2021):25547-25557. |
入库方式： OAI收割
来源：过程工程研究所
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