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Accelerating evaluation of the mobility of ionic liquid-modulated PEDOT flexible electronics using machine learning
Ding, Wei-Lu1; Lu, Yumiao1; Peng, Xing-Liang2; Dong, Hao1; Chi, Wei-Jie3; Yuan, Xiaoqing1; Sun, Zhu-Zhu4; He, Hongyan1,5,6
2021-11-23
Source PublicationJOURNAL OF MATERIALS CHEMISTRY A
ISSN2050-7488
Volume9Issue:45Pages:25547-25557
AbstractPEDOT 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.
DOI10.1039/d1ta08013j
Language英语
WOS KeywordHOLE TRANSPORT ; MULTISCALE ; MORPHOLOGY ; CHEMISTRY ; POLYMERS
Funding ProjectNational 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 Research AreaChemistry ; Energy & Fuels ; Materials Science
WOS SubjectChemistry, Physical ; Energy & Fuels ; Materials Science, Multidisciplinary
Funding OrganizationNational Natural Science Foundation of China ; DNL Cooperation Fund, CAS ; Youth Innovation Promotion Association CAS
WOS IDWOS:000716529600001
PublisherROYAL SOC CHEMISTRY
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ipe.ac.cn/handle/122111/50939
Collection中国科学院过程工程研究所
Corresponding AuthorSun, Zhu-Zhu; He, Hongyan
Affiliation1.Chinese Acad Sci, Beijing Key Lab Ion Liquids Clean Process, Inst Proc Engn, CAS Key Lab Green Proc & Engn, Beijing 100190, Peoples R China
2.Tsinghua Univ, Dept Chem, MOE Key Lab Organ Optoelect & Mol Engn, Beijing 100084, Peoples R China
3.Singapore Univ Technol & Design, Singapore Sci & Math Cluster, Singapore 487372, Singapore
4.Heze Univ, Coll Phys & Elect Engn, Heze 274015, Peoples R China
5.Chinese Acad Sci, Innovat Acad Green Mfg, Beijing 100190, Peoples R China
6.Dalian Natl Lab Clean Energy, Dalian 116023, Peoples R China
Recommended Citation
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.
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