2016
DOI: 10.1039/c6tc03823a
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Quantitative prediction of morphology and electron transport in crystal and disordered organic semiconductors

Abstract: The morphologies and electron mobilities for 20 single-crystal and 21 thin-film organic n-type semiconductors are predicted using a multi-mode methodology previously applied by our group for p-type materials [I. Yavuz, et al., J. Am. Chem. Soc., 2015, 137, 2856–2866].

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Cited by 29 publications
(18 citation statements)
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“…One challenge underlying all theoretical efforts to simulate organic electronics applications and to computationally design new materials is the inherent multiscale nature of all models of organic electronics (see Figure 1). [46][47][48][49] The quantitative prediction of thin film properties like the charge carrier mobility ( Figure 1b) [39,[50][51][52][53] or device properties (Figure 1c) [54] requires knowledge about the electronic structure of each individual molecule in a disordered system comprising of thousands of molecules. [46][47][48][49] The quantitative prediction of thin film properties like the charge carrier mobility ( Figure 1b) [39,[50][51][52][53] or device properties (Figure 1c) [54] requires knowledge about the electronic structure of each individual molecule in a disordered system comprising of thousands of molecules.…”
Section: Introductionmentioning
confidence: 99%
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“…One challenge underlying all theoretical efforts to simulate organic electronics applications and to computationally design new materials is the inherent multiscale nature of all models of organic electronics (see Figure 1). [46][47][48][49] The quantitative prediction of thin film properties like the charge carrier mobility ( Figure 1b) [39,[50][51][52][53] or device properties (Figure 1c) [54] requires knowledge about the electronic structure of each individual molecule in a disordered system comprising of thousands of molecules. [46][47][48][49] The quantitative prediction of thin film properties like the charge carrier mobility ( Figure 1b) [39,[50][51][52][53] or device properties (Figure 1c) [54] requires knowledge about the electronic structure of each individual molecule in a disordered system comprising of thousands of molecules.…”
Section: Introductionmentioning
confidence: 99%
“…[42][43][44][45] Thin film and device properties not only depend on the molecular structure of the constituent materials but also on the microscopic arrangement of molecules as well as on the mesoscale structure formation (Figure 1a). [46][47][48][49] The quantitative prediction of thin film properties like the charge carrier mobility ( Figure 1b) [39,[50][51][52][53] or device properties ( Figure 1c) [54] requires knowledge about the electronic structure of each individual molecule in a disordered system comprising of thousands of molecules. [55][56][57] The formation of the morphology depends on weak intermolecular forces acting on long time scales during film preparation.…”
Section: Introductionmentioning
confidence: 99%
“…10 Specifically, this simple approach often allows to describe qualitatively the difference in charge mobilities within the series of structurally similar compounds, 30 and was recently proposed as a means for screening the high-mobility OSs. [25][26][27] However, several concerns arise with the charge transport model based on Eq. (1) (we will henceforth refer to it as to Marcus model).…”
Section: Introductionmentioning
confidence: 99%
“…Their incoherent hopping model takes into account local anisotropy of molecular crystals, but it completely neglects structural and energetic disorder. Another recent example of the molecular-scale extension of the GDM applied on low-molecular-weight materials is a detailed calculation procedure suggested by Deng et al in Nature Protocols, 42 or the work published by Yavuz et al, 43,44 in which the parameters of Gaussian DOS were obtained for the crystal structures calculated using molecular dynamics.…”
Section: Introductionmentioning
confidence: 99%