2016
DOI: 10.1021/acs.jpclett.6b00871
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A Model of Charge-Transfer Excitons: Diffusion, Spin Dynamics, and Magnetic Field Effects

Abstract: In this letter we explore how the microscopic dynamics of charge transfer (CT) excitons are influenced by the presence of an external magnetic field in disordered molecular semiconductors. This influence is driven by the dynamic interplay between the spin and spatial degrees of freedom of the electron-hole pair. To account for this interplay we have developed a numerical framework that combines a traditional model of quantum spin dynamics with a stochastic coarse-grained model of charge transport. This combina… Show more

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Cited by 19 publications
(22 citation statements)
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“…Our model does not include any specific atomistic-level detail, nor does it include high-level information about the electronic structure. Nonetheless, as we have previously demonstrated, [34,35] when this model is parameterized appropriately it exhibits the remarkable ability to simultaneously reproduce multiple experimental observations related to the dynamics of CT excitons.…”
Section: Namicsmentioning
confidence: 80%
“…Our model does not include any specific atomistic-level detail, nor does it include high-level information about the electronic structure. Nonetheless, as we have previously demonstrated, [34,35] when this model is parameterized appropriately it exhibits the remarkable ability to simultaneously reproduce multiple experimental observations related to the dynamics of CT excitons.…”
Section: Namicsmentioning
confidence: 80%
“…In WS 2 /tetracene heterostructure, because CT exciton binding energy E B is inversely proportional to the e-h distance, delocalized CT excitons with large e-h distance have smaller E B and higher energy. When the e-h distance is reduced, the more localized CT excitons with lower energies can serve as traps ( 43 ). There could be certain sites to accommodate the lower-energy and more localized states where emission is more likely to occur ( 25 ).…”
Section: Resultsmentioning
confidence: 99%
“…Instead of performing molecular simulations, many modelers of OSCs resort to phenomenological models [20][21][22][23] with parameters inferred from experiments or a limited number of quantum calculations, e.g., lattice models with Gaussian energetic disorder. [24][25][26] These methods have greatly advanced our understanding of electronic processes in OSCs, but have limited predictive power due to the lack of molecular details and often chemical specificity. In this work, we aim at exploring the use of machine learning (ML) in predicting the electronic properties of OSCs with an accuracy comparable to DFT but at a much lower computational cost.…”
Section: Introductionmentioning
confidence: 99%