Electronic Excited States of Conjugated Oligothiophenes

Taliani, C.; Gebauer, W.

doi:10.1002/9783527611713.ch7

Cited by 13 publications

(9 citation statements)

References 163 publications

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“…Oligothiophenes, OT n , with an even number of thiophene rings ( n ) crystallize in herringbone layers, with dominant inequivalent interactions leading to a very large Davydov splitting of the order of 1 eV. , For even n , the molecular symmetry is C 2 h , and the transition dipole moment corresponding to the lowest optical transition is not aligned with the long (inertial) axis, leading to a slight misalignment of the two transition dipoles in the monoclinic unit cell. − The ac -polarized absorption spectrum is dominated by the blue-shifted H-band, with a much weaker ac -polarized peak near the origin ( A 0 ) . By contrast, the b -polarized spectrum is much weaker (because of the nearly parallel transition dipoles) but dominated by the origin vibronic peak as expected for an ideal J-aggregate.…”

Section: Discussionmentioning

confidence: 99%

Absorption, Circular Dichroism, and Photoluminescence in Perylene Diimide Bichromophores: Polarization-Dependent H- and J-Aggregate Behavior

et al. 2011

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Using a single-mode Holstein Hamiltonian with through-space excitonic couplings evaluated quantum mechanically, the absorption, circular dichroism, and photoluminescence spectral line shapes of a chiral perylene diimide dimer complex were accurately reproduced. In general, a dimer consisting of two chromophores related through a C(2) rotation is neither a J- nor an H-aggregate because oscillator strength is divided between the top and bottom of the exciton band. The division gives rise to the two Davydov components per vibronic band in the absorption spectrum. Nevertheless, it is shown that the vibronic structure of the absorption component polarized in the same direction as the lower (upper) Davydov component is identical to what one would obtain from an ideal J- (H-) aggregate. Emission generally contains both polarization components, but the component polarized in the same direction as the lower (upper) Davydov component behaves similarly to the emission from an ideal J- (H-) aggregate. The basic photophysical behavior also applies to molecular crystals containing two molecules per unit cell in which the interactions between inequivalent molecules dominate over interactions between equivalent molecules.

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Section: Discussionmentioning

confidence: 99%

Absorption, Circular Dichroism, and Photoluminescence in Perylene Diimide Bichromophores: Polarization-Dependent H- and J-Aggregate Behavior

et al. 2011

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“…100,101 Another caveat is that the solvent effects on the excited-state energies and transition dipoles are neglected, and this approximation is acceptable given that the solvatochromism of OTs is relatively small (see Section III in SI for more discussions). 102 Figure 6: Experimental (black solid lines) and calculated (blue solid lines) absorption spectra of OTs in dichloromethane. The contributions from the first and second excited states to the absorption spectra are shown as green and red dashed lines, respectively.…”

Section: Transition Dipole Moment Prediction and Charge-transfer Charmentioning

confidence: 99%

“…In using Eq. , we have also assumed the adiabaticity between the two excited states, a good approximation for OTs in solution. , Another caveat is that the solvent effects on the excited-state energies and transition dipoles are neglected, and this approximation might be reasonable given that the solvatochromism of OTs is relatively small (see section SIII in the Supporting Information for more discussions). − However, as some configurations of OTs have charge-transfer character, electronic polarization could be important, but its effects may be hidden under the broad spectra.…”

Section: Application To Absorption Spectrummentioning

confidence: 99%

Deep Learning for Optoelectronic Properties of Organic Semiconductors

Liu

Sun

et al. 2020

J. Phys. Chem. C

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Atomistic modeling of energetic disorder in organic semiconductors (OSCs) and its effects on the optoelectronic properties of OSCs requires a large number of excitedstate electronic-structure calculations, a computationally daunting task for many OSC applications. In this work, we advocate the use of deep learning to address this challenge and demonstrate that state-of-the-art deep neural networks (DNNs) are capable of predicting the electronic properties of OSCs at an accuracy comparable with the quantum chemistry methods used for generating training data. We extensively investigate the performances of four recent DNNs (deep tensor neural network, SchNet, message passing neural network, and multilevel graph convolutional neural network) in predicting various electronic properties of an important class of OSCs, i.e., oligothiophenes (OTs), including their HOMO and LUMO energies, excited-state energies and associated transition dipole moments. We find that SchNet shows the best performance for OTs of different sizes (from bithiophene to sexithiophene), achieving average prediction errors in the range of 20-80meV compared to the results from (time-dependent) density functional theory. We show that SchNet also consistently outperforms shallow feed-forward neural networks, especially in difficult cases with large molecules or limited training data. We further show that SchNet could predict the transition dipole moment accurately, a task previously known to be difficult for feed-forward neural networks, and we ascribe the relatively large errors in transition dipole prediction seen for some OT configurations to the charge-transfer character of their excited states. Finally, we demonstrate the effectiveness of SchNet by modeling the UV-Vis absorption spectra of OTs in dichloromethane and a good agreement is observed between the calculated and experimental spectra. Our results show the great promise of DNNs in depicting the rugged energy landscapes encountered in OSCs, serving as the first step in the atomistic modeling of optoelectronic processes in OSCs relevant to device performances. 2

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“…To address the first issue, a common strategy is to infer the electronic properties of long oligomers/polymers by extrapolating short oligomer properties, and empirical relations have been devised. [5][6][7][8][9] However, this approach is mostly used with optimized geometries, and how to learn from short oligomers to predict the properties of long oligomers/polymers at their nonequilibrium geometries remains largely unexplored. In this work, following the extrapolation idea we advocate the use of transfer learning, a machine learning (ML) technique, to tackle this problem.…”

Section: Introductionmentioning

confidence: 99%

Transfer learning with graph neural networks for optoelectronic properties of conjugated oligomers

Lee

et al. 2021

The Journal of Chemical Physics

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Despite the remarkable progress of machine learning (ML) techniques in chemistry, modeling the optoelectronic properties of long conjugated oligomers and polymers with ML remains challenging due to the difficulty in obtaining sufficient training data. Here we use transfer learning to address the data scarcity issue by pretraining graph neural networks using data from short oligomers. With only a few hundred training data, we are able to achieve an average error of about 0.1 eV for excited state energy of oligothiophenes against TDDFT calculations. We show that the success of our transfer learning approach relies on the relative locality of low-lying electronic excitations in long conjugated oligomers. Finally, we demonstrate the transferability of our approach by modeling the lowest-lying excited-state energies of poly(3-hexylthiopnene) (P3HT) in its single-crystal and solution phases using the transfer learning models trained with data of gas-phase oligothiophenes. The transfer learning predicted excited-state energy distributions agree quantitatively with TDDFT calculations and capture some important qualitative features observed in experimental absorption spectra.

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Electronic Excited States of Conjugated Oligothiophenes

Cited by 13 publications

References 163 publications

Absorption, Circular Dichroism, and Photoluminescence in Perylene Diimide Bichromophores: Polarization-Dependent H- and J-Aggregate Behavior

Absorption, Circular Dichroism, and Photoluminescence in Perylene Diimide Bichromophores: Polarization-Dependent H- and J-Aggregate Behavior

Deep Learning for Optoelectronic Properties of Organic Semiconductors

Transfer learning with graph neural networks for optoelectronic properties of conjugated oligomers

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