Electronic Properties of Polythiophenes

Hotta, Shu; Ito, Kohzo

doi:10.1002/9783527611713.ch2

Cited by 3 publications

(2 citation statements)

References 188 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, this might not be surprising since excitons are also relatively localized in P3HT at ambient temperature due to conformational disorder as suggested by other theoretical studies. 95,98 The blue-shifted and much broader distribution for the P3HT solution compared to that for the crystal from our simulation is also consistent with the experimental UV-Vis absorption spectra of P3HT solution and crystal, 105,106 although the P3HT in the experiments is much longer potentially with substantial interchain and through-space intra-chain excitonic couplings. The successful application of the transfer learning model to P3HT shows the transferability of the trained model to different chemical environments, critical for its application to more relevant systems for optoelectronic devices, e.g., bulk heterojunctions for organic photovoltaics.…”

Section: Application To P3htsupporting

confidence: 85%

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

Lee

et al. 2021

The Journal of Chemical Physics

View full text Add to dashboard Cite

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.

show abstract

Section: Application To P3htsupporting

confidence: 85%

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

Lee

et al. 2021

The Journal of Chemical Physics

View full text Add to dashboard Cite

show abstract

“…At elevated temperatures, the protruding alkyl chains cause torsioning along the backbone of the molecule. Due to the rigidity of the double bond, the most probable location for this twisting is at the bridging bonds between the thiophene rings [5,6]. In general, polythiophenes have relatively low rotational barriers, making the chains fairly flexible and increasing the likelihood of twisting along the backbone [7].…”

Section: Chain Torsionmentioning

confidence: 99%

Effect of thermal cycling on performance of Poly(3-hexylthiophene) Transistors

2003

View full text Add to dashboard Cite

We present the results of studies on the electrical and physical modifications to Poly(3-hexylthiophene), upon thermal annealing. Thermally-induced performance modifications and thermal stability of polythiophene thin film transistors are explored. We observe substantial mobility improvements in devices annealed at low temperatures (<80°C), as well as increases in on/off ratios by two orders of magnitude at moderate anneal temperatures (~120°C). We document changes in conductivity, mobility, on current, and on/off ratio with anneal temperature and total thermal budget. In conjunction with material analysis, we develop qualitative models for the mechanisms involved in the annealing/ degradation processes. Hence, this study provides a comprehensive analysis of the effect of thermal cycling of polythiophene TFTs on various device performance metrics, and identifies the relevant thermal limits and failure mechanisms.

show abstract