Abstract
Many organic electronics applications such as organic solar cells or thermoelectric generators rely on PEDOT:PSS as a conductive polymer that is printable and transparent. It was found that doping PEDOT:PSS with sorbitol enhances the conductivity through morphological changes. However, the microscopic mechanism is not well understood. In this work, we combine computational tools with machine learning to investigate changes in morphological and electronic properties of PEDOT:PSS when doped with sorbitol. We find that sorbitol improves the alignment of PEDOT oligomers, leading to a reduction of energy disorder and an increase in electronic couplings between PEDOT chains. The high accuracy (r 2 > 0.9) and speed up of energy level predictions of neural networks compared to density functional theory enables us to analyze HOMO energies of PEDOT oligomers as a function of time. We find a surprisingly low degree of static energy disorder compared to other organic semiconductors. This finding might help to better understand the microscopic origin of the high charge carrier mobility of PEDOT:PSS in general and potentially help to design new conductive polymers.
Highlights
While the number of organic semiconductors—both small molecules and polymers—is large and growing [1,2,3,4,5,6,7], there are only very few examples of highly conductive organic materials, one of them being poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS, see figure 1) [8,9,10,11,12,13]
We start with an analysis of PEDOT conformations as well as PEDOT:PEDOT and PEDOT:PSS arrangement, followed by a density functional theory (DFT) and ML based analysis of the energy levels of PEDOT oligomers and the static and dynamic energy disorder in PEDOT:PSS
We presented a multi-scale simulation study of PEDOT:PSS doped with D-sorbitol and analysed the effect of sorbitol on morphological and electronic properties of the PEDOT oligomers in PEDOT:PSS
Summary
While the number of organic semiconductors—both small molecules and polymers—is large and growing [1,2,3,4,5,6,7], there are only very few examples of highly conductive organic materials, one of them being poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS, see figure 1) [8,9,10,11,12,13]. The boxes are analyzed using DFT, yielding the energy disorder of the PEDOT oligomers caused by their individual conformations in the amorphous film. The analysis of a dynamical time series (MD trajectory) of each molecule in the film is computationally too costly, so we employed the data obtained by analyzing the last MD timestep to train a neural network model that can predict the HOMO energies of arbitrary conformations of PEDOT oligomers at a fraction of the cost of a DFT calculation
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