Evaluating new, promising organic molecules to make next-generation organic optoelectronic devices necessitates the evaluation of charge carrier transport performance through the semi-conducting medium. In this work, we utilize quantum chemical calculations (QCC) and kinetic Monte Carlo (KMC) simulations to predict the zero-field hole mobilities of ∼100 morphologies of the benchmark polymer poly(3-hexylthiophene), with varying simulation volume, structural order, and chain-length polydispersity. Morphologies with monodisperse chains were generated previously using an optimized molecular dynamics force-field and represent a spectrum of nanostructured order. We discover that a combined consideration of backbone clustering and system-wide disorder arising from side-chain conformations are correlated with hole mobility. Furthermore, we show that strongly interconnected thiophene backbones are required for efficient charge transport. This definitively shows the role “tie-chains” play in enabling mobile charges in P3HT. By marrying QCC and KMC over multiple length- and time-scales, we demonstrate that it is now possible to routinely probe the relationship between molecular nanostructure and device performance.
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Using Graphs to Quantify Energetic and Structural Order in Semicrystalline Oligothiophene Thin Films
In semicrystalline conjugated polymer thin films, the mobility of charges depends on the arrangement of the individual polymer chains. In particular, the ordering of the polymer backbones affects the charge transport within the film, as electron transfer generally occurs along the backbones with alternating single and double bonds. In this paper, we demonstrate that polymer ordering should be discussed not only in terms of structural but also energetic ordering of polymer chains. We couple data from molecular dynamics simulations and quantum chemical calculations to quantify both structural and energetic ordering of polymer chains. We leverage a graph-based representation of the polymer chains to quantify the transport pathways in a computationally efficient way. Next, we formulate the morphological descriptors that correlate well with hole mobility determined using kinetic Monte Carlo simulations. We show that the shortest and fastest path calculations are predictive of mobility in equilibrated morphologies. In this sense, we leverage graph-based descriptors to provide a basis for the quantitative structure-property relationships.
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- Award ID(s):
- 1653954
- NSF-PAR ID:
- 10066734
- Date Published:
- Journal Name:
- Molecular Systems Design & Engineering
- ISSN:
- 2058-9689
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Charge transport in molecular solids, such as semiconducting polymers, is strongly affected by packing and structural order over several length scales. Conventional approaches to modeling these phenomena range from analytical models to numerical models using quantum mechanical calculations. While analytical approaches cannot account for detailed structural effects, numerical models are expensive for exhaustive (and statistically significant) analysis. Here, we report a computationally scalable methodology using graph theory to explore the influence of molecular ordering on charge mobility. This model accurately reproduces the analytical results for transport in nematic and isotropic systems, as well as experimental results of the dependence of the charge carrier mobility on orientation correlation length for polymers. We further model how defect distribution (correlated and uncorrelated) in semiconducting polymers can modify the mobility, predicting a critical defect density above which the mobility plummets. This work enables rapid (and computationally extensible) evaluation of charge mobility semiconducting polymer devices.
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Abstract Conjugated polyelectrolytes (CPEs), comprised of conjugated backbones and pendant ionic functionalities, are versatile organic materials with diverse applications. However, the myriad of possible molecular structures of CPEs render traditional, trial-and-error materials discovery strategy impractical. Here, we tackle this problem using a data-centric approach by incorporating machine learning with high-throughput first-principles calculations. We systematically examine how key materials properties depend on individual structural components of CPEs and from which the structure–property relationships are established. By means of machine learning, we uncover structural features crucial to the CPE properties, and these features are then used as descriptors in the machine learning to predict the properties of unknown CPEs. Lastly, we discover promising CPEs as hole transport materials in halide perovskite-based optoelectronic devices and as photocatalysts for water splitting. Our work could accelerate the discovery of CPEs for optoelectronic and photocatalytic applications.
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Abstract Charge transport in semiconducting polymers is inextricably linked to their microstructure, making the characterization of polymer morphology at all length‐scales essential for understanding the factors that limit mobility in these materials. Indeed, charge transport depends both on the ability of polarons to delocalize at the approximately nanometer length‐scale and navigate a complex energetic and morphological mesoscale landscape. While characterization of the mesoscale morphology of polymers is well‐established, studies of the local chain packing and nanoscale disorder, which affect delocalization, can be significantly more difficult to carry out. Through infrared charge modulation spectroscopy and theoretical modeling, the effect of the local chain environment on polaron delocalization is directly measured and quantified. Using a series of polymers based on the model system, poly(3‐hexylthiophene), the link between disorder and polaron localization is systematically explored. Polaron delocalization is correlated with known trends in mobility, revealing that while charge delocalization is always beneficial, the formation of tie‐chains is necessary to reach the highest mobilities in semicrystalline polymers. The results provide direct evidence for the importance of both nanoscale (charge carrier delocalization) and mesoscale (tie‐chains) orders, demonstrating the need to distinguish the key length‐scale limiting charge transport in the design of new, high mobility polymers.
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Abstract Solution‐processable highly conductive polymers are of great interest in emerging electronic applications. For p‐doped polymers, conductivities as high a nearly 105S cm−1have been reported. In the case of n‐doped polymers, they often fall well short of the high values noted above, which might be achievable, if much higher charge‐carrier mobilities determined could be realized in combination with high charge‐carrier densities. This is in part due to inefficient doping and dopant ions disturbing the ordering of polymers, limiting efficient charge transport and ultimately the achievable conductivities. Here, n‐doped polymers that achieve a high conductivity of more than 90 S cm−1by a simple solution‐based co‐deposition method are reported. Two conjugated polymers with rigid planar backbones, but with disordered crystalline structures, exhibit surprising structural tolerance to, and excellent miscibility with, commonly used n‐dopants. These properties allow both high concentrations and high mobility of the charge carriers to be realized simultaneously in n‐doped polymers, resulting in excellent electrical conductivity and thermoelectric performance.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/C8ME00028J
