An official website of the United States government
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.
more »
« less
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.
more »
« less
- Award ID(s):
- 1653954
- PAR ID:
- 10066734
- Date Published:
- Journal Name:
- Molecular Systems Design & Engineering
- ISSN:
- 2058-9689
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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.more » « less
-
Surface tension is a critical property that influences polymer behavior at interfaces and affects applications ranging from coatings to biomedical devices. Traditional experimental methods for measuring polymer surface tension are time-consuming, costly, and sensitive to environmental conditions. Computational approaches such as molecular dynamics (MD) simulations are valuable but computationally intensive, especially for polymers with long chains. This study investigates the use of machine learning (ML) techniques to predict polymer surface tension using different levels of molecular representation, focusing on multilinear regression (MLR), random forest (RF), and graph neural networks (GNNs). A data set of 317 homopolymers collected from the PolyInfo database is used to train and evaluate these models. Descriptors are derived at various levels of complexity, ranging from manually calculated features to graph-based representations. The GNN approach captures the intrinsic connectivity of polymer structures, while the MLR and RF models rely on manually crafted descriptors. The performance of these models is compared with experimental data, with the GNN model demonstrating superior accuracy due to its ability to directly learn from molecular graphs. Our results show that GNNs can better capture complex nonlinear relationships in polymer structures than traditional descriptorbased methods, suggesting their significant potential for accelerating polymer design and development. The study also includes validation of model predictions against molecular dynamics simulations, highlighting the potential of GNNs to accurately model polymer interfacial properties.more » « less
-
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.more » « less
-
The hierarchical structure and dynamics of polymer solutions control the transport of nanoparticles (NPs) through them. Here, we perform multi-particle collision dynamics simulations of solutions of semiflexible polymer chains with tunable persistence length l p to investigate the effect of chain stiffness on NP transport. The NPs exhibit two distinct dynamical regimes – subdiffusion on short time scales and diffusion on long time scales. The long-time NP diffusivities are compared with predictions from the Stokes–Einstein relation (SER), mode-coupling theory (MCT), and a recent polymer coupling theory (PCT). Increasing deviations from the SER as the polymer chains become more rigid ( i.e. as l p increases) indicate that the NP motions become decoupled from the bulk viscosity of the polymer solution. Likewise, polymer stiffness leads to deviations from PCT, which was developed for fully flexible chains. Independent of l p , however, the long-time diffusion behavior is well-described by MCT, particularly at high polymer concentration. We also observed that the short-time subdiffusive dynamics are strongly dependent on polymer flexibility. As l p is increased, the NP dynamics become more subdiffusive and decouple from the dynamics of the polymer chain center-of-mass. We posit that these effects are due to differences in the segmental mobility of the semiflexible chains.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/C8ME00028J
