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Predicting structure and charge transport in semiconducting polymers

ORAL · Invited

Abstract

Organic semiconducting polymers for photovoltaics are improving in their performance, but a nanoscale picture of these materials is lacking. What does the donor-acceptor interface look like? What do free charges look like? And how do charges move, along and between chains? These are theoretically challenging questions, for which we need both simulations and quantum calculations. But simulations are hard, because these molecules are bulky and slow to move. DFT calculations are also hard, in part because the system is disordered.

To explore the interface, we need atomistic simulations, fast enough to equilibrate mixtures over many microseconds. We achieve this by taking advantage of the stiffness of rings to greatly reduce degrees of freedom, while retaining atomistic structure. Equilibrated donor-acceptor amorphous interfaces are a few nanometers wide, consistent with simulation results for chi using our recently developed pulling technique. Acceptors are somewhat soluble in the donor phase, which may trap excitons and reduce performance.

To describe charges and how they move, we use tight-binding models parameterized by comparison to DFT calculations on monomers and dimers. Free charges are present as polarons, stabilized by electronic polarization of the surrounding material. Polarons move along chains in an energy landscape determined by dihedral disorder; hops between chains are described by Marcus theory. We assemble these ingredients without adjustable parameters to predict polaron mobility in amorphous P3HT, and find promising agreement with experiment.

Publication: 1. Jindal V, Janik MJ, Milner ST (2024) Tight-binding model describes frontier orbitals of non-fullerene acceptors. Molecular Systems Design & Engineering, https://doi.org/10.1039/d3me00195d<br> 2. Jindal V, Aldahdooh MKR, Gomez ED, Janik MJ, Milner ST (2024) Tight-binding model predicts exciton energetics and structure for photovoltaic molecules. Physical Chemistry Chemical Physics, 26(21):15472–15483. https://doi.org/10.1039/d4cp00554f<br> 3. Agarwala P, Donaher S, Ganapathysubramanian B, Gomez ED, Milner ST (2023) Machine Learning Identifies Strong Electronic Contacts in Semiconducting Polymer Melts. Macromolecules, 56(15):5698–5707. https://doi.org/10.1021/acs.macromol.3c00987<br> 4. Agarwala P, Gomez ED, Milner ST (2023) Predicting χ from Concentration Response to Spatially Varying Potentials. Macromolecules, 56(17):6859–6869. https://doi.org/10.1021/acs.macromol.3c00793<br> 5. Agarwala P, Gomez ED, Milner ST (2022) Fast, Faithful Simulations of Donor–Acceptor Interface Morphology. Journal of Chemical Theory and Computation, 18(11):6932–6939. https://doi.org/10.1021/acs.jctc.2c00470<br> 6. Agarwala P, Gomez ED, Milner ST (2024) Crystalline and Amorphous Interface Simulations of Donor–Acceptor Blends. Journal of Chemical Theory and Computation, 20(15):6848–6857. https://doi.org/10.1021/acs.jctc.4c00667

Presenters

  • Scott T Milner

    Pennsylvania State University

Authors

  • Scott T Milner

    Pennsylvania State University