Geoffrey R. Weal | Searcharxiv

arXiv2026

Delocalisation explains efficient transport and charge generation in neat Y6 organic photovoltaics

Non-fullerene acceptors (NFA), such as Y6, have significantly improved the efficiency of organic photovoltaic devices (OPVs). However, the fundamental processes behind the high efficiencies of NFA devices have remained incompletely understood, with the high efficiencies persisting without the large energetic offsets often thought to be required for charge separation. Even more surprising has been the efficient charge generation in neat Y6 devices, where there is no energetic offset at all. Here, we simulate charge transport and separation in Y6 using delocalised kinetic Monte Carlo (dKMC) parameterised using atomistic calculations, thus taking into account the often-neglected ingredients of delocalisation, disorder, and polaron formation. Including delocalisation predicts higher carrier mobilities and exciton diffusion coefficients than is possible with classical simulations, bringing them into agreement with experimental values. Delocalisation also predicts higher charge-generation efficiencies in neat Y6, in agreement with experimental measurements. Finally, this work establishes dKMC as a realistic, predictive tool for understanding next-generation OPVs.

cond-mat.mtrl-sci↗

arXiv2023

Exciton diffusion in amorphous organic semiconductors: reducing simulation overheads with machine learning

Simulations of exciton and charge hopping in amorphous organic materials involve numerous physical parameters. Each of these parameters must be computed from costly ab initio calculations before the simulation can commence, resulting in a significant computational overhead for studying exciton diffusion, especially in large and complex material datasets. While the idea of using machine learning to quickly predict these parameters has been explored previously, typical machine learning models require long training times which ultimately contribute to simulation overheads. In this paper, we present a new machine learning architecture for building predictive models for intermolecular exciton coupling parameters. Our architecture is designed in such a way that the total training time is reduced compared to ordinary Gaussian process regression or kernel ridge regression models. Based on this architecture, we build a predictive model and use it to estimate the coupling parameters which enter into an exciton hopping simulation in amorphous pentacene. We show that this hopping simulation is able to achieve excellent predictions for exciton diffusion tensor elements and other properties as compared to a simulation using coupling parameters computed entirely from density functional theory. This result, along with the short training times afforded by our architecture, therefore shows how machine learning can be used to reduce the high computational overheads associated with exciton and charge diffusion simulations in amorphous organic materials.

cond-mat.mtrl-sci↗