arXiv subjects

Justin M. Hodgkiss

Publications and source records attributed to Justin M. Hodgkiss.

5 recordsLinked to original sources

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↗

arXiv2021

Extracting Quantitative Dielectric Properties from Pump-Probe Spectroscopy

Optical pump-probe spectroscopy is a powerful tool for the study of non-equilibrium electronic dynamics and finds wide applications across a range of fields, from physics and chemistry to material science and biology. However, a shortcoming of conventional pump-probe spectroscopy is that photoinduced changes in transmission, reflection and scattering can simultaneously contribute to the measured differential spectra, leading to ambiguities in assigning the origin of spectral signatures and ruling out quantitative interpretation of the spectra. Ideally, these methods would measure the underlying dielectric function (or the complex refractive index) which would then directly provide quantitative information on the transient excited state dynamics free of these ambiguities. Here we present and test a model independent route to transform differential transmission or reflection spectra, measured via conventional optical pump-probe spectroscopy, to changes in the quantitative transient dielectric function. We benchmark this method against changes in the real refractive index measured using time-resolved Frequency Domain Interferometry in prototypical inorganic and organic semiconductor films. Our methodology can be applied to existing and future pump-probe data sets, allowing for an unambiguous and quantitative characterisation of the transient photoexcited spectra of materials. This in turn will accelerate the adoption of pump-probe spectroscopy as a facile and robust materials characterisation and screening tool.

physics.app-ph↗

arXiv2021

Efficient Energy Transport in an Organic Semiconductor Mediated by Transient Exciton Delocalization

Efficient energy transport is highly desirable for organic semiconductor (OSC) devices such as photovoltaics, photodetectors, and photocatalytic systems. However, photo-generated excitons in OSC films mostly occupy highly localized states over their lifetime. Energy transport is hence thought to be mainly mediated by the site-to-site hopping of localized excitons, limiting exciton diffusion coefficients to below ~10^{-2} cm^2/s with corresponding diffusion lengths below ~50 nm. Here, using ultrafast optical microscopy combined with non-adiabatic molecular dynamics simulations, we present evidence for a new highly-efficient energy transport regime: transient exciton delocalization, where energy exchange with vibrational modes allows excitons to temporarily re-access spatially extended states under equilibrium conditions. In films of highly-ordered poly(3-hexylthiophene) nanofibers, prepared using living crystallization-driven self-assembly, we show that this enables exciton diffusion constants up to 1.1+-0.1 cm^2/s and diffusion lengths of 300+-50 nm. Our results reveal the dynamic interplay between localized and delocalized exciton configurations at equilibrium conditions, calling for a re-evaluation of the basic picture of exciton dynamics. This establishes new design rules to engineer efficient energy transport in OSC films, which will enable new devices architectures not based on restrictive bulk heterojunctions.

cond-mat.mtrl-sci↗

arXiv2020

Bandgap Control in Two-Dimensional Semiconductors via Coherent Doping of Plasmonic Hot Electrons

Bandgap control is of central importance for semiconductor technologies. The traditional means of control is to dope the lattice chemically, electrically or optically with charge carriers. Here, we demonstrate for the first time a widely tunable bandgap (renormalisation up to 650 meV at room-temperature) in two-dimensional (2D) semiconductors by coherently doping the lattice with plasmonic hot electrons. In particular, we integrate tungsten-disulfide (WS$_2$) monolayers into a self-assembled plasmonic crystal, which enables coherent coupling between semiconductor excitons and plasmon resonances. Accompanying this process, the plasmon-induced hot electrons can repeatedly fill the WS$_2$ conduction band, leading to population inversion and a significant reconstruction in band structures and exciton relaxations. Our findings provide an innovative and effective measure to engineer optical responses of 2D semiconductors, allowing a great flexiblity in design and optimisation of photonic and optoelectronic devices.

physics.optics↗