Charge Transport

Charge transport studies aim to understand how electrical charge moves through materials, a crucial aspect for developing advanced electronic devices. Current research focuses on improving the accuracy and efficiency of charge transport modeling, employing techniques like kinetic Monte Carlo simulations for nanoparticle networks, and machine learning algorithms (including deep learning and Bayesian approaches) for analyzing experimental data and predicting material properties, such as HOMO/LUMO energies in organic semiconductors. These advancements are vital for optimizing device performance in diverse applications, from high-power electronics to neuromorphic computing and organic photovoltaics, by enabling faster and more accurate characterization and design.