To address these challenges, Dr. Gershoni-Poranne will combine chemistry and computer science techniques to investigate the relationship between the structure of polycyclic aromatic hydrocarbons and their electronic behavior. Polycyclic aromatic hydrocarbons are pervasive in chemistry and materials science, and are especially important in the field of organic electronics. Dr. Gershoni-Poranne will use high-throughput quantum chemical calculations to construct a database which will supply the data needed for the application of machine learning algorithms and the training of deep generative models. The overarching goal is to design optimal candidates for various organic electronic-based uses — including photovoltaics, field-effect transistors, and light-emitting diodes — as a demonstration of the utility of deep learning within the realm of chemistry, and for inverse design in particular. This will enable resource-efficient molecular discovery, paving the way to more effective and environmentally responsible molecules and materials in the future.
