Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials

We curate experimental datasets and use them to train ML models for accelerating the identification of transparent conductors, demonstrating how ML can reveal candidates that may have been previously overlooked as viable transparent conductors.