High-throughput screening of electron-phonon interactions and charge transport

The temperature dependence of experimental mobility is commonly used as a predictor of the dominant scattering mechanism in thermoelectric and photovoltaic materials. However, if the scattering mechanism is determined incorrectly, this can lead to wildly inaccurate predictions of materials properties, frustrating efforts to optimise devices. In this work, I use a combination of high-throughput workflows and machine-learned materials properties to generate a dataset of 24,000 mobility calculations. I demonstrate that the temperature-dependence of mobility is not a reliable indicator of the dominant scattering mechanism and instead reveal that many materials long considered to be dominated by deformation-potential scattering are instead controlled by polar optical phonons. This work highlights the potential for data-driven approaches to provide insights for materials discovery and optimisation.