Using High-Throughput Calculations and Machine Learning to Understand Electronic Transport in Semiconductors

The temperature dependence of experimental mobility is commonly used as a predictor of the dominant scattering mechanism in thermoelectric materials. 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. Based on this dataset, I demonstrate that the temperature-dependence of mobility is not a reliable indicator of the dominant scattering mechanism. Instead, I 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.