  First-principles techniques for electronic transport property prediction have
seen rapid progress in recent years. However, it remains a challenge to model
heterostructures incorporating variability due to fabrication processes.
Machine-learning (ML)-based materials informatics approaches (MI) are
increasingly used to accelerate design and discovery of new materials with
targeted properties, and extend the applicability of first-principles
techniques to larger systems. However, few studies exploited MI to learn
electronic structure properties and use the knowledge to predict the respective
transport coefficients. In this work, we propose an
electronic-transport-informatics (ETI) framework that trains on ab initio
models of small systems and predicts thermopower of silicon/germanium
heterostructures beyond the length-scale accessible with first-principles
techniques, matching measured data. We demonstrate application of MI to extract
important physics that determines electronic transport in semiconductor
heterostructures, breaking from combinatorial strategies pursued especially for
thermoelectric materials. We anticipate that ETI would have broad applicability
to diverse materials classes.
