Accelerated Discovery of Thermoelectric Materials Using Machine Learning

Optimized electronic and thermal transport properties are the key requirements for the discovery of efficient thermoelectric materials. Owing to the complex interdependence, simultaneous optimization of these properties is a non-trivial and challenging task, especially if one wants to explore the large available search space of materials. With the advent of statistical high-throughput and machine learning based approaches, several of these challenges for thermoelectrics have been addressed. The goal of this chapter is to highlight these data-assisted efforts towards accelerated development of high-performance thermoelectric materials. We will discuss the contribution of curated databases for high-throughput screening of desired electronic and thermal transport properties. The utilization of these databases will also be described for development of prediction models of transport properties, which has accelerated the discovery of highly efficient thermoelectric materials. Details of commonly used strategies and methods to select a relevant descriptor set for developing the prediction models will be covered. A new approach for selecting descriptors by analyzing the high-throughput property map will be explained. The potential of machine learning methods in relating the unrelated properties will be illustrated by establishing a connection between otherwise independent electronic and thermal transport properties. Further, for designing the highly transferable models for a single target property of interest, we will also cover localized regression based algorithmic development.