Using Machine Learning Techniques to Discover Novel Thermoelectric Materials

Abstract

Thermoelectric materials can be utilized to build devices that convert waste heat to power or vice versa. In the literature, the best-known thermoelectrics, however, are based on rare, costly or even hazardous materials, limiting their general usage. New types of effective thermoelectric materials are thus required to enable worldwide deployment. Although theoretical models of transport characteristics can aid in the creation of novel thermoelectrics, they are currently too computationally costly to be used simply for high-throughput screening of all conceivable candidates in the wide chemical space. Machine learning (ML) has been viewed as a promising technique to aid materials design/discovery because of its quick inference time. In this book chapter, we provide the whole workflow for machine learning applications to the identification of novel thermoelectric materials, predicting electrical and thermal transport properties and optimizing processes for materials and structures using cutting-edge ML methods.