In this article we provide an overview of data mining, informatics, and machine learning approaches for thermoelectrics. We describe how the initial development of a thermoelectric materials database has enabled the creation of a recommendation engine governed by machine learning and how this engine introduces a new paradigm in thermoelectric materials development. Performance probability is generated based on training models. A demonstration of the data mining approach is set forth in a ternary intermetallic system, where we report new materials.
Sparks, Taylor D., Michael W. Gaultois, Anton Oliynyk, Jakoah Brgoch, and Bryce Meredig. “Data Mining Our Way to the next Generation of Thermoelectrics.” Scripta Materialia 111 (2016): 10–15. https://doi.org/10.1016/j.scriptamat.2015.04.026.