Data mining our way to the next generation of thermoelectrics. (15th January 2016) - Sparks, Taylor D.; Gaultois, Michael W.; Oliynyk, Anton; Brgoch, Jakoah; Meredig, Bryce
Graphical abstract: Abstract: 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.