Data mining our way to the next generation of thermoelectrics. (15th January 2016)

• Record Type: Journal Article
• Authors: Sparks, Taylor D.; Gaultois, Michael W.; Oliynyk, Anton; Brgoch, Jakoah; Meredig, Bryce
• Is Part Of: Scripta materialia. Volume 111(2016)
• Page Start: 10
• Page End: 15
• Publication Date: 2016-01-15
• Subjects: Materials discovery -- Thermoelectric materials -- Rapid screening -- Data mining -- Machine learning; Materials -- Periodicals; Metallurgy -- Periodicals; Metalen; Legeringen; Materiaalkunde; Metals, metalworking and machinery industries; Metals; Electronic journals; 620.11
• DOI: 10.1016/j.scriptamat.2015.04.026
• Languages: English
• ISSNs: 1359-6462

Abstract

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.