Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1498108 | Scripta Materialia | 2016 | 6 Pages |
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.
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Related Topics
Physical Sciences and Engineering
Materials Science
Ceramics and Composites
Authors
Taylor D. Sparks, Michael W. Gaultois, Anton Oliynyk, Jakoah Brgoch, Bryce Meredig,