Data-Driven Review of Thermoelectric Materials: Performance and Resource Considerations

By M. Gaultois, Taylor D. Sparks, Christopher K. H. Borg, R. Seshadri, W. D. Bonificio, David R. Clarke. 2013

444 citations recorded by the source corpus.

In this review, we describe the creation of a large database of thermoelectric materials prepared by abstracting information from over 100 publications. The database has over 18 000 data points from multiple classes of compounds, whose relevant properties have been measured at several temperatures. Appropriate visualization of the data immediately allows certain insights to be gained with regard to the property space of plausible thermoelectric materials. Of particular note is that any candidate material needs to display an electrical resistivity value that is close to 1 mΩ cm at 300 K, that is, samples should be significantly more conductive than the Mott minimum metallic conductivity. The Herfindahl–Hirschman index, a commonly accepted measure of market concentration, has been calculated from geological data (known elemental reserves) and geopolitical data (elemental production) for much of the periodic table. The visualization strategy employed here allows rapid sorting of thermoelectric compositions w...

Subfields: Materials Chemistry, Polymers and Plastics

View Paper: https://www.semanticscholar.org/paper/7bfb244fea84bb08ad8824e5f9a2d9428574d04c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. Ryan Jacobs
2. Dane Morgan
3. Wei Li

Citations in the corpus

• Machine-learning-assisted materials discovery using failed experiments (2016, background): “…target candidates for synthetic efforts 11,15 ; determination of the structure–property relationship from large bodies of experimental data 16,17 , enabled by integration with high-throughput synthesis and measurement tools 18 ; and clustering on the basis of similar crystallographic…”
• Predicting the thermodynamic stability of perovskite oxides using machine learning models (2018, background): “Herfindahl−Hirschman Index (HHI)[40] of elements, which are hard to explain in terms of fundamental physics and chemistry.”