The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Data-Driven Review of Thermoelectric Materials: Performance and Resource Considerations
Open accessAbstract
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 with respect to important issues of elemental scarcity and supply risk.
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Cites
The high-throughput highway to computational materials design
Curtarolo, S., Hart, G. L. W., Nardelli, M. B., Mingo, N., Sanvito, S., & Levy, O. (2013) · Nature Materials · doi:10.1038/nmat3568 · Cited by 2,053
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Materials Synthesis Insights from Scientific Literature via Text Extraction and Machine Learning
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A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction
Jensen, Z., Kim, E., Kwon, S., Gani, T. Z. H., Román-Leshkov, Y., Moliner, M., Corma, A., & Olivetti, E. (2019) · ACS Central Science · doi:10.1021/acscentsci.9b00193 · Cited by 282
Virtual screening of inorganic materials synthesis parameters with deep learning
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Opportunities and challenges of text mining in materials research
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A review on machine learning-guided design of energy materials
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Metadata
- Authors
- Gaultois, M. W., Sparks, T. D., Borg, C. K. H., Seshadri, R., Bonificio, W. D., & Clarke, D. R.
- Venue
- Chemistry of Materials
- Publisher
- American Chemical Society (ACS)
- Published
- 2013-05-21
- Volume
- 25
- Issue
- 15
- Pages
- 2911-2920
- ISSN
- 0897-4756 (ISSN-L)
- 1520-5002
- OpenAlex
- W2065832194
- Cited by
- 500
- References
- 1
- FWCI
- 13.17
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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