Sustainable Thermoelectric Materials Predicted by Machine Learning
Abstract: Using datasets from several sources, a list of more than 450 materials is generated and related them with their thermoelectric properties. This is obtained by generating a set of features using only the molecular formula. Subsequently, a machine learning algorithm classifies the materials in specific, binary classes, for example, possessing high or low Seebeck coefficients or electrical conductivity. After adjusting the threshold values and grouping the materials into clusters, the thermoelectric performance of more than 25k materials is predicted. Finally, the results are filtered to obtain only the sustainable materials, that is, neither toxic nor critical, (ideally) inexpensive, and isotropic with regard to their transport properties to simplify the preparation procedure.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Sustainable Thermoelectric Materials Predicted by Machine Learning ; day:03 ; month:10 ; year:2022 ; extent:8
Advanced theory and simulations ; (03.10.2022) (gesamt 8)
- Urheber
- DOI
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10.1002/adts.202200351
- URN
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urn:nbn:de:101:1-2022100415113633602794
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:21 MESZ
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Beteiligte
- Chernyavsky, Dmitry
- van den Brink, Jeroen
- Park, Gyu‐Hyeon
- Nielsch, Kornelius
- Thomas, Andy
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