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.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Sustainable Thermoelectric Materials Predicted by Machine Learning ; day:03 ; month:10 ; year:2022 ; extent:8 ; Advanced theory and simulations ; (03.10.2022) (gesamt 8)

Contributors, Places and Time

• Creator: Chernyavsky, Dmitry ; van den Brink, Jeroen ; Park, Gyu‐Hyeon ; Nielsch, Kornelius ; Thomas, Andy

Further information

• DOI: 10.1002/adts.202200351
• URN: urn:nbn:de:101:1-2022100415113633602794
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:21 AM CEST