Sustainable Thermoelectric Materials Predicted by Machine Learning

Chernyavsky D, Van Den Brink J, Park GH, Nielsch K, Thomas A (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Advanced Theory and Simulations John Wiley && Sons Ltd

Book Volume: 5

Article Number: 2200351

Journal Issue: 11

DOI: 10.1002/adts.202200351

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.

Involved external institutions

Leibniz-Institut für Festkörper- und Werkstoffforschung Dresden e.V. (IFW) / Leibniz Institute for Solid State and Materials Research Dresden DE Germany (DE)

How to cite

APA:

Chernyavsky, D., Van Den Brink, J., Park, G.-H., Nielsch, K., & Thomas, A. (2022). Sustainable Thermoelectric Materials Predicted by Machine Learning. Advanced Theory and Simulations, 5(11). https://doi.org/10.1002/adts.202200351

MLA:

Chernyavsky, Dmitry, et al. "Sustainable Thermoelectric Materials Predicted by Machine Learning." Advanced Theory and Simulations 5.11 (2022).

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