Unlocking Thermoelectric Potential: A Machine Learning Stacking Approach for Half Heusler Alloys

Thermoelectric properties of Half Heusler alloys are predicted by adopting an ensemble modelling approach, specifically the stacking model integrated using Random Forest and XGBoost scheme. Leveraging a diverse dataset encompassing thermal conductivity, the Seebeck coefficient, electrical conductivi...

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Veröffentlicht in:	arXiv.org 2024-08
Hauptverfasser:	Vipin, K E, Padhan, Prahallad
Format:	Artikel
Sprache:	eng
Schlagworte:	Design optimization Electrical resistivity Figure of merit Heusler alloys Machine learning Modelling Performance prediction Prediction models Seebeck effect Strategic materials Thermal conductivity Thermoelectric materials
Online-Zugang:	Volltext
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