Unlocking Thermoelectric Potential: A Machine Learning Stacking Approach for Half Heusler Alloys
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910550634528768 |
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| author | E, Vipin K. Padhan, Prahallad |
| author_facet | E, Vipin K. Padhan, Prahallad |
| contents | 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 conductivity, and the figure of merit (ZT), the study demonstrates superior predictive performance of the stacking Model, outperforming individual base models with high R2 values. Key features such as temperature, mean Covalent Radius, and average deviation of the Gibbs energy per atom emerge as critical influencers, highlighting their pivotal roles in optimizing thermoelectric behavior. The unification of Random Forest and XGBoost in the stacking model effectively captures nuanced relationships, offering a holistic understanding of thermoelectric performance in Half Heusler alloys. This work advances predictive modelling in thermoelectricity and provides valuable insights for strategic material design, paving the way for enhanced efficiency and performance in thermoelectric applications. The ensemble modelling framework, coupled with insightful feature selection and meticulous engineering, establishes a robust foundation for future research in pursuing high-performance thermoelectric materials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00466 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unlocking Thermoelectric Potential: A Machine Learning Stacking Approach for Half Heusler Alloys E, Vipin K. Padhan, Prahallad Materials Science Computational Physics 00:General J.2; I.2.0 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 conductivity, and the figure of merit (ZT), the study demonstrates superior predictive performance of the stacking Model, outperforming individual base models with high R2 values. Key features such as temperature, mean Covalent Radius, and average deviation of the Gibbs energy per atom emerge as critical influencers, highlighting their pivotal roles in optimizing thermoelectric behavior. The unification of Random Forest and XGBoost in the stacking model effectively captures nuanced relationships, offering a holistic understanding of thermoelectric performance in Half Heusler alloys. This work advances predictive modelling in thermoelectricity and provides valuable insights for strategic material design, paving the way for enhanced efficiency and performance in thermoelectric applications. The ensemble modelling framework, coupled with insightful feature selection and meticulous engineering, establishes a robust foundation for future research in pursuing high-performance thermoelectric materials. |
| title | Unlocking Thermoelectric Potential: A Machine Learning Stacking Approach for Half Heusler Alloys |
| topic | Materials Science Computational Physics 00:General J.2; I.2.0 |
| url | https://arxiv.org/abs/2408.00466 |