Advancements in Tuning Thermoelectric Properties: Insights from Hybrid Functional Studies, Strain Engineering, and Machine Learning Models

Vipin K E; Prahallad Padhan

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arXiv · 2407.19241

Advancements in Tuning Thermoelectric Properties: Insights from Hybrid Functional Studies, Strain Engineering, and Machine Learning Models

Vipin K E·Prahallad Padhan

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Abstract

Thermoelectric properties in topological insulator Bi2Se3 are explored with multifaceted strategies, i.e., hybrid functional with strain and artificial intelligence methodology. The assessment with the experimental band gap values recognises the limitations of conventional functional and the effectiveness of screened hybrid functionals. A thorough investigation into the impact of biaxial and uniaxial strain on thermoelectric parameters uncovers distinctive behaviours in n-type and p-type Bi2Se3 , providing insights into optimal strain conditions for improved performance. Furthermore, the studies on the role of topologically non-trivial surface states (TNSS) in thermoelectric properties reveal that TNSS significantly dominate electronic transport. Dual scattering time approximation elucidates the segregation of thermoelectric transport contributions from bulk and surface states, highlighting the importance of controlling the relaxation time ratio for enhanced thermoelectric performance. Additionally, the prediction of thermoelectric properties using Random Forest and Neural Networks models showcase impressive agreement with density functional theory predictions across varying temperatures, offering a powerful tool for understanding complex temperature-dependent trends in thermoelectric properties. In summary, this interdisciplinary study presents a unique approach to advancing the understanding and optimization of thermoelectric properties in Bi2Se3 . It provides a comprehensive framework for tailoring material behaviour for diverse thermoelectric applications.

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Vipin K E, Prahallad Padhan. 2024-07-27. Advancements in Tuning Thermoelectric Properties: Insights from Hybrid Functional Studies, Strain Engineering, and Machine Learning Models. https://arxiv.org/abs/2407.19241

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