Unveiling the impact of intrinsic defects on thermal conductivity in CuInTe ₂ using neural network potential

Wenjin LI; Yongwei Tang; Qingbo Liu; Ziyang Yu; Shiheng Liang; Zhihong Lu; Rui Xiong; Pan Zhang

doi:10.1039/d5cp03045e

January 1, 2025Physical Chemistry Chemical Physics

Unveiling the impact of intrinsic defects on thermal conductivity in CuInTe ₂ using neural network potential

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Key Points

Thermal conductivity is affected by intrinsic defects in CuInTe2, with opposing influences from specific defects.
V Cu decreases thermal conductivity, while Cu In enhances it, leading to counterintuitive thermal transport trends.
The impact of intrinsic defects was modeled using a neural network potential, allowing for precise predictions.
Understanding these mechanisms may enable better thermal management in semiconductor applications.

Implication

Observational analysis reveals intrinsic defects impact thermal conductivity in CuInTe2, suggesting complex thermal transport dynamics.

Abstract

Counterintuitively, intrinsic point defects in CuInTe 2 exert opposing effects on thermal transport: V Cu acts to decrease the κ L , whereas Cu In contributes to its enhancement.

Discussion

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Cite This Study

LI et al. (2025) studied this question.

synapsesocial.com/papers/6925572cc0ce034ddc35a8ca https://doi.org/10.1039/d5cp03045e

View at Publisher

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Unveiling the impact of intrinsic defects on thermal conductivity in CuInTe ₂ using neural network potential

Key Points

Implication

Abstract

Discussion

Cite This Study

Unveiling the impact of intrinsic defects on thermal conductivity in CuInTe ₂ using neural network potential

Key Points

Implication

Abstract

Discussion

Cite This Study

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