Machine‐Learning‐Assisted Understanding of Depth‐Dependent Thermal Conductivity in Lithium Niobate Induced by Point Defects Article Swipe
Yunjia Bao
,
Tao Chen
,
Zhuo Miao
,
Weidong Zheng
,
Puqing Jiang
,
Kunfeng Chen
,
Ruiqiang Guo
,
Dongfeng Xue
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/aelm.202400944
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/aelm.202400944
Lithium niobate (LiNbO 3 , LN) has unique electro‐optic and piezoelectric properties, making it widely used in optical devices, telecommunications, sensors, and acoustic systems. Thermal conductivity κ is a critical property influencing the performance and reliability of these applications. Point defects commonly exist in LN and can significantly reduce its κ . However, the effects of point defects on thermal transport in LN remain poorly understood. In this work, LN crystals are prepared through thermal reduction at 600–800 °C, inducing a depth‐dependent distribution of oxygen vacancies (V O ) that increases in concentration with increasing reduction temperature. Time‐domain thermoreflectance and square‐pulsed source measurements reveal a significant suppression and a notable gradient in κ , attributed to the depth‐dependent distribution of V O . A machine learning potential with ab initio accuracy is developed to simulate the impact of typical point defects on thermal transport in LN, demonstrating that V O predominantly suppresses κ by affecting the transport of low‐frequency phonons below 6 THz. Notably, niobium vacancies and antisite defects exhibit similar effects, whereas lithium vacancies show minimal impact. This work highlights the dominant role of V O in modulating κ and provides insights into defect engineering for advanced LN‐based devices and similar ferroelectric crystals.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/aelm.202400944
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/aelm.202400944
- OA Status
- gold
- Cited By
- 3
- References
- 100
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409959332
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4409959332Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/aelm.202400944Digital Object Identifier
- Title
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Machine‐Learning‐Assisted Understanding of Depth‐Dependent Thermal Conductivity in Lithium Niobate Induced by Point DefectsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2025Year of publication
- Publication date
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2025-04-28Full publication date if available
- Authors
-
Yunjia Bao, Tao Chen, Zhuo Miao, Weidong Zheng, Puqing Jiang, Kunfeng Chen, Ruiqiang Guo, Dongfeng XueList of authors in order
- Landing page
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https://doi.org/10.1002/aelm.202400944Publisher landing page
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/aelm.202400944Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/aelm.202400944Direct OA link when available
- Concepts
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Lithium niobate, Materials science, Thermal conductivity, Lithium (medication), Conductivity, Point (geometry), Thermal, Composite material, Optoelectronics, Thermodynamics, Psychology, Physical chemistry, Mathematics, Geometry, Chemistry, Physics, PsychiatryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
- References (count)
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100Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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