Machine‐Learning‐Assisted Understanding of Depth‐Dependent Thermal Conductivity in Lithium Niobate Induced by Point Defects
Abstract Lithium niobate (LiNbO3, 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. P...
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| Format: | Article |
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Wiley-VCH
2025-07-01
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| Series: | Advanced Electronic Materials |
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| Online Access: | https://doi.org/10.1002/aelm.202400944 |
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| author | Yunjia Bao Tao Chen Zhuo Miao Weidong Zheng Puqing Jiang Kunfeng Chen Ruiqiang Guo Dongfeng Xue |
| author_facet | Yunjia Bao Tao Chen Zhuo Miao Weidong Zheng Puqing Jiang Kunfeng Chen Ruiqiang Guo Dongfeng Xue |
| author_sort | Yunjia Bao |
| collection | DOAJ |
| description | Abstract Lithium niobate (LiNbO3, 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 (VO) 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 VO. 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 VO 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 VO in modulating κ and provides insights into defect engineering for advanced LN‐based devices and similar ferroelectric crystals. |
| format | Article |
| id | doaj-art-90af256c41154d24a693fd5b535e1beb |
| institution | DOAJ |
| issn | 2199-160X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Advanced Electronic Materials |
| spelling | doaj-art-90af256c41154d24a693fd5b535e1beb2025-08-20T03:12:05ZengWiley-VCHAdvanced Electronic Materials2199-160X2025-07-011111n/an/a10.1002/aelm.202400944Machine‐Learning‐Assisted Understanding of Depth‐Dependent Thermal Conductivity in Lithium Niobate Induced by Point DefectsYunjia Bao0Tao Chen1Zhuo Miao2Weidong Zheng3Puqing Jiang4Kunfeng Chen5Ruiqiang Guo6Dongfeng Xue7Institute of Novel Semiconductors State Key Laboratory of Crystal Materials Shandong University Jinan 250100 ChinaSchool of Energy and Power Engineering Huazhong University of Science and Technology Wuhan Hubei 430074 ChinaThermal Science Research Center Shandong Institute of Advanced Technology Jinan Shandong 250103 ChinaThermal Science Research Center Shandong Institute of Advanced Technology Jinan Shandong 250103 ChinaSchool of Energy and Power Engineering Huazhong University of Science and Technology Wuhan Hubei 430074 ChinaInstitute of Novel Semiconductors State Key Laboratory of Crystal Materials Shandong University Jinan 250100 ChinaThermal Science Research Center Shandong Institute of Advanced Technology Jinan Shandong 250103 ChinaShenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen 518110 ChinaAbstract Lithium niobate (LiNbO3, 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 (VO) 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 VO. 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 VO 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 VO in modulating κ and provides insights into defect engineering for advanced LN‐based devices and similar ferroelectric crystals.https://doi.org/10.1002/aelm.202400944lithium niobatemachine learningpoint defectspump‐probe thermoreflectancethermal conductivity gradient |
| spellingShingle | Yunjia Bao Tao Chen Zhuo Miao Weidong Zheng Puqing Jiang Kunfeng Chen Ruiqiang Guo Dongfeng Xue Machine‐Learning‐Assisted Understanding of Depth‐Dependent Thermal Conductivity in Lithium Niobate Induced by Point Defects Advanced Electronic Materials lithium niobate machine learning point defects pump‐probe thermoreflectance thermal conductivity gradient |
| title | Machine‐Learning‐Assisted Understanding of Depth‐Dependent Thermal Conductivity in Lithium Niobate Induced by Point Defects |
| title_full | Machine‐Learning‐Assisted Understanding of Depth‐Dependent Thermal Conductivity in Lithium Niobate Induced by Point Defects |
| title_fullStr | Machine‐Learning‐Assisted Understanding of Depth‐Dependent Thermal Conductivity in Lithium Niobate Induced by Point Defects |
| title_full_unstemmed | Machine‐Learning‐Assisted Understanding of Depth‐Dependent Thermal Conductivity in Lithium Niobate Induced by Point Defects |
| title_short | Machine‐Learning‐Assisted Understanding of Depth‐Dependent Thermal Conductivity in Lithium Niobate Induced by Point Defects |
| title_sort | machine learning assisted understanding of depth dependent thermal conductivity in lithium niobate induced by point defects |
| topic | lithium niobate machine learning point defects pump‐probe thermoreflectance thermal conductivity gradient |
| url | https://doi.org/10.1002/aelm.202400944 |
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