Machine-Learning-Assisted Understanding of Depth-Dependent Thermal Conductivity in Lithium Niobate Induced by Point Defects

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 (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.