Impact of high-order anharmonicity and nitrogen vacancy defects on the thermal conductivity of
Phys. Rev. B 112, 064309 – Published 18 August, 2025
DOI: https://doi.org/10.1103/gmh7-gsk8
Abstract
Accurately determining the thermal transport properties of the two-dimensional material is essential for its application in micro- and nanoscale semiconductor devices. To date, substantial discrepancy persists between theoretical prediction and experimental measurement of the thermal conductivity of , with the underlying physical mechanisms remaining unclear. In this work, the intrinsic of is accurately determined by using the neuroevolutionary potential combined with the phonon Boltzmann transport equation (BTE). Taking the temperature-dependent interatomic force constants extracted from effective harmonic model as the inputs, the room-temperature of calculated by the BTE method (approximately ) exhibits remarkable consistency with results obtained from a homogeneous nonequilibrium molecular-dynamics method (approximately ). These results underscore the critical influence of higher-order phonon scattering and temperature renormalization of force constants on the intrinsic of . Our calculations also demonstrate that the thermal transport performance of could be evidently suppressed by nitrogen vacancy. As for the sample with 0.02% randomly distributed N vacancy, the could be decreased to approximately , which provides a plausible explanation for the discrepancy between theoretical predictions and experimental measurements (approximately ). Interestingly, compared to the internal N vacancy, the surface N vacancy exhibits particularly pronounced effect in suppressing the of . Such a phenomenon is primarily attributed to significant modifications of the symmetry of the charge density distribution and the geometric environment induced by the surface N vacancy, which weaken Si-N bond interactions and amplify the vibrational motion of Si atoms, thereby enhancing the phonon anharmonicity. This work offers valuable insights into the thermal transport properties of the family and serves as a case study for exemplifying the use of machine-learning interatomic potentials to investigate the impact of low-concentration disordered vacancy defects on the thermal conductivity of materials.