On-the-fly machine learning force fields (MLFFs), trained using ab initio molecular dynamics, offer a fast and accurate alternative to density functional theory (DFT) for predicting lattice thermal conductivity (κL) in two-dimensional materials. This study focuses on MoS2 monolayer, where the MLFF demonstrates excellent agreement with DFT, yielding low root-mean-square errors (RMSEs) of 0.303 meV·atom–1 for energies and 0.013 eV·Å–1 for atomic forces. Leveraging this accuracy and the computational efficiency of MLFFs, we investigated the effects of biaxial tensile strain on κL. Our results show an 80% reduction in κL under 16% strain, which is attributed to suppressed phonon group velocities (vλ), shortened phonon relaxation times (τλ), and enhanced anharmonicity. These mechanisms were understood through detailed analysis of strain-induced changes in phonon dispersions, τλ, and vλ. This study on MoS2 monolayer reveals that MLFFs provide a powerful and efficient framework for calculating κL in low-dimensional systems under strain, significantly advancing our understanding of strain-modulated thermal transport.
Saisurin et al. (Sun,) studied this question.