Machine Learning Force Field Accelerated Prediction of Low Lattice Thermal Conductivity in Strained WS ₂ Monolayer

Due to its excellent electronic properties, the WS 2 monolayer is promising for electronic and optoelectronic devices. However, its high lattice thermal conductivity (κ L ) limits its application in thermoelectric and thermal insulation. One effective strategy to reduce κ L is the application of biaxial tensile strain. In this work, we perform an efficient approach that integrates on-the-fly machine learning force fields with the Peierls-Boltzmann transport equation to accurately and efficiently evaluate κ L of the WS 2 monolayer. This method reproduces density functional theory results with substantial computational savings. Extending this, we predicted the κ L of the WS 2 monolayer under 2–20% biaxial tensile strain, beyond which dynamical instability arises. The room temperature κ L shows a nonmonotonic decrease with strain, reaching a minimum of 11.18 W/mK at 16%. Thermal transport analysis reveals that reduced phonon velocity and strong phonon–phonon scattering at 16% strain led to such low κ L . The 16% strained WS 2 monolayer is confirmed to be dynamically, thermally, and mechanically stable for practical applicability. The finding suggests the 16% strained WS 2 monolayer as a potential energy-conversion material, offering an efficient approach to explore and engineer thermal transport in 2D materials.