Insight into the temperature-dependent lattice thermal properties of CeO 2 stabilized ZrO 2 by machine learning force fields

superlattice system containing 20 736 atoms were investigated using the neuroevolution potential (NEP) framework within molecular dynamics simulations. Significant first-order and second-order phase transitions were observed during heating. The homogeneous non-equilibrium molecular dynamics (HNEMD) method was applied to examine variations in thermal conductivity due to lattice size effects, while NEP-based MD simulations were used to predict the lattice thermal conductivity at different doping concentrations. At room temperature, the system exhibits relatively low thermal conductivity. To understand the underlying mechanisms of these thermal transport properties, spectral decomposition analysis was further conducted.