The computation of thermal conductivity for finite nanoparticulate systems, particularly those of irregular shapes, poses significant challenges. Much of the previous work on the thermal conductivity of nanoparticles has been carried out by applying traditional nonequilibrium molecular dynamics (NEMD) methods. One of our recent works [Physical Review B 103, 035417 (2021)] proposed that equilibrium molecular dynamics (EMD) methods can be used for the simulation of thermal conductivity of finite-scale systems and demonstrated their equivalence to NEMD methods. In this work, we explored the application of the EMD approach to calculating the thermal conductivity of zero-dimensional nanoparticles. In our initial step, we merged both methodologies to substantiate the equivalence in thermal conductivity calculation for cube and cylinder nanoparticles. Post data filtration, the usefulness of EMD for thermal conductivity evaluation of zero-dimensional materials was confirmed. The NEMD method struggles with accuracy in nanoparticle systems characterized by a cross-sectional area change in the direction of transport, whereas in the case where the volume can be determined, EMD methods can be used to calculate the thermal conductivity. In a subsequent study, we used the latest machine learning potential (NEP) to calculate the thermal conductivity of spherical nanoparticles and evaluated the results against the classical Tersoff potential.Ultimately, predictions were made for the thermal conductivity of nanoparticles with various unique shapes in all directions. Collectively, our findings underscore the simplicity and effectiveness of using EMD methods for thermal conductivity calculations in the context of nanoparticles. This paves the way for novel strategies in studying the thermal transport properties of finite nanoparticle systems as well as nanopowders.
Keywords: equilibrium molecular dynamic; nanoparticle; thermal conductivity.
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