Molecular dynamics simulations of heat transport using machine-learned potentials: A mini-review and tutorial on GPUMD with neuroevolution potentials

Abstract

Molecular dynamics (MD) simulations play an important role in understanding and engineering heat transport properties of complex materials. An essential requirement for reliably predicting heat transport properties is the use of accurate and efficient interatomic potentials. Recently, machine-learned potentials (MLPs) have shown great promise in providing the required accuracy for a broad range of materials. In this mini-review and tutorial, we delve into the fundamentals of heat transport, explore pertinent MD simulation methods, and survey the applications of MLPs in MD simulations of heat transport. Furthermore, we provide a step-by-step tutorial on developing MLPs for highly efficient and predictive heat transport simulations, utilizing the neuroevolution potentials as implemented in the GPUMD package. Our aim with this mini-review and tutorial is to empower researchers with valuable insights into cutting-edge methodologies that can significantly enhance the accuracy and efficiency of MD simulations for heat transport studies.

Goldene: An Anisotropic Metallic Monolayer with Remarkable Stability and Rigidity and Low Lattice Thermal Conductivity

“…These values are around five- and two-fold lower than the corresponding values of graphene [ 36 ] and the quasi-hexagonal C 60 monolayer [ 42 ], suggesting the low lattice thermal conductivity of the goldene system. To better evaluate this aspect, MLIPs can provide a highly accurate understanding [ 43 , 44 , 45 , 46 , 47 , 48 ]. In Figure 1 f, the MTP-based NEMD predictions for the length effects on the room temperature phononic thermal conductivity of the goldene nanosheet along the x and y directions are plotted.…”