  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 (NEPs) 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.
