  In silicon, lattice thermal conductivity plays an important role in a wide
range of applications such as thermoelectric and microelectronic devices. Grain
boundaries (GBs) in polycrystalline silicon can significantly reduce lattice
thermal conductivity, but the impact of GB atomic structures on it remains to
be elucidated. This study demonstrates accurate predictions of the GB
structures, GB energies, and GB phonon properties in silicon using machine
learning potentials (MLPs). The results indicate that the MLPs enable robust GB
structure searches owing to the fact that the MLPs were developed from a
training dataset covering a wide variety of structures. We also investigate
lattice thermal conduction at four GB atomic structures using large-scale
perturbed molecular dynamics and phonon wave-packet simulations. The comparison
of these results indicates that the GB structure dependence of thermal
conductivity stems from anharmonic vibrations at GBs rather than from the
phonon transmission behavior at GBs. The advantages of the MLPs compared with a
typical empirical potential of silicon are also thoroughly investigated.
