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In silicon, lattice thermal conductivity plays an important role in a widerange of applications such as thermoelectric and microelectronic devices. Grainboundaries (GBs) in polycrystalline silicon can significantly reduce latticethermal conductivity, but the impact of GB atomic structures on it remains tobe elucidated. This study demonstrates accurate predictions of the GBstructures, GB energies, and GB phonon properties in silicon using machinelearning potentials (MLPs). The results indicate that the MLPs enable robust GBstructure searches owing to the fact that the MLPs were developed from atraining dataset covering a wide variety of structures. We also investigatelattice thermal conduction at four GB atomic structures using large-scaleperturbed molecular dynamics and phonon wave-packet simulations. The comparisonof these results indicates that the GB structure dependence of thermalconductivity stems from anharmonic vibrations at GBs rather than from thephonon transmission behavior at GBs. The advantages of the MLPs compared with atypical empirical potential of silicon are also thoroughly investigated.

Structure and lattice thermal conductivity of grain boundaries in silicon by using machine learning potential and molecular dynamics