  We demonstrate that a high-dimensional neural network potential (HDNNP) can
predict the lattice thermal conductivity of semiconducting materials with an
accuracy comparable to that of density functional theory (DFT) calculation.
After a training procedure based on the force, the root mean square error
between the forces predicted by the HDNNP and DFT is less than 40 meV/{\AA}. As
typical examples, we present the results for Si and GaN bulk crystals. The
deviation from the thermal conductivity calculated using DFT is within 1% at
200 to 500 K for Si and within 5.4% at 200 to 1000 K for GaN.
