[19p-Z32-1~15] 23.1 Joint Session N "Informatics"

[19p-Z32-3] Prediction of Thermal Conductivity of CNT by Neural Network MD

〇Kazuki Mori¹, Nobuhiko Kato¹, Masahiro Saito¹, Jun Koyanahi² (1.ITOCHU Techno-Solutions Corp., 2.TUS)

Keywords:neural network MD, Carbon nanotubes, thermal conductivity

Carbon nanotubes (CNT) are expected in future industries as new materials for nanotechnology. In this study, we propose a new method for evaluating the thermal conductivity of CNT by theoretical calculation. So far, classical molecular dynamics has been mainly used. However, it is difficult to sufficiently evaluate the high thermal conductivity of CNT with the conventional classical force field. On the other hand, high-precision calculations using first-principles calculations can only handle small-scale models, so calculation costs are high when trying to calculate a wide variety of CNT structures. Therefore, we evaluated the lattice thermal conductivity of single-walled carbon nanotubes (SWCNT) using a neural network MD (NNMD), which is faster than first-principles calculations and more accurate than existing molecular dynamics calculations. As a result, that was good agreement, and that calculation time was shortened.