Molecular dynamics study of thermal conductivities of cubic diamond, lonsdaleite, and nanotwinned diamond via machine-learned potential

Abstract

Diamond is a wide-bandgap semiconductor with a variety of crystal configurations, and has the potential applications in the field of high-frequency, radiation-hardened, and high-power devices. There are several important polytypes of diamonds, such as cubic diamond, lonsdaleite, and nanotwinned diamond (NTD). The thermal conductivities of semiconductors in high-power devices at different temperatures should be calculated. However, there has been no reports about thermal conductivities of cubic diamond and its polytypes both efficiently and accurately based on molecular dynamics (MD). Here, using interatomic potential of neural networks can provide obvious advantages. For example, comparing with the use of density functional theory (DFT), the calculation time is reduced, while maintaining high accuracy in predicting the thermal conductivities of the above-mentioned three diamond polytypes. Based on the neuroevolution potential (NEP), the thermal conductivities of cubic diamond, lonsdaleite, and NTD at 300 K are respectively 2507.3 W⋅m−1⋅K−1, 1557.2 W⋅m−1⋅K−1, and 985.6 W⋅m−1⋅K−1, which are higher than the calculation results based on Tersoff-1989 potential (1508 W⋅m−1⋅K−1, 1178 W⋅m−1⋅K−1, and 794 W⋅m−1⋅K−1, respectively). The thermal conductivities of cubic diamond and lonsdaleite, obtained by using the NEP, are closer to the experimental data or DFT data than those from Tersoff-potential. The molecular dynamics simulations are performed by using NEP to calculate the phonon dispersions, in order to explain the possible reasons for discrepancies among the cubic diamond, lonsdaleite, and NTD. In this work, we propose a scheme to predict the thermal conductivity of cubic diamond, lonsdaleite, and NTD precisely and efficiently, and explain the differences in thermal conductivity among cubic diamond, lonsdaleite, and NTD.