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, which has the potential for applications in the field of high-frequency, radiation-hardened, and high-power devices. There are several important polytypes of diamond, such as cubic diamond, lonsdaleite and nanotwinned diamond (NTD). The thermal conductivity of semiconductor should be calculated at different temperatures in high-power devices. However, there has been no potential reported for cubic diamond and its polytypes combining efficiency and accuracy based on molecular dynamics (MD) to predict thermal conductivity. Here, an interatomic potential utilizing neural networks could offer distinct advantages, such as reduced computational time compared to density functional theory (DFT), while maintaining high accuracy in predicting the thermal conductivity of the 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, 1557.2, and 985.6 W·m -1·K -1, which are higher than the calculation results based on Tersoff-1989 potential (1508, 1178, and 794 W·m -1·K -1, respectively). The thermal conductivities of cubic diamond and lonsdaleite using the NEP are closer to the experimental data or DFT data compared with Tersoff-potential. The molecular dynamics simulations are performed using NEP to calculate the phonon dispersions to illustrate the possible reasons for discrepancies among the cubic diamond lonsdaleite and NTD. In this work, we have proposed 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.

Insight into Interfacial Heat Transfer of β-Ga2O3/Diamond Heterostructures via the Machine Learning Potential

“…In this work, cell structures were visualized using VESTA software . The model in this study was more than 130% larger than that in similar previous work. ,, Furthermore, convergence tests on the model size were conducted, and detailed information can be found in the Supporting Information. As a result, it could be shown that the size convergence was very good for the diamond and the β-Ga 2 O 3 model.…”

“…β-Ga 2 O 3 is regarded as a potential candidate for next-generation power devices due to the wide band gap of ∼4.9 eV and high breakdown electric field of 8 MV·cm –1 . − The major barrier to the realization of high-power β-Ga 2 O 3 devices lies in the low thermal conductivity of ∼10 to 29 W·m –1 ·K –1 , , which usually results in heat dissipation issues. An excellent solution is the integration of the high thermal conductivity materials with the low ones to enhance heat dissipation. − Diamond is a well-recognized promising candidate (>2000 W·m –1 ·K –1 ) − for integration with β-Ga 2 O 3 . For example, Kim et al adhered n-type β-Ga 2 O 3 to p-type diamond through van der Waals interactions and achieved a p–n junction photodiode due to the most important factor that diamond nearly boasts the greatest heat dissipation properties compared with other materials .…”