A comprehensive exploration of thermal transport at Cu/diamond interfaces via machine learning potentials
Zhanpeng Sun, Hutao Shi, Yi Zhu, Rui Li, Xiang Sun, Qijun Wang, Zijun Qi, Lijie Li, Sheng Liu, Wei Shen, Gai Wu
Abstract
The fundamental thermal limitation of pure copper impedes progress in high-power devices, which is becoming more critical with advances in power electronics. The Cu/diamond composite becomes a promising candidate for thermal management due to its excellent theoretical thermal conductivity and customizable coefficient of thermal expansion (CTE). Actually, the thermal conductivity of Cu/diamond composite is much lower than its theoretical value, for which a key bottleneck is interfacial thermal transport at the Cu/diamond interface. However, many atomic-level microscopic mechanisms of heat transport at Cu/diamond interfaces remain poorly understood at present. Especially when different interlayer materials are involved, theoretical studies become extremely complex and challenging. In this work, a machine learning potential for comprehensive simulations of thermal transport at Cu/diamond interfaces has been successfully constructed. The effects of key factors, such as interlayer material, temperature, strain, and crystal orientation, on heat transport at Cu/diamond interfaces have been studied. Furthermore, the underlying mechanisms are thoroughly analyzed and discussed. Finally, the insightful strategies are proposed to optimize and enhance the thermal properties of Cu/diamond interfaces. These advancements can lay a foundation and pave the way for further investigations into interfacial thermal transport at Cu/diamond interfaces as well as in other structures containing interlayer materials.