A comprehensive exploration of thermal transport at Cu/diamond interfaces via machine learning potentials

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.