Enterprise AI Analysis
A comprehensive exploration of thermal transport at Cu/diamond interfaces via machine learning potentials
By Zhanpeng Sun1,2,7, Hutao Shi1,2,7, Yilong Zhu1,2, Rui Li1,2,3,4,5, Xiang Sun1,2,3,4,5, Qijun Wang1,2,3,4,5, Zijun Qi1,2, Lijie Li, Sheng Liu1,2,3,5, Wei Shen1,2,3,4,5 & Gai Wu1,2,3,4,5
Published: 25 November 2025
Executive 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.
Key Performance Metrics & Potential Impact
Leveraging advanced Machine Learning Potentials, this research unveils critical insights into thermal transport at Cu/diamond interfaces, enabling significant improvements for high-power electronics.
0%
TBR Reduction (with Ti interlayer)
0%
Strain-Induced TBR Change
0%
Low-Frequency Phonon Contribution to ITC
Deep Analysis & Enterprise Applications
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Executive Summary and Machine Learning Potential Validation
This research successfully constructs a machine learning potential (NEP) for comprehensive simulations of thermal transport at Cu/diamond interfaces. The potential demonstrates high accuracy in predicting energy, force, and virial values, aligning closely with DFT calculations. This robust computational tool enables a deeper exploration into critical factors influencing thermal interface conductance.
0.99
R² Value (Energy Prediction Accuracy)
The Neuroevolution Potential (NEP) achieved an R² value of 0.99 for energy prediction, demonstrating strong consistency with DFT calculations for Cu/diamond systems.
Optimizing Interfacial Thermal Transport with Interlayers
The study reveals that the choice of interlayer material critically influences thermal transport at Cu/diamond interfaces. Materials that shorten the "phonon bridge" — the length of the inefficient phonon channel hindering heat transfer — significantly reduce Thermal Boundary Resistance (TBR). Conversely, materials that lengthen it, like Tungsten (W), impede heat flow. Understanding the phonon channel matching between interlayer and parent materials is key.
| Interlayer | TBR (m²·K·GW⁻¹) | Effect on Phonon Bridge | Mechanism |
|---|---|---|---|
| None | 10.5 | Long (26.7 THz) | Weak chemical affinity, large vibrational mismatch. |
| Ti | 6.1 (42% reduction) | Shortened | Good match of efficient phonon channels with Cu. |
| Cr | 6.7 (36% reduction) | Shortened | Extremely short phonon bridge, promoting transport. |
| Mo | 7.6 (28% reduction) | Shortened | Reduced phonon bridge length. |
| TiC | 6.4 (39% reduction) | Shortened, two channels | Two efficient phonon channels connected by abundant inefficient ones. |
| W | 17.4 (66% increase) | Lengthened | Significantly hinders thermal transport. |
| WC | 10.9 (Minimal change) | Similar length | Two efficient channels, but poor connection to Cu's efficient channel. |
Enterprise Process Flow: Phonon Heat Transfer
Efficient Phonon Channel (Source)
→
Encounter Phonon Wall
→
Utilize Inefficient Phonon Channel (Bridge)
→
Connect to Next Efficient Channel
→
Heat Transfer Continues (Sink)
Manipulating Thermal Properties with Uniaxial Strain
Strain engineering is identified as a potent method to adjust thermal transport at Cu/diamond interfaces. Compressive strain decreases TBR by enhancing phonon density of states (PDOS) overlap and strengthening interfacial bonding. Conversely, tensile strain increases TBR by reducing overlap and weakening bonds. The significant difference in hardness between Cu and diamond causes differential strain distribution, amplifying these effects.
114.5%
Max TBR Change (±3% Uniaxial Strain)
Applying a uniaxial strain of ±3% can induce a remarkable 114.5% change in the Thermal Boundary Resistance, offering a powerful lever for tuning thermal properties.
Mechanism Breakdown: Under uniaxial tensile strain, the PDOS of Cu and diamond both shift towards the low-frequency region, reducing overlap and decreasing interfacial bond strength. For uniaxial compressive strain, the PDOS shifts towards the high-frequency region, increasing overlap and bond strength. The differing hardness of Cu and diamond exacerbates these shifts, making strain a highly effective thermal modulator.
Crystal Orientation's Influence on Phonon Dynamics
Despite Cu and diamond lacking significant thermal conductivity anisotropy, crystal orientation profoundly affects interfacial thermal transport. Variations in TBR across different orientations are primarily attributed to inelastic phonon scattering and differences in elastic phonon heat transfer within diamond, rather than intrinsic PDOS properties.
| Cu/Diamond Interface | TBR (m²·K·GW⁻¹) | LFPM Contribution to ITC | I-PDOS Overlap (Elastic Transfer) |
|---|---|---|---|
| Cu(100)/Dia(100) | 14.6 | 65% | 82.85% |
| Cu(100)/Dia(110) | 15.0 | 66% | 81.80% |
| Cu(100)/Dia(111) | 15.5 | 69% | 78.93% |
Key Insights: The varying TBR values across orientations highlight the complex interplay of phonon scattering mechanisms. Lower I-PDOS overlap for elastic phonon transfer within diamond for the (111) orientation contributes to its higher TBR, emphasizing the need for interface-specific engineering.
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Your AI Implementation Roadmap
A structured approach to integrating machine learning potentials for advanced thermal management in your high-power devices.
Phase 1: Discovery & Strategy
Initial consultation to understand current thermal management challenges, define project scope, and identify key performance indicators. This phase includes a detailed review of your existing material systems and performance bottlenecks.
Phase 2: ML Potential Development & Simulation
Leverage advanced machine learning potentials (like NEP) to model thermal transport across your specific material interfaces. Simulate the effects of interlayers, strain, and crystal orientation to predict optimal configurations for enhanced thermal conductivity.
Phase 3: Material Design & Optimization
Based on simulation results, propose novel material designs or modifications, including optimal interlayer materials (e.g., Ti for Cu/diamond) and strain conditions. Focus on practical implementation strategies and material compatibility.
Phase 4: Validation & Scaling
Prototype development and experimental validation of the optimized thermal management solutions. Scale up successful designs for integration into your high-power electronic devices, ensuring robust and efficient heat dissipation.
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