With the rapid development of high-power electronics, wide-bandgap semiconductors, heterogeneous integration, and advanced packaging technologies, heat transport research is shifting from the characterization of individual thermophysical properties toward cross-scale thermal design across materials, interfaces, structures, and devices. Heat conduction involves atomic interactions, interfacial thermal transfer, heat-flow regulation, and device-level heat dissipation. Conventional high-fidelity calculations, empirical models, and case-by-case simulations are often limited by high computational cost, restricted design-space exploration, and insufficient parameter transfer across scales. Artificial intelligence, especially machine learning, provides new methodological support for thermal transport research and high-performance thermal management through data mining, surrogate modeling, and optimization.
This review focuses on artificial-intelligence-enabled cross-scale thermal design, with machine learning as the central thread. Organized around a chain of data, models, parameter transfer, device optimization, and experimental validation, it first clarifies the boundaries of AI/ML methods, physical solvers, and optimization algorithms. It then reviews thermophysical-property prediction and target-oriented materials design, covering data generation, model construction, candidate generation, inverse design, uncertainty quantification, and validation. Atomistic interfacial design using system-specific machine-learning interatomic potentials and universal pretrained atomistic models is discussed for heterogeneous solid–solid interfaces, van der Waals interfaces involving two-dimensional materials, and solid–liquid interfaces. Attention is paid to cross-scale parameter bridging, including rotation of anisotropic thermal-conductivity tensors, interfacial thermal-conductance boundary conditions, combination of intrinsic and process-induced thermal resistances, and propagation of parameter uncertainty to device-level outputs. Applications of surrogate modeling, multi-objective optimization, and machine-learning-assisted topology optimization are reviewed for advanced packaging, hotspot mitigation, cold-plate and microchannel design, manufacturability, and reliability. A GaN/BAs heterostructure serves as an end-to-end case study of transferring material and interfacial parameters into finite-element models with experimental benchmarking.
Taken together, AI-enabled cross-scale thermal design is moving beyond isolated prediction and optimization toward an integrated framework linking data generation, parameter transfer, device-level decision making, and experimental feedback. However, realizing this vision requires addressing key challenges in data quality and standardization, physical consistency, out-of-distribution generalization of atomistic models, coarse-graining of nonequilibrium phonon information, cross-scale uncertainty propagation, and closed-loop experimental validation. Future progress will depend on the deeper integration of multiscale simulation, physics-informed machine learning, data-driven optimization, additive manufacturing, and digital twins, ultimately advancing heat transport research toward reliable and intelligent thermal design from atoms to devices under realistic operating conditions.