One central challenge in understanding phonon thermal transport is a lack of experimental tools to investigate frequency-resolved phonon transport. Although recent advances in computation lead to frequency-resolved information, it is hindered by unknown defects in bulk regions and at interfaces. Here, a framework that can uncover microscopic phonon transport information in heterostructures is presented, integrating state-of-the-art ultrafast electron diffraction (UED) with advanced scientific machine learning (SciML). Taking advantage of the dual temporal and reciprocal-space resolution in UED, and the ability of SciML to solve inverse problems involving coupled Boltzmann transport equations, the frequency-dependent interfacial transmittance and frequency-dependent relaxation times of the heterostructure from the diffraction patterns are reliably recovered. The framework is applied to experimental Au/Si UED data, and a transport pattern beyond the diffuse mismatch model is revealed, which further enables a direct reconstruction of real-space, real-time, frequency-resolved phonon dynamics across the interface. The work provides a new pathway to probe interfacial phonon transport mechanisms with unprecedented details.
Keywords: phonon; scientific machine learning; thermal transport; ultrafast diffraction.
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