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• W4310197908 abstract "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 O(103)$mathcal{O}({10^3})$ 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." @default.
• W4310197908 created "2022-11-30" @default.
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• W4310197908 date "2022-11-28" @default.
• W4310197908 modified "2023-10-14" @default.
• W4310197908 title "Panoramic Mapping of Phonon Transport from Ultrafast Electron Diffraction and Scientific Machine Learning" @default.
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• W4310197908 doi "https://doi.org/10.1002/adma.202206997" @default.
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