Panoramic Mapping of Phonon Transport from Ultrafast Electron Diffraction and Scientific Machine Learning
Author(s)
Chen, Zhantao; Shen, Xiaozhe; Andrejevic, Nina; Liu, Tongtong; Luo, Duan; Nguyen, Thanh; Drucker, Nathan C; Kozina, Michael E; Song, Qichen; Hua, Chengyun; Chen, Gang; Wang, Xijie; Kong, Jing; Li, Mingda; ... Show more Show less
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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 ( 10 3 ) $\mathcal{O}({10^3})$ 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.
Date issued
2023-01Department
Massachusetts Institute of Technology. Department of Nuclear Science and EngineeringJournal
Advanced Materials
Publisher
Wiley
Citation
Chen, Zhantao, Shen, Xiaozhe, Andrejevic, Nina, Liu, Tongtong, Luo, Duan et al. 2023. "Panoramic Mapping of Phonon Transport from Ultrafast Electron Diffraction and Scientific Machine Learning." Advanced Materials, 35 (2).
Version: Final published version