Panoramic Mapping of Phonon Transport from Ultrafast Electron Diffraction and Scientific Machine Learning
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. Her...
| Main Authors: | , , , , , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023
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| Online Access: | https://hdl.handle.net/1721.1/147618 |
| _version_ | 1826197600949239808 |
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| author | 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 |
| author2 | Massachusetts Institute of Technology. Department of Nuclear Science and Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Nuclear Science and Engineering 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 |
| author_sort | Chen, Zhantao |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T10:50:28Z |
| format | Article |
| id | mit-1721.1/147618 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:50:28Z |
| publishDate | 2023 |
| publisher | Wiley |
| record_format | dspace |
| spelling | mit-1721.1/1476182023-01-21T03:35:11Z Panoramic Mapping of Phonon Transport from Ultrafast Electron Diffraction and Scientific Machine Learning 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 Massachusetts Institute of Technology. Department of Nuclear Science and Engineering 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. 2023-01-20T18:43:49Z 2023-01-20T18:43:49Z 2023-01 2023-01-20T18:18:35Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/147618 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). en 10.1002/adma.202206997 Advanced Materials Creative Commons Attribution NonCommercial License 4.0 https://creativecommons.org/licenses/by-nc/4.0/ application/pdf Wiley Wiley |
| spellingShingle | 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 Panoramic Mapping of Phonon Transport from Ultrafast Electron Diffraction and Scientific Machine Learning |
| title | Panoramic Mapping of Phonon Transport from Ultrafast Electron Diffraction and Scientific Machine Learning |
| title_full | Panoramic Mapping of Phonon Transport from Ultrafast Electron Diffraction and Scientific Machine Learning |
| title_fullStr | Panoramic Mapping of Phonon Transport from Ultrafast Electron Diffraction and Scientific Machine Learning |
| title_full_unstemmed | Panoramic Mapping of Phonon Transport from Ultrafast Electron Diffraction and Scientific Machine Learning |
| title_short | Panoramic Mapping of Phonon Transport from Ultrafast Electron Diffraction and Scientific Machine Learning |
| title_sort | panoramic mapping of phonon transport from ultrafast electron diffraction and scientific machine learning |
| url | https://hdl.handle.net/1721.1/147618 |
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