Panoramic mapping of phonon transport from ultrafast electron diffraction and machine learning
Abstract
One central challenge in understanding phonon thermal transport is a lack of experimental tools to investigate mode-based transport information. Although recent advances in computation lead to mode-based information, it is hindered by unknown defects in bulk region and at interfaces. Here we present a framework that can reveal microscopic phonon transport information in heterostructures, integrating state-of-the-art ultrafast electron diffraction (UED) with advanced scientific machine learning. Taking advantage of the dual temporal and reciprocal-space resolution in UED, we are able to reliably recover the frequency-dependent interfacial transmittance with possible extension to frequency-dependent relaxation times of the heterostructure. This enables a direct reconstruction of real-space, real-time, frequency-resolved phonon dynamics across an interface. Our work provides a new pathway to experimentally probe phonon transport mechanisms with unprecedented details.
Cite
@article{arxiv.2202.06199,
title = {Panoramic mapping of phonon transport from ultrafast electron diffraction and machine learning},
author = {Zhantao Chen and Xiaozhe Shen and Nina Andrejevic and Tongtong Liu and Duan Luo and Thanh Nguyen and Nathan C. Drucker and Michael E. Kozina and Qichen Song and Chengyun Hua and Gang Chen and Xijie Wang and Jing Kong and Mingda Li},
journal= {arXiv preprint arXiv:2202.06199},
year = {2022}
}