Panoramic mapping of phonon transport from ultrafast electron diffraction and machine learning

TL;DR

This paper introduces a novel framework combining ultrafast electron diffraction and machine learning to directly observe and analyze microscopic phonon transport across interfaces in heterostructures, providing unprecedented experimental insights.

Contribution

It presents a new method integrating UED and machine learning to recover frequency-dependent phonon transmittance and dynamics in heterostructures, enabling detailed mode-based transport analysis.

Findings

Revealed frequency-dependent interfacial transmittance.

Reconstructed real-space, real-time phonon dynamics.

Enabled experimental probing of phonon transport mechanisms.

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.