Panoramic Mapping of Phonon Transport from Ultrafast Electron Diffraction and Scientific Machine Learning.

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 <math xmlns="http://www.w3.org/1998/Math/MathML"> <semantics><mrow><mi>O</mi> <mo>(</mo> <msup><mn>10</mn> <mn>3</mn></msup> <mo>)</mo></mrow> <annotation>$\mathcal{O}({10^3})$</annotation></semantics> </math> 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.