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Researcher profile

Nathan C. Drucker

Nathan C. Drucker contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
cond-mat.mtrl-scicond-mat.mes-hallcond-mat.str-elMachine Learning

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Published work

4 published item(s)

preprint2026arXiv

Probing Non-Equilibrium Grain Boundary Dynamics with XPCS and Domain-Adaptive Machine Learning

Grain-boundary (GB) dynamics control the stability, mechanical, and functional response of nanocrystalline materials, but direct experimental access to their slow non-equilibrium motion has been limited. Here we establish X-ray photon correlation spectroscopy (XPCS), combined with domain-adaptive machine learning, as a quantitative probe of GB dynamics. Temperature- and grain-size-dependent two-time XPCS measurements in nanocrystalline silicon reveal pronounced departures from time-translation invariance, showing that GB relaxation can remain far from equilibrium over experimental timescales. However, direct extraction of quantitative physical information from these high-dimensional, noisy fluctuation maps faces a significant challenge. To overcome this barrier, we develop a semi-supervised learning framework that transfers physical parameter labels from continuum simulations to unlabeled experimental XPCS maps through domain-adaptive representation alignment. This AI-augmented approach enables the extraction of key kinetic parameters, including bulk diffusivity, GB stiffness, and effective GB concentration, directly from experimental XPCS measurements. Our results show how machine learning can transform indirect fluctuation signals into quantitative materials dynamics, providing a general route to study non-equilibrium defect motion in solids.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

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

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.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Spin-polarized imaging of strongly interacting fermions in the ferrimagnetic state of Weyl candidate CeBi

CeBi has an intricate magnetic phase diagram whose fully-polarized state has recently been suggested as a Weyl semimetal, though the role of $f$ states in promoting strong interactions has remained elusive. Here we focus on the less-studied, but also time-reversal symmetry-breaking ferrimagnetic phase of CeBi, where our density functional theory (DFT) calculations predict additional Weyl nodes near the Fermi level $E_\mathrm{F}$. We use spin-polarized scanning tunneling microscopy and spectroscopy to image the surface ferrimagnetic order on the itinerant Bi $p$ states, indicating their orbital hybridization with localized Ce $f$ states. We observe suppression of this spin-polarized signature at $E_\mathrm{F}$, coincident with a Fano line shape in the conductance spectra, suggesting the Bi $p$ states partially Kondo screen the $f$ magnetic moments, and this $p-f$ hybridization causes strong Fermi-level band renormalization. The $p$ band flattening is supported by our quasiparticle interference (QPI) measurements, which also show band splitting in agreement with DFT, painting a consistent picture of a strongly interacting magnetic Weyl semimetal.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

van der Waals metamaterials

The van der Waals heterostructures are a fertile frontier for discovering emergent phenomena in condensed matter systems. They are constructed by stacking elements of a large library of two-dimensional materials, which couple together through van der Waals interactions. However, the number of possible combinations within this library is staggering, and fully exploring their potential is a daunting task. Here we introduce van der Waals metamaterials to rapidly prototype and screen their quantum counterparts. These layered metamaterials are designed to reshape the flow of ultrasound to mimic electron motion. In particular, we show how to construct analogues of all stacking configurations of bilayer and trilayer graphene through the use of interlayer membranes that emulate van der Waals interactions. By changing the membrane's density and thickness, we reach coupling regimes far beyond that of conventional graphene. We anticipate that van der Waals metamaterials will explore, extend, and inform future electronic devices. Equally, they allow the transfer of useful electronic behavior to acoustic systems, such as flat bands in magic-angle twisted bilayer graphene, which may aid the development of super-resolution ultrasound imagers.

0 citations0 reviewsNo code linkedOpen access