arXiv subjects

Mary Scott

Publications and source records attributed to Mary Scott.

14 recordsLinked to original sources

arXiv2026

Crystallographic Challenges in Microscopy of Multidomain Spinel Materials

Electron microscopy techniques are instrumental in the characterization of energy storage materials, with atomic resolution images providing the detailed structural features that are needed to understand their properties. Atomically resolved electron microscopy techniques have been routinely used to study the microstructure in high performing Mn-based oxide cathodes, which often contain spinel-like ordering. Here, we evaluate STEM-HAADF imaging and subsequent Fourier filtering as tools for characterizing {\delta}-DRX spinel domains and their antiphase boundaries, which play a central role in the material's electrochemical performance. Using electron microscopy simulations and recent theoretical insight into the structural makeup of {\delta}-DRX, we attempt to characterize the crystallographic spinel variants which occur in its multi-domain structure. We show that each domain interface, arising from pairings among eight distinct variants, can be categorized into one of four Fourier filtered profiles, one of which leaves the boundary undetectable in atomically resolved electron microscopy when viewed along the preferred [110] zone axis. Our results also suggest that the appearance of seemingly disordered or layered-like regions might actually arise from low energy domain boundaries which are slanted relative to the [110] viewing direction. Our findings highlight the need for careful interpretation of atomic-resolution micrographs of phase transitions, where local reordering drives transformations from higher to lower symmetry structures while maintaining lattice coherence.

cond-mat.mtrl-sci↗

arXiv2025

Investigating the Degradation of LATP Solid Electrolyte in High Alkaline Li-$O_2$ Batteries

In this study, we address the challenge of electrolyte degradation in all-solid-state humidified Li-$O_2$ batteries, which offer high theoretical energy density and potential cost advantages over conventional lithium-ion batteries. Combining STEM-EELS, and XPS characterizations with DFT calculations, we reveal the leaching of $(PO_4)^{3+}$ and $Al^{3+}$ ions from the $Li_{1.3}Al_{0.3}Ti_{1.7}(PO_4)_3$ (LATP) solid electrolyte upon battery discharge, caused by the highly alkaline environment. Upon charging, the leached ions precipitate as $Li_3PO_4$ and $AlPO_4$, which accumulate on the LATP surface and contribute to battery degradation. A Ti-rich layer is observed at the surface after a few cycles due to depletion of other cations. Our findings suggest that the degradation products are formed through repeated dissolution and precipitation in the discharge-charge cycles. Furthermore, our results indicate that the Ti-rich layer on the LATP surface can potentially reduce parasitic reactions. Our study provides mechanistic understanding of LATP solid electrolyte degradation in humidified Li-$O_2$ cell, paving the way for designing more durable and efficient Li-$O_2$ batteries.

cond-mat.mtrl-sci↗

arXiv2024

Federated and differentially private estimation of KL divergence

Measuring distribution drifts is a key task in managing distributed, sensitive data, as it underpins a wide range of federated learning and analytics applications. In many practical settings, however, directly sharing such information is either undesirable (e.g., due to privacy concerns) or infeasible (e.g., due to high communication costs). In this work, we present FedPriKL, a novel method for estimating the KL divergence of data across federated computational models under differential privacy (DP) guarantees. We establish its theoretical properties, showing that FedPriKL is unbiased with low and bounded sensitivity and variance, thereby ensuring strong utility under DP. In addition, we present an empirical study that explores parameter choices to optimize accuracy while minimizing communication overhead. Our experiments demonstrate that FedPriKL achieves accuracy comparable to a similar sampling-based non-private estimator, while delivering greater stability and accuracy than a baseline variant in which users perturb their data locally to obtain privacy guarantees.

cs.LG↗

arXiv2024

Towards Robust Federated Analytics via Differentially Private Measurements of Statistical Heterogeneity

Statistical heterogeneity is a measure of how skewed the samples of a dataset are. It is a common problem in the study of differential privacy that the usage of a statistically heterogeneous dataset results in a significant loss of accuracy. In federated scenarios, statistical heterogeneity is more likely to happen, and so the above problem is even more pressing. We explore the three most promising ways to measure statistical heterogeneity and give formulae for their accuracy, while simultaneously incorporating differential privacy. We find the optimum privacy parameters via an analytic mechanism, which incorporates root finding methods. We validate the main theorems and related hypotheses experimentally, and test the robustness of the analytic mechanism to different heterogeneity levels. The analytic mechanism in a distributed setting delivers superior accuracy to all combinations involving the classic mechanism and/or the centralized setting. All measures of statistical heterogeneity do not lose significant accuracy when a heterogeneous sample is used.

cs.LG↗

arXiv2023

A robust synthetic data generation framework for machine learning in High-Resolution Transmission Electron Microscopy (HRTEM)

Machine learning techniques are attractive options for developing highly-accurate automated analysis tools for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM). However, successfully implementing such machine learning tools can be difficult due to the challenges in procuring sufficiently large, high-quality training datasets from experiments. In this work, we introduce Construction Zone, a Python package for rapidly generating complex nanoscale atomic structures, and develop an end-to-end workflow for creating large simulated databases for training neural networks. Construction Zone enables fast, systematic sampling of realistic nanomaterial structures, and can be used as a random structure generator for simulated databases, which is important for generating large, diverse synthetic datasets. Using HRTEM imaging as an example, we train a series of neural networks on various subsets of our simulated databases to segment nanoparticles and holistically study the data curation process to understand how various aspects of the curated simulated data -- including simulation fidelity, the distribution of atomic structures, and the distribution of imaging conditions -- affect model performance across several experimental benchmarks. Using our results, we are able to achieve state-of-the-art segmentation performance on experimental HRTEM images of nanoparticles from several experimental benchmarks and, further, we discuss robust strategies for consistently achieving high performance with machine learning in experimental settings using purely synthetic data.

cond-mat.mtrl-sci↗

arXiv2023

Disorder-driven localization and electron interactions in Bi$_x$TeI thin films

Strong disorder has a crucial effect on the electronic structure in quantum materials by increasing localization, interactions, and modifying the density of states. Bi$_x$TeI films grown at room temperature and \SI{230}{K} exhibit dramatic magnetotransport effects due to disorder, localization and electron correlation effects, including a MIT at a composition that depends on growth temperature. The increased disorder caused by growth at 230K causes the conductivity to decrease by several orders of magnitude, for several compositions of Bi$_x$TeI. The transition from metal to insulator with decreasing composition $x$ is accompanied by a decrease in the dephasing length which leads to the disappearance of the weak-antilocalization effect. Electron-electron interactions cause low temperature conductivity corrections on the metallic side and Efros-Shklovskii (ES) variable range hopping on the insulating side, effects which are absent in single crystalline Bi$_x$TeI. The observation of a tunable metal-insulator transition and the associated strong localization and quantum effects in Bi$_x$TeI shows the possibility of tuning spin transport in quantum materials via disorder.

cond-mat.mtrl-sci↗

arXiv2023

Imaging 3D Chemistry at 1 nm Resolution with Fused Multi-Modal Electron Tomography

Measuring the three-dimensional (3D) distribution of chemistry in nanoscale matter is a longstanding challenge for metrological science. The inelastic scattering events required for 3D chemical imaging are too rare, requiring high beam exposure that destroys the specimen before an experiment completes. Even larger doses are required to achieve high resolution. Thus, chemical mapping in 3D has been unachievable except at lower resolution with the most radiation-hard materials. Here, high-resolution 3D chemical imaging is achieved near or below one nanometer resolution in a Au-Fe$_3$O$_4$ metamaterial, Co$_3$O$_4$ - Mn$_3$O$_4$ core-shell nanocrystals, and ZnS-Cu$_{0.64}$S$_{0.36}$ nanomaterial using fused multi-modal electron tomography. Multi-modal data fusion enables high-resolution chemical tomography often with 99\% less dose by linking information encoded within both elastic (HAADF) and inelastic (EDX / EELS) signals. Now sub-nanometer 3D resolution of chemistry is measurable for a broad class of geometrically and compositionally complex materials.

physics.comp-ph↗

arXiv2023

Establishing Coherent Momentum-Space Electronic States in Locally Ordered Materials

In our understanding of solids, the formation of highly spatially coherent electronic states, fundamental to command the quantum behavior of materials, relies on the existence of discrete translational symmetry of the crystalline lattice. In contrast, in the absence of long-range order, as in the case of non-crystalline materials, the electronic states are localized and electronic coherence does not develop. This brings forward the fundamental question whether long range order is necessary condition to establish coherence and structured momentum-dependent electronic state, and how to characterize it in the presence of short-range order. Here we study Bi$_2$Se$_3$, a material that exists in its crystalline form with long range order, in amorphous form, with short and medium range order, and in its nanocrystalline form, with reduced short range order. By using angle resolved photoemission spectroscopy to directly access the electronic states in a momentum resolved manner, we reveal that, even in the absence of long-range order, a well-defined real-space length scale is sufficient to produce dispersive band structures. Moreover, we observe for the first time a repeated Fermi surface structure of duplicated annuli, reminiscent of Brillouin zone-like repetitions. These results, together with our simulations using amorphous Hamiltonians, reveal that the typical momentum scale where coherence occurs is the inverse average nearest-neighbor distance, the direct fingerprint of the local order of the underlying atomic structure. These results, not only lead the way to a new understanding of electronic coherence in solids, but also open the way to the realization of novel momentum-dependent quantum phenomena such as momentum pairing and spin-orbit coupling, in a much broader class of materials than the currently studied ones, lacking long range crystalline translational symmetry.

cond-mat.mtrl-sci↗

arXiv2022

Aggregation and Transformation of Vector-Valued Messages in the Shuffle Model of Differential Privacy

Advances in communications, storage and computational technology allow significant quantities of data to be collected and processed by distributed devices. Combining the information from these endpoints can realize significant societal benefit but presents challenges in protecting the privacy of individuals, especially important in an increasingly regulated world. Differential privacy (DP) is a technique that provides a rigorous and provable privacy guarantee for aggregation and release. The Shuffle Model for DP has been introduced to overcome challenges regarding the accuracy of local-DP algorithms and the privacy risks of central-DP. In this work we introduce a new protocol for vector aggregation in the context of the Shuffle Model. The aim of this paper is twofold; first, we provide a single message protocol for the summation of real vectors in the Shuffle Model, using advanced composition results. Secondly, we provide an improvement on the bound on the error achieved through using this protocol through the implementation of a Discrete Fourier Transform, thereby minimizing the initial error at the expense of the loss in accuracy through the transformation itself. This work will further the exploration of more sophisticated structures such as matrices and higher-dimensional tensors in this context, both of which are reliant on the functionality of the vector case.

cs.CR↗

arXiv2021

Applying the Shuffle Model of Differential Privacy to Vector Aggregation

In this work we introduce a new protocol for vector aggregation in the context of the Shuffle Model, a recent model within Differential Privacy (DP). It sits between the Centralized Model, which prioritizes the level of accuracy over the secrecy of the data, and the Local Model, for which an improvement in trust is counteracted by a much higher noise requirement. The Shuffle Model was developed to provide a good balance between these two models through the addition of a shuffling step, which unbinds the users from their data whilst maintaining a moderate noise requirement. We provide a single message protocol for the summation of real vectors in the Shuffle Model, using advanced composition results. Our contribution provides a mechanism to enable private aggregation and analysis across more sophisticated structures such as matrices and higher-dimensional tensors, both of which are reliant on the functionality of the vector case.

cs.CR↗

arXiv2020

Reconstructing the Scattering Matrix from Scanning Electron Diffraction Measurements Alone

Three-dimensional phase contrast imaging of multiply-scattering samples in X-ray and electron microscopy is extremely challenging, due to small numerical apertures, the unavailability of wavefront shaping optics, and the highly nonlinear inversion required from intensity-only measurements. In this work, we present a new algorithm using the scattering matrix formalism to solve the scattering from a non-crystalline medium from scanning diffraction measurements, and recover the illumination aberrations. Our method will enable 3D imaging and materials characterization at high resolution for a wide range of materials.

physics.comp-ph↗

arXiv2020

py4DSTEM: a software package for multimodal analysis of four-dimensional scanning transmission electron microscopy datasets

Scanning transmission electron microscopy (STEM) allows for imaging, diffraction, and spectroscopy of materials on length scales ranging from microns to atoms. By using a high-speed, direct electron detector, it is now possible to record a full 2D image of the diffracted electron beam at each probe position, typically a 2D grid of probe positions. These 4D-STEM datasets are rich in information, including signatures of the local structure, orientation, deformation, electromagnetic fields and other sample-dependent properties. However, extracting this information requires complex analysis pipelines, from data wrangling to calibration to analysis to visualization, all while maintaining robustness against imaging distortions and artifacts. In this paper, we present py4DSTEM, an analysis toolkit for measuring material properties from 4D-STEM datasets, written in the Python language and released with an open source license. We describe the algorithmic steps for dataset calibration and various 4D-STEM property measurements in detail, and present results from several experimental datasets. We have also implemented a simple and universal file format appropriate for electron microscopy data in py4DSTEM, which uses the open source HDF5 standard. We hope this tool will benefit the research community, helps to move the developing standards for data and computational methods in electron microscopy, and invite the community to contribute to this ongoing, fully open-source project.

cond-mat.mtrl-sci↗

arXiv2019

Role of Element-Specific Damping on the Ultrafast, Helicity-Independent All-Optical Switching Dynamics in Amorphous (Gd,Tb)Co Thin Films

Ultrafast control of the magnetization in ps timescales by fs laser pulses offers an attractive avenue for applications such as fast magnetic devices for logic and memory. However, ultrafast helicity-independent all-optical switching (HI-AOS) of the magnetization has thus far only been observed in Gd-based, ferrimagnetic amorphous (\textit{a}-) rare earth-transition metal (\textit{a}-RE-TM) systems, and a comprehensive understanding of the reversal mechanism remains elusive. Here, we report HI-AOS in ferrimagnetic \textit{a}-Gd$_{22-x}$Tb$_x$Co$_{78}$ thin films, from x = 0 to x = 18, and elucidate the role of Gd in HI-AOS in \textit{a}-RE-TM alloys and multilayers. Increasing Tb content results in increasing perpendicular magnetic anisotropy and coercivity, without modifying magnetization density, and slower remagnetization rates and higher critical fluences for switching but still shows picosecond HI-AOS. Simulations of the atomistic spin dynamics based on the two-temperature model reproduce these results qualitatively and predict that the lower damping on the RE sublattice arising from the small spin-orbit coupling of Gd (with $L = 0$) is instrumental for the faster dynamics and lower critical fluences of the Gd-rich alloys. Annealing \textit{a}-Gd$_{10}$Tb$_{12}$Co$_{78}$ leads to slower dynamics which we argue is due to an increase in damping. These simulations strongly indicate that acounting for element-specific damping is crucial in understanding HI-AOS phenomena. The results suggest that engineering the element specific damping of materials can open up new classes of materials that exhibit low-energy, ultrafast HI-AOS.

cond-mat.mtrl-sci↗

arXiv2019

Observation of spin-momentum locked surface states in amorphous Bi$_{2}$Se$_{3}$

Crystalline symmetries have played a central role in the identification of topological materials. The use of symmetry indicators and band representations have enabled a classification scheme for crystalline topological materials, leading to large scale topological materials discovery. In this work we address whether amorphous topological materials, which lie beyond this classification due to the lack of long-range structural order, exist in the solid state. We study amorphous Bi$_2$Se$_3$ thin films, which show a metallic behavior and an increased bulk resistance. The observed low field magnetoresistance due to weak antilocalization demonstrates a significant number of two dimensional surface conduction channels. Our angle-resolved photoemission spectroscopy data is consistent with a dispersive two-dimensional surface state that crosses the bulk gap. Spin resolved photoemission spectroscopy shows this state has an anti-symmetric spin texture resembling that of the surface state of crystalline Bi$_2$Se$_3$. These experimental results are consistent with theoretical photoemission spectra obtained with an amorphous tight-binding model that utilizes a realistic amorphous structure. This discovery of amorphous materials with topological properties uncovers an overlooked subset of topological matter outside the current classification scheme, enabling a new route to discover materials that can enhance the development of scalable topological devices.

cond-mat.mtrl-sci↗