arXiv subjects

Jim Ciston

Publications and source records attributed to Jim Ciston.

At least 19 recordsLinked to original sources

arXiv2026

Hydration-controlled twist forms a moir\'e glass in charge-frustrated layered silicates

Twisting layered materials produces moir\'e superlattices, but prescribed twist angles are usually obtained by demanding assembly procedures. Here we show that montmorillonite, an abundant swelling clay, forms tunable moir\'e superlattices naturally. Focal-series high-resolution transmission electron microscopy, geometric phase analysis, and molecular dynamics simulation reveal that its apparent rotational disorder is biased toward low-angle misorientations inherited from discrete hydration states. Multilayer stacks preferentially adopt twists near 1-2{\deg}, 4{\deg}, and 10{\deg}, producing long-wavelength moir\'es without long-range rotational order. We define this kinetically trapped state as a moir\'e glass, distinct from featureless turbostratic stacking. Simulations indicate that lattice-charge disorder stabilizes the angular preferences, whereas charge ordering promotes random stacking. Hydration screens interlayer interactions and lubricates twist, while dehydration arrests the resulting configurations in discrete steps. These results establish dynamic hydration as a macroscopic handle for programming twist in layered matter.

cond-mat.mtrl-sci↗

arXiv2026

Topochemically-engineered coexistence of charge and spin orders in intercalated endotaxial heterostructures

Correlated electron systems that host multiple electronic orders offer routes to multifunctional quantum materials, but strong competition between these orders often prevents their coexistence. Here we show that nanoscale, metastable intercalated heterostructures can stabilize a rare combination of long-range magnetism and a commensurate charge density wave (C-CDW) order in a single material. We synthesize a two-dimensional (2D) metastable crystal, T/H-Fe$_x$TaS2, which comprises an endotaxial polytype heterostructure of 1T-TaS$_2$ and H-TaS$_2$ with Fe intercalated in the van der Waals interfaces. In T/H-Fe$_x$TaS2, Fe intercalants provide localized spins that support ferromagnetism, while 1T layers host a robust commensurate charge density wave (C-CDW) that persists to room temperature. In these intercalated heterostructures, Fe content simultaneously tunes ordering of spin and charge degrees of freedom, positioning topochemically-prepared intercalated endotaxial heterostructures as a route to stabilize and control competing quantum phases in 2D materials.

cond-mat.str-el↗

arXiv2026

Predictive autoencoder-transformer model of Cu oxidation state from EELS and XAS spectra

X-ray absorption spectroscopy (XAS) and electron energy-loss spectroscopy (EELS) produce detailed information about oxidation state, bonding, and coordination, making them essential for quantitative studies of redox and structure in functional materials. However, high-throughput quantitative analysis of these spectra, especially for mixed valence materials, remains challenging as diverse experimental conditions introduce noise, misalignment, broadening of the spectral features. We address this challenge by training a machine learning model consisting of an autoencoder to standardize the spectra and a transformer model to predict both Cu oxidation state and Bader charge directly from L-edge spectra. The model is trained on a large dataset of FEFF-simulated spectra and evaluates model performance on both simulated and experimental data. The results of the machine learning model exhibit highly accurate prediction across the domains of simulated and experimental XAS as well as experimental EELS. These advances enable future quantitative analysis of Cu redox processes under in situ and operando conditions.

cond-mat.mtrl-sci↗

arXiv2024

CuXASNet: Rapid and Accurate Prediction of Copper L-edge X-Ray Absorption Spectra Using Machine Learning

In this work, we have developed CuXASNet, a dense neural network that predicts simulated Cu L-edge X-ray absorption spectra (XAS) from atomic structures. Featurization of the Cu local environment is performed using a component of M3GNet, a graph neural network developed for predicting the potential energy surface. CuXASNet is trained on simulated spectra from FEFF9 at the multiple scattering level of theory, and can predict the L3 and L2 edges for Cu sites to quantitative accuracy. To validate our approach, we compare 14 experimental spectra extracted from the literature with the predictions of CuXASNet. The agreement of CuXASNet with experiments is shown by an average MAE of 0.125 and an average Spearman's correlation coefficient of 0.891, which is comparable to FEFF9's values of 0.131 and 0.898 for the same metrics. As such, CuXASNet can rapidly generate a large number of L-edge XAS spectra at the same accuracy as FEFF9 simulations. This can be used as a drop-in replacement for multiple scattering codes for fast screening of candidate atomic structure models of a measured system. This model establishes a general framework for Cu XAS prediction, and can be extended to more computationally expensive levels of theory and to other transition metal L-edges.

cond-mat.mtrl-sci↗

arXiv2024

In-situ Study of Understanding the Resistive Switching Mechanisms of Nitride-based Memristor Devices

Interface-type resistive switching (RS) devices with lower operation current and more reliable switching repeatability exhibits great potential in the applications for data storage devices and ultra-low-energy computing. However, the working mechanism of such interface-type RS devices are much less studied compared to that of the filament-type devices, which hinders the design and application of the novel interface-type devices. In this work, we fabricate a metal/TiOx/TiN/Si (001) thin film memristor by using a one-step pulsed laser deposition. In situ transmission electron microscopy (TEM) imaging and current-voltage (I-V) characteristic demonstrate that the device is switched between high resistive state (HRS) and low resistive state (LRS) in a bipolar fashion with sweeping the applied positive and negative voltages. In situ scanning transmission electron microscopy (STEM) experiments with electron energy loss spectroscopy (EELS) reveal that the charged defects (such as oxygen vacancies) can migrate along the intrinsic grain boundaries of TiOx insulating phase under electric field without forming obvious conductive filaments, resulting in the modulation of Schottky barriers at the metal/semiconductor interfaces. The fundamental insights gained from this study presents a novel perspective on RS processes and opens up new technological opportunities for fabricating ultra-low-energy nitride-based memristive devices.

physics.app-ph↗

arXiv2024

Random Forest Prediction of Crystal Structure from Electron Diffraction Patterns Incorporating Multiple Scattering

Diffraction is the most common method to solve for unknown or partially known crystal structures. However, it remains a challenge to determine the crystal structure of a new material that may have nanoscale size or heterogeneities. Here we train an architecture of hierarchical random forest models capable of predicting the crystal system, space group, and lattice parameters from one or more unknown 2D electron diffraction patterns. Our initial model correctly identifies the crystal system of a simulated electron diffraction pattern from a 20 nm thick specimen of arbitrary orientation 67% of the time. We achieve a topline accuracy of 79% when aggregating predictions from 10 patterns of the same material but different zone axes. The space group and lattice predictions range from 70-90% accuracy and median errors of 0.01-0.5 angstroms, respectively, for cubic, hexagonal, trigonal and tetragonal crystal systems while being less reliable on orthorhombic and monoclinic systems. We apply this architecture to a 4D-STEM scan of gold nanoparticles, where it accurately predicts the crystal structure and lattice constants. These random forest models can be used to significantly accelerate the analysis of electron diffraction patterns, particularly in the case of unknown crystal structures. Additionally, due to the speed of inference, these models could be integrated into live TEM experiments, allowing real-time labeling of a specimen.

cond-mat.mtrl-sci↗

arXiv2024

Tailored topotactic chemistry unlocks heterostructures of magnetic intercalation compounds

The construction of thin film heterostructures has been a widely successful archetype for fabricating materials with emergent physical properties. This strategy is of particular importance for the design of multilayer magnetic architectures in which direct interfacial spin--spin interactions between magnetic phases in dissimilar layers lead to emergent and controllable magnetic behavior. However, crystallographic incommensurability and atomic-scale interfacial disorder can severely limit the types of materials amenable to this strategy, as well as the performance of these systems. Here, we demonstrate a method for synthesizing heterostructures comprising magnetic intercalation compounds of transition metal dichalcogenides (TMDs), through directed topotactic reaction of the TMD with a metal oxide. The mechanism of the intercalation reaction enables thermally initiated intercalation of the TMD from lithographically patterned oxide films, giving access to a new family of multi-component magnetic architectures through the combination of deterministic van der Waals assembly and directed intercalation chemistry.

cond-mat.mtrl-sci↗

arXiv2024

Improved Three-Dimensional Reconstructions in Electron Ptychography through Defocus Series Measurements

A detailed analysis of ptychography for 3D phase reconstructions of thick specimens is performed. We introduce multi-focus ptychography, which incorporates a 4D-STEM defocus series to enhance the quality of 3D reconstructions along the beam direction through a higher overdetermination ratio. This method is compared with established multi-slice ptychography techniques, such as conventional ptychography, regularized ptychography, and multi-mode ptychography. Additionally, we contrast multi-focus ptychography with an alternative method that uses virtual optical sectioning through a reconstructed scattering matrix ($\mathcal{S}$-matrix), which offers more precise 3D structure information compared to conventional ptychography. Our findings from multiple 3D reconstructions based on simulated and experimental data demonstrate that multi-focus ptychography surpasses other techniques, particularly in accurately reconstructing the surfaces and interface regions of thick specimens.

physics.optics↗

arXiv2023

Probing defectivity beneath the hydrocarbon blanket in 2D hBN using TEM-EELS

The controlled creation and manipulation of defects in 2D materials has become increasingly popular as a means to design and tune new material functionalities. However, defect characterization by direct atomic imaging is often severely limited by surface contamination due to a blanket of hydrocarbons. Thus, analysis techniques are needed that can characterize atomic scale defects despite the contamination. In this work we use electron energy loss spectroscopy to probe beneath the hydrocarbon blanket, characterizing defect structures in 2D hexagonal boron nitride (hBN) based on fine structure in the boron K-edge. Since this technique is performed in a transmission electron microscope, imaging can also be used to assess contamination levels and other factors such as tears in the fragile 2D sheets, which can affect the spectroscopic analysis. Furthermore, by locally probing individual areas, multiple regions on the same specimen that have undergone different defect engineering treatments can be investigated for systematic studies at increased throughput. For 2D hBN samples irradiated with different ions for a range doses, we find spectral signatures indicative of boron-oxygen bonding that can be used as a measure of sample defectiveness depending on the ion beam treatment.

cond-mat.mtrl-sci↗

arXiv2023

Prediction of the Cu Oxidation State from EELS and XAS Spectra Using Supervised Machine Learning

Electron energy loss spectroscopy (EELS) and X-ray absorption spectroscopy (XAS) provide detailed information about bonding, distributions and locations of atoms, and their coordination numbers and oxidation states. However, analysis of XAS/EELS data often relies on matching an unknown experimental sample to a series of simulated or experimental standard samples. This limits analysis throughput and the ability to extract quantitative information from a sample. In this work, we have trained a random forest model capable of predicting the oxidation state of copper based on its L-edge spectrum. Our model attains an $R^2$ score of 0.85 and a root mean square valence error of 0.24 on simulated data. It has also successfully predicted experimental L-edge EELS spectra taken in this work and XAS spectra extracted from the literature. We further demonstrate the utility of this model by predicting simulated and experimental spectra of mixed valence samples generated by this work. This model can be integrated into a real time EELS/XAS analysis pipeline on mixtures of copper containing materials of unknown composition and oxidation state. By expanding the training data, this methodology can be extended to data-driven spectral analysis of a broad range of materials.

cond-mat.mtrl-sci↗

arXiv2023

Three-dimensional imaging of buried heterointerfaces

We report three-dimensional (3D) structure determination of a twisted hexagonal boron nitride (h-BN) heterointerface from a single-view data set using multislice ptychography. We identify the buried heterointerface between two twisted h-BN flakes with a lateral resolution of 0.57 {\AA} and a depth resolution of 2.5 nm. The latter is a significant improvement (~2.7 times) over the aperture-limited depth resolution of incoherent imaging modes such as annular-dark-field scanning transmission electron microscopy. This is attributed to the diffraction signal extending beyond the aperture edge with the depth resolution set by the curvature of the Ewald sphere. Future advances to this approach could improve the depth resolution to the sub-nanometer level and enable the identification of individual dopants, defects and color centers in twisted heterointerfaces and other materials.

cond-mat.mtrl-sci↗

arXiv2023

The 4D Camera: an 87 kHz direct electron detector for scanning/transmission electron microscopy

We describe the development, operation, and application of the 4D Camera -- a 576 by 576 pixel active pixel sensor for scanning/transmission electron microscopy which operates at 87,000 Hz. The detector generates data at approximately 480 Gbit/s which is captured by dedicated receiver computers with a parallelized software infrastructure that has been implemented to process the resulting 10 - 700 Gigabyte-sized raw datasets. The back illuminated detector provides the ability to detect single electron events at accelerating voltages from 30 - 300 keV. Through electron counting, the resulting sparse data sets are reduced in size by 10 - 300x compared to the raw data, and open-source sparsity-based processing algorithms offer rapid data analysis. The high frame rate allows for large and complex 4D-STEM experiments to be accomplished with typical STEM scanning parameters.

physics.ins-det↗

arXiv2022

Rotational and Dilational Reconstruction in Transition Metal Dichalcogenide Moir\'e Bilayers

Lattice reconstruction and corresponding strain accumulation play a key role in defining the electronic structure of two-dimensional moir\'e superlattices, including those of transition metal dichalcogenides (TMDs). Imaging of TMD moir\'es has so far provided a qualitative understanding of this relaxation process in terms of interlayer stacking energy, while models of the underlying deformation mechanisms have relied on simulations. Here, we use interferometric four-dimensional scanning transmission electron microscopy to quantitatively map the mechanical deformations through which reconstruction occurs in small-angle twisted bilayer MoS2 and WSe2/MoS2 heterobilayers. We provide direct evidence that local rotations govern relaxation for twisted homobilayers, while local dilations are prominent in heterobilayers possessing a sufficiently large lattice mismatch. Encapsulation of the moir\'e layers in hBN further localizes and enhances these in-plane reconstruction pathways, suppressing out-of-plane corrugation. We also find that extrinsic uniaxial heterostrain, which introduces a lattice constant difference in twisted homobilayers, leads to accumulation and redistribution of reconstruction strain, demonstrating another route to modify the moir\'e potential.

cond-mat.mtrl-sci↗

arXiv2022

Deep Learning Coherent Diffractive Imaging

We report the development of deep learning coherent electron diffractive imaging at sub-angstrom resolution using convolutional neural networks (CNNs) trained with only simulated data. We experimentally demonstrate this method by applying the trained CNNs to directly recover the phase images from electron diffraction patterns of twisted hexagonal boron nitride, monolayer graphene and a Au nanoparticle with comparable quality to those reconstructed by a conventional ptychographic method. Fourier ring correlation between the CNN and ptychographic images indicates the achievement of a spatial resolution in the range of 0.70 and 0.55 angstrom (depending on different resolution criteria). The ability to replace iterative algorithms with CNNs and perform real-time imaging from coherent diffraction patterns is expected to find broad applications in the physical and biological sciences.

cond-mat.mtrl-sci↗

arXiv2022

One Dimensional Wormhole Corrosion in Metals

Corrosion is a ubiquitous failure mode of materials in extreme environments. The more localized it is, the more difficult it is to detect and more deleterious its effects. Often, the progression of localized corrosion is accompanied by the evolution of porosity in materials, creating internal void-structures that facilitate the ingress of the external environment into the interior of the material, further accelerating the internal corrosion. Previously, the dominant morphology of such void-structures has been reported to be either three-dimensional (3D) or two-dimensional (2D). Here, we report a more localized form of corrosion, which we call 1D wormhole corrosion. Using electron tomography, we show multiple examples of this 1D and percolating morphology that manifests a significantly high aspect ratio differentiable from 2D and 3D corrosion. To understand the origin of this mechanism in a Ni-Cr alloy corroded by molten salt, we combined energy-filtered four-dimensional scanning transmission electron microscopy (EF-4D-STEM) and ab initio density functional theory (DFT) calculations to develop a vacancy mapping method with nanometer-resolution, identifying a remarkably high vacancy concentration in the diffusion-induced grain boundary migration (DIGM) zone, up to 100 times the equilibrium value at the melting point. These vacancy supersaturation regions act as the precursors of wormholes, and lead to the asymmetrical growth of voids along GBs. We show that similar 1D penetrating corrosion morphologies could also occur in other materials or corrosion conditions, implying the broad impact of this extremely localized corrosion mechanism. Deciphering the origins of 1D corrosion is an important step towards designing structural materials with enhanced corrosion resistance, and also offers new pathways to create ordered-porous materials for functional applications.

cond-mat.mtrl-sci↗

arXiv2022

Atomic-scale identification of the active sites of nanocatalysts

Alloy nanocatalysts have found broad applications ranging from fuel cells to catalytic converters and hydrogenation reactions. Despite extensive studies, identifying the active sites of nanocatalysts remains a major challenge due to the heterogeneity of the local atomic environment. Here, we advance atomic electron tomography to determine the 3D local atomic structure, surface morphology and chemical composition of PtNi and Mo-doped PtNi nanocatalysts. Using machine learning trained by density functional theory calculations, we identify the catalytic active sites for the oxygen reduction reaction from experimental 3D atomic coordinates, which are corroborated by electrochemical measurements. By quantifying the structure-activity relationship, we discover a local environment descriptor to explain and predict the catalytic active sites at the atomic level. The ability to determine the 3D atomic structure and chemical species coupled with machine learning is expected to expand our fundamental understanding of a wide range of nanocatalysts.

physics.chem-ph↗

arXiv2022

Disentangling multiple scattering with deep learning: application to strain mapping from electron diffraction patterns

Implementation of a fast, robust, and fully-automated pipeline for crystal structure determination and underlying strain mapping for crystalline materials is important for many technological applications. Scanning electron nanodiffraction offers a procedure for identifying and collecting strain maps with good accuracy and high spatial resolutions. However, the application of this technique is limited, particularly in thick samples where the electron beam can undergo multiple scattering, which introduces signal nonlinearities. Deep learning methods have the potential to invert these complex signals, but previous implementations are often trained only on specific crystal systems or a small subset of the crystal structure and microscope parameter phase space. In this study, we implement a Fourier space, complex-valued deep neural network called FCU-Net, to invert highly nonlinear electron diffraction patterns into the corresponding quantitative structure factor images. We trained the FCU-Net using over 200,000 unique simulated dynamical diffraction patterns which include many different combinations of crystal structures, orientations, thicknesses, microscope parameters, and common experimental artifacts. We evaluated the trained FCU-Net model against simulated and experimental 4D-STEM diffraction datasets, where it substantially out-performs conventional analysis methods. Our simulated diffraction pattern library, implementation of FCU-Net, and trained model weights are freely available in open source repositories, and can be adapted to many different diffraction measurement problems.

cond-mat.mtrl-sci↗

arXiv2021

Fast Grain Mapping with Sub-Nanometer Resolution Using 4D-STEM with Grain Classification by Principal Component Analysis and Non-Negative Matrix Factorization

High-throughput grain mapping with sub-nanometer spatial resolution is demonstrated using scanning nanobeam electron diffraction (also known as 4D scanning transmission electron microscopy, or 4D-STEM) combined with high-speed direct electron detection. An electron probe size down to 0.5 nm in diameter is implemented and the sample investigated is a gold-palladium nanoparticle catalyst. Computational analysis of the 4D-STEM data sets is performed using a disk registration algorithm to identify the diffraction peaks followed by feature learning to map the individual grains. Two unsupervised feature learning techniques are compared: Principal component analysis (PCA) and non-negative matrix factorization (NNMF). The characteristics of the PCA versus NNMF output are compared and the potential of the 4D-STEM approach for statistical analysis of grain orientations at high spatial resolution is discussed.

physics.app-ph↗