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At least 1,423 records · Page 79

A continuum dislocation-based model of wedge microindentation of single crystals

Recent Electron Backscatter Diffraction (EBSD) experiments have revealed the emergence of heterogeneous dislocation microstructures forming under a wedge indenter in fcc crystals, where micro-meter dislocation patterns challenge the predictions of traditional models of plasticity. In order to explain the formation of these features and develop a relationship between the force-displacement curve and the dislocation substructure, here we present a model of wedge indentation based on the continuum theory of dislocations. The model accounts for large deformation kinematics through the multiplicative split of the deformation gradient tensor, where the incompatible plastic component of deformation results from the flux of dislocations on different and interacting slips systems. Constitutive equations for dislocation fluxes are determined from a dissipative variational principle. As a result, each dislocation density satisfies an initial-boundary value problem with convective-diffusive character, which is coupled to the macroscopic stress and displacement fields governing the deformation process. Solution to the self-consistent continuum formulation is found using the finite element method. Computer simulations mimic the experimental conditions of wedge micro-indentation experiments into Ni single-crystals used by Kysar et al. (2010a). A comparison of overall dislocation density distribution and macroscopic mechanical response shows good overall agreement with the experimental results in terms of the detailed features of dislocation patterns and lattice rotations as well as the macroscopic force-displacement response.

36 MATERIALS SCIENCE↗

Reliable thermodynamic estimators for screening caloric materials

Reversible, diffusionless, first-order solid-solid phase transitions accompanied by caloric effects are critical for applications in the solid-state cooling and heat-pumping devices. Accelerated discovery of caloric materials requires reliable but faster estimators for predictions and high-throughput screening of system-specific dominant caloric contributions. We assess reliability of the computational methods that provide thermodynamic properties in relevant solid phases at or near a phase transition. Here, we test the methods using the well-studied B2 FeRh alloy as a “fruit fly” in such a materials genome discovery, as it exhibits a metamagnetic transition which generates multicaloric (magneto-, elasto-, and baro-caloric) responses. For lattice entropy contributions, we find that the commonly-used linear-response and small-displacement phonon methods are invalid near instabilities that arise from the anharmonicity of atomic potentials, and we offer a more reliable and precise method for calculating lattice entropy at a fixed temperature. Then, we apply a set of reliable methods and estimators to the metamagnetic transition in FeRh (predicted 346 ± 12 K, observed 353 ± 1 K) and calculate the associated caloric properties, such as isothermal entropy and isentropic temperature changes.

36 MATERIALS SCIENCE↗

Adsorption mechanism of alkyl hydroxamic acid onto bastnäsite: Fundamental steps toward rational collector design for rare earth elements

Rare earth element (REE) production is limited in part by inefficient strategies for beneficiation. Hydroxamic acid ligands are promising reagents for the selective flotation of bastnäsite [(Ce,La)FCO 3 ], a major REE ore mineral, but the mechanism and energetics of adsorption are not understood, interfering with the design of new, more efficient reagents. In this work, the adsorption of octyl hydroxamic acid onto bastnäsite was calculated using a combination of experimental and computational methods. In-situ attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy revealed changes in the hydroxamate functional group vibrational frequencies, corresponding to chelation with cerium cations at the bastnäsite surface. The results indicate a monodentate chemisorption mechanism at low surface loading that changes to bidentate chemisorption at higher concentrations. This interpretation is supported by molecular vibrational frequency shifts calculated using density functional theory (DFT), and orientation of the hydrocarbon chain measured by sum frequency generation (SFG) vibrational spectroscopy. The binding enthalpies of octyl hydroxamic acid interacting with La and Ce-bastnäsite surfaces were measured using isothermal titration calorimetry (ITC) revealing a stronger coordinating ability with bastnäsite than with a common gangue mineral, calcite (CaCO 3 ). Because octyl hydroxamate favors monodentate adsorption at low surface coverages, the relative chelating strength of metal ions could be a poor predictor for selectivity under monolayer adsorption conditions. At higher surface loadings, where the bidentate mode of adsorption is active, selectivity is likely to be limited by increased flotation of gangue ores.

58 GEOSCIENCES↗

Predicting metal-binding proteins and structures through integration of evolutionary-scale and physics-based modeling

Metals are essential elements in all living organisms, binding to approximately 50% of proteins. They serve to stabilize proteins, catalyze reactions, regulate activities, and fulfill various physiological and pathological functions. While there have been many advancements in determining the structures of protein-metal complexes, numerous metal-binding proteins still need to be identified through computational methods and validated through experiments. Here, to address this need, we have developed the ESMBind workflow, which combines evolutionary scale modeling (ESM) for metal-binding prediction and physics-based protein-metal modeling. Our approach utilizes the ESM-2 and ESM-IF models to predict metal-binding probability at the residue level. In addition, we have designed a metal-placement method and energy minimization technique to generate detailed 3D structures of protein-metal complexes. Our workflow outperforms other models in terms of residue and 3D-level predictions. To demonstrate its effectiveness, we applied the workflow to 142 uncharacterized fungal pathogen proteins and predicted metal-binding proteins involved in fungal infection and virulence.

59 BASIC BIOLOGICAL SCIENCES↗

Type IV failure in weldment of creep resistant ferritic alloys: I. Micromechanical origin of creep strain localization in the heat affected zone

Creep strength enhanced ferritic (CSEF) steels containing 9-12wt% chromium have been extensively used in fossil-fuel-fired power plants. Despite their excellent creep resistance at high temperatures, premature failures (especially Type IV cracking) are often found in the fine-grained heat affected zone (HAZ) or intercritical HAZ of the welded components. This failure mode is preceded by the strain localization in the HAZ, as measured by the Digital Image Correlation (DIC) technique. The present work aims to develop a finite-element based computational method to determine the micromechanical and microstructural origin of the strain localization phenomenon. We construct a two-dimensional digital microstructure based on the actual microstructure of ferritic steel weldments by using the Voronoi-tessellation method, to account for the effects of its large grain-size gradients. A mechanism-based finite element method is developed for modeling the high temperature deformation resulting from a synergy of thermally activated dislocation movements, diffusional flow and grain boundary sliding. The numerical results agree well with the strain measurements by our DIC technique, particularly revealing the effect of pre-welding tempering on the evolution of strain localization in HAZ of creep resistant steel weldments. It is found that the diffusional creep with dependence on grain sizes, dislocation creep with dependence on material strength, and more importantly, grain boundary sliding, contribute synergistically to the creep strain accumulation in the HAZ, and their relative degree of significance is quantified. The creep rupture life will be investigated in the companion paper.

36 MATERIALS SCIENCE↗

Revealing the complex chemistry of grain boundaries in K-doped BaFe 2 As 2 with atom probe tomography

Iron-based superconductors have attractive properties for high-field applications, but there is a lack of understanding of the effect of grain boundary chemistry on the in-field performance. The near atomic-scale resolution, ppm sensitivity and 3D analysis offered by atom probe tomography make it a powerful tool to investigate the nanoscale structure and chemistry of these defects in fine-grained K-doped BaFe 2 As 2 samples. A computational method to systematically extract and compare the Gibbsian interfacial excess of chemical species across grain boundaries has been explored in this work. The robustness of the method has been tested by evaluating the effects of selected variables on simulated APT datasets. The accuracy and precision of the calculated Gibbsian interfacial excess were found to be stable over a range of analysis conditions: varying grain boundary widths and detection efficiencies, spatial precisions below 1.5 nm, and bin widths between 1.2 and 1.6 nm. For the K-doped BaFe 2 As 2 samples studied, segregation of As, Ba, K and impurities of O, Na, and Sb were found at grain boundaries. The Gibbsian excess values were found to vary widely between different boundaries, showing the complexity of the grain boundary chemistry in this material. Possible links between the observed critical current density (Jc) of these samples and their nano- and micro-structure have also been investigated and discussed.

36 MATERIALS SCIENCE↗

Integrated simulation of U-10Mo monolithic fuel swelling behavior

Here, a separate computational branch has been implemented within the DART (Dispersion Analysis Research Tool) computational code to simulate the swelling behavior of U-10Mo monolithic fuel under the operating conditions of high-power research and test reactors (RTRs). The monolithic branch of the DART code implements a mechanistic rate-theory-based fission-gas-behavior model for the calculation of fission gas swelling, as well as a suite of thermal, physical, and mechanical models to take into account various processes occurring in RTR fuels during irradiation. In order to accurately simulate and eventually predict U-10Mo monolithic fuel irradiation behavior, the code uses materials properties calculated with lower length-scale computational methods, such as gas atom diffusivity and U-Mo surface energy from atomic simulations and grain-morphology-specific recrystallization kinetics (recrystallized fuel volume fractions vs. fission density) predicted using the phase-field method. The remainder of fission gas behavior parameters used in the model were calibrated with measured intergranular bubble size distributions. With this integrated simulation approach, the swelling behavior of U-10Mo monolithic fuel was simulated for various initial grain sizes at different operating conditions and compared with measured data. Furthermore, because limited experimental data exist for parameter calibration detailed sensitivity studies for the important parameters used in the fission gas behavior model were performed in order to examine their impact on both intergranular gas bubble morphology at low fission density, and on total porosity at high fission density.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Synthesis and characterization of the dinuclear cobalt(III) complex: [(C 2 F 5 ) 3 Co(μ-F)] 2 2–

Here, reaction of the versatile tris(perfluoroethyl) cobalt(III) precursor [fac-(MeCN) 3 Co(C 2 F 5 ) 3 ] with [NMe 4 ]F and [PPh 4 ]Cl in THF formed the unexpected cobalt(III) bridging fluoride dimer ([(C 2 F 5 ) 3 Co(μ-F)] 2 2– . The new fluoro-organometallic cobalt(III) complex was characterized by NMR and UV-vis spectroscopies, X-ray crystallography, cyclic voltammetry, and by computational methods. In the strongly coordinating solvent MeCN, ([(C 2 F 5 ) 3 Co(μ-F)] 2 2– exhibits dynamic processes on the NMR timescale which are consistent with solvent coordination. However, in the weakly coordinating solvent CH 2 Cl 2 , a more static structure is observed suggesting that the dimer retains its structure in solution state. An electrochemical analysis of ([(C 2 F 5 ) 3 Co(μ-F)] 2 2– was performed, and the data are compared to previously reported cobalt(III) perfluoroethyl complexes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Effects of charged interfaces on electrolyte decomposition at the lithium metal anode

Lithium–Sulfur batteries are promising candidates to substitute conventional Li-ion batteries due to their higher energy density and reduced cost. However, several challenges related to the reactivity of the lithium metal anode have prevented this technology from becoming broadly commercialized. Lithium's high reactive nature leads to the continuous decomposition of the electrolyte and the formation of the solid-electrolyte interphase (SEI) layer. Thus, a comprehensive understanding of how the SEI film is formed is crucial to help elucidate improvements for this battery technology. In this work, we use density functional theory (DFT) based computational methods to investigate the effect of charged interfaces on the electrolyte degradation at the lithium anode. Here, several electrolyte mixtures were considered including 1,2-dimethoxyethane (DME) and 1,3-dioxolane (DOL) as solvents, and lithium bis(fluorosulfonyl)imide (LiFSI) and lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) as salts. It is found that the extent of salt decomposition is higher when the interface is charged. In addition, two types of solvent reduction mechanisms are identified: C–O bond cleavage and radical attacks. Charge transfer and evolution analysis are characterized in detail. Finally, solvent decomposition reactions are found to become more thermodynamically favorable and their activation barriers to be diminished under the effect of constant potentials and electric fields.

25 ENERGY STORAGE↗

Prediction of perovskite and other ternary oxide multilayers as mirrors for soft X-rays

Highlights: • Theoretical framework to identify optimal multilayers for high x-ray reflectivity. • Perovskite oxide multilayers are proposed as soft x-ray mirrors. • High soft x-ray reflectivity up to 33% are expected in perovskite multilayers. - Abstract: We propose multilayers of perovskite and related ternary oxides with a general formula of ABO{sub 3} as high reflectivity mirrors, especially for use in water window region (2.3–4.4 nm). The high reflectivity combinations of oxide multilayers were deduced using evolutionary search algorithms such as genetic algorithms, and the calculation speed was accelerated via parallel computing methods We also propose a figure of merit for X-ray reflectivity in periodic multilayers systems, which can simplify the future efforts on identifying material combinations, and the search through this multi-dimensional parameter space. The highest reflectivity value was found to be over 33% at 3.1 nm in the water window region. The effect of interface roughness was simulated and the decrease in reflectivity was found to be modest for practically achievable roughness values. This work establishes the foundation for future experimental and theoretical studies towards achieving high reflectivity x-ray mirrors of complex oxide multilayers.

36 MATERIALS SCIENCE↗

Deep-Learning-Derived Evaluation Metrics Enable Effective Benchmarking of Computational Tools for Phosphopeptide Identification

Tandem mass spectrometry (MS/MS)-based phosphoproteomics is a powerful technology for global phosphorylation analysis. However, applying four computational pipelines to a typical mass spectrometry (MS)-based phosphoproteomic dataset from a human cancer study, we observed a large discrepancy among the reported phosphopeptide identification and phosphosite localization results, underscoring a critical need for benchmarking. While efforts have been made to compare performance of computational pipelines using data from synthetic phosphopeptides, evaluations involving real application data have been largely limited to comparing the numbers of phosphopeptide identifications due to the lack of appropriate evaluation metrics. We investigated three deep learning-derived features as potential evaluation metrics: phosphosite probability, Delta RT and spectral similarity. Predicted phosphosite probability is computed by MusiteDeep, which provides high accuracy as previously reported; Delta RT is defined as the absolute retention time (RT) difference between RTs observed and predicted by AutoRT; and spectral similarity is defined as the Pearson’s correlation coefficient between spectra observed and predicted by pDeep2. Using a synthetic peptide dataset, we found that both Delta RT and spectral similarity provided excellent discrimination between correct and incorrect peptide-spectrum matches (PSMs) both when incorrect PSMs involved wrong peptide sequences and even when incorrect PSMs were caused by only incorrect phosphosite localization. Based on these results, we used all the three deep learning-derived features as evaluation metrics to compare different computational pipelines on diverse set of phosphoproteomic datasets and showed their utility in benchmarking performance of the pipelines. The benchmark metrics demonstrated in this study will enable users to select computational pipelines and parameters for routine analysis of phosphoproteomics data and will offer guidance for developers to improve computational methods.

59 BASIC BIOLOGICAL SCIENCES↗

An investigation of geometrically necessary dislocations and back stress in large grained tantalum via EBSD and CPFEM

This study explores the evolution of GNDs and their effects on back stress through experimental and computational methods. Four large-grained tantalum tensile specimens were strained in uniaxial tension, electron backscatter diffraction (EBSD) data were collected, and geometrically necessary dislocation (GND) maps of the four specimens in the unloaded state were produced. EBSD-based GND maps revealed several types of features with high GND content which caused back stress in the specimens. Correlations between five geometrically-based grain boundary (GB) transmission factors and the GB GND content were evaluated, and statistically significant correlations were found for transmission factors based on Livingston and Chalmer’s N factor, Werner and Prantl’s slip transfer number, and GB misorientation. The sign of individual components of the Nye tensor were used to visually and quantitatively identify clustering of GNDs of the same sign, thus giving additional evidence of increasing back stress due to deformation. Deformation of one of the specimens was simulated using multiple CPFEM based modeling approaches and predicted stress-strain responses are compared. The super dislocation model (SD model) — a crystal plasticity finite element method (CPFEM) which incorporates elastic dislocation interactions — was able to isolate impact of back stress on the overall flow stress. The SD model predicted correct stresses when compared with experimental data; however, when the elastic interactions in the SD model were turned off, stress predictions were 25% too low. Thus, demonstrating the importance of incorporating back stress into the model.

42 ENGINEERING↗

The structural and dynamical role of water in natural organic matter: A 2H NMR and XRD study

Natural organic matter (NOM) is an important component in many near-surface geochemical environments, and its properties are greatly affected by the incorporation of water. Because of its importance, the macroscopic behavior and effects of water in NOM and soil organic matter (SOM) have been extensively studied using a wide range of experimental and computational methods. The molecular scale structural and dynamical behavior of water in these materials, however, is less well understood. This paper presents a variable temperature 2 H NMR and XRD study of water in Suwannee River NOM and its fulvic acid (FA) and humic acid (HA) fractions that provides new insight into the dynamical behavior of structurally different types of water and exchangeable hydrogen environments in NOM. The results provide a basis for future studies of more complex natural organic materials and the interaction of organic materials with mineral surfaces. Room temperature 2 H NMR spectra of samples hydrated in 2 H 2 O and then dehydrated, distinguish 2 H 2 O molecules that are in rapid reorientational motion (correlation times, ν c, > 10 5 Hz), 2 H exchanged onto carboxylic sites of the NOM that do not undergo rapid reorientation at frequencies >~10 3 Hz, and 2 H exchanged onto phenolic and possibly other alcohol sites of the NOM that undergoes rapid, but anisotropic, dynamical reorientation. For samples exposed to water and not dried, the XRD results collected at temperatures from 173 to 298 K show the formation of ice-1h in samples exposed to 100% relative humidity (R.H.) but not in samples exposed to 43% R.H. 2 H NMR of those samples collected at temperatures from 313 K to 173 K show the presence of multiple sites. Near room temperature, the spectra contain a narrow resonance for mobile water undergoing rapid isotropic motion, and a broader symmetrical resonance probably due to a combination of more dynamically restricted water molecules and 2 H exchanged onto phenolic and alcohol functional groups undergoing rapid anisotropic motion. The 43% R.H. samples also yield a broader, quadrupole-dominated, resonance for 2 H exchanged onto functional groups of the NOM. With decreasing temperature the resonances for dynamically restricted water molecules and 2 H exchanged onto phenolic and alcohol functional groups become broader, reflecting a decreasing rate of exchange between the water molecules and functional groups and a decreasing rate of reorientation of the 2 H 2 O molecules. The formation of ice-1h is directly reflected in the 2 H spectra of the 100% R.H. samples as a resonance with a quadrupole coupling constant (QCC) of ~180 kHz. For the 43% R.H. samples, there is also a broad, poorly resolved resonance with typical QCCs of ~180 kHz for which the relative signal intensity increases with decreasing temperature. This signal represents 2 H 2 O molecules that are not crystallized in ice-1h but have greatly reduced reorientation frequencies at low temperature and a hydrogen bonding network with hydrogen bond strengths similar to, but somewhat weaker than, ice-1h. Such molecules are also likely to be present in the 100% R.H. samples. At both R.H.s, some of the 2 H 2 O molecules do not freeze and retain their isotropic motion down to 173 K, the lowest temperature investigated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NEML2: An efficient and modular multiphysics constitutive modeling library for hybrid computing environments

This paper presents NEML2, an open-source, high-performance library developed for constitutive material modeling, designed to support the flexible and modular development of models for complex material behavior. Building on the foundational structure of its predecessor, NEML, the NEML2 library introduces significant improvements, including enhanced vectorization, automatic differentiation, and seamless integration with PyTorch, facilitating the application of machine learning techniques in material simulations. NEML2 provides a C++ backend with Python bindings, enabling users to create custom material models that can be executed efficiently on both CPU and GPU platforms. The library also supports coupling with Multiphysics simulation frameworks like MOOSE, making it suitable for realistic simulations involving coupled physical processes. Rigorous quality assurance through unit and regression testing ensures the reliability of results, while the extensible, user-friendly design encourages collaboration and reproducibility across the scientific community. This paper provides an overview of NEML2’s architecture, core features, and applications, highlighting its impact on accelerating material qualification and advancing computational methods in materials science.

GPU↗

Antiviral discovery using sparse datasets by integrating experiments, molecular simulations, and machine learning

Computational methods have demonstrated success in identifying virucidal agents, effectively contributing to the discovery of novel virucidal molecules. In this study, we developed a machine learning (ML) model, trained on a small dataset, to predict inhibitors of human enterovirus 71 (EV71), a pathological agent that causes severe disease in children and immunocompromised adults. Despite the dataset’s limitation, comprising of only 36 compounds tested, our ML framework demonstrated significant predictive capability. Notably, experimental validation revealed that five out of the eight compounds predicted by our model from the Chinese cosmetic material list exhibited virucidal activity. The inhibitor effects displayed by the main active compounds were further confirmed by molecular dynamics simulation. This underscores the potential of our AI-driven approach to bypass data constraints in identifying active molecules against viral pathogens.

60 APPLIED LIFE SCIENCES↗

Numerical study of variable density turbulence interaction with a normal shock wave

Accurate numerical simulations of shock–turbulence interaction (STI) are conducted with a hybrid monotonicity-preserving–compact-finite-difference scheme for a detailed study of STI in variable density flows. Theoretical and numerical assessments of data confirm that all turbulence scales as well as the STI are well captured by the computational method. Linear interaction approximation (LIA) convergence tests conducted with the shock-capturing simulations exhibit a similar trend of converging to LIA predictions to shock-resolving direct numerical simulations (DNS). The effects of density variations on STI are studied by comparing the results corresponding to an upstream multi-fluid mixture with the single-fluid case. Here the results show that for the current parameter ranges, the turbulence amplification by the normal shock wave is much higher and the reduction in turbulence length scales is more significant when strong density variations exist. Turbulent mixing enhancement by the shock is also increased and stronger mixing asymmetry in the postshock region is observed when there is significant density variation. The turbulence structure is strongly modified by the shock wave, with a differential distribution of turbulent statistics in regions having different densities. The dominant mechanisms behind the variable density STI are identified by analysing the transport equations for the Reynolds stresses, vorticity, normalized mass flux and density specific volume covariance.

42 ENGINEERING↗

Atomic-Scale Structural Mapping of Active Sites in Monolayer PGM-Free Catalysts by Low-Voltage 4D-STEM

Two-dimensional (2D) materials have attracted a large amount of attention in both basic and applied fields, and scanning transmission electron microscopy (STEM) is often uniquely well-suited for characterizing the atomic-scale structure of these materials [1-4]. As a result, STEM is poised to significantly impact progress on platinum group metal (PGM)-free catalysts, which are currently under intense development to enable low-cost, commercially viable hydrogen fuel cells [5]. While recent advancements have resulted in fuel cell performance comparable to Pt catalysts by some measures [6], cell durability remains a significant challenge, limiting practical applications [7]. Catalytically active sites in PGM-free materials are proposed to be FeN4 complexes embedded in a graphene lattice (Fig. 1b) within layered or other larger materials, but this is still under debate largely due to the range of potential actives sites predicted by computational methods and lack of methods for directly validating these models [5]. Fundamental insights into the atomic structure and resulting degradation pathways of proposed active sites are therefore needed to fully understand and control cell performance and durability [6].2D materials typically make ideal samples for STEM, but those within PGM-free catalysts present additional challenges since these materials are often defect-rich, with a high density of edges, dopant atoms, etc., which significantly increase susceptibility to beam damage at standard operating voltages. This makes analysis of potential FeN4 active sites particularly challenging, since a large proportion of Fe exists at edge sites where beam-induced atomic displacements can prohibit high-resolution structural characterization [6]. Conventional dark-field imaging compounds this problem by producing less signal for a given dose and being less sensitive to light elements than dose-efficient phase contrast imaging techniques such as those enabled by four-dimensional (4D)-STEM [8-10] (Fig. 1a). Consequently, active site structural analysis is often left to methods such as low-resolution imaging combined with quantum chemical calculations [6], which hinders accurate determination of reaction and degradation mechanisms.Here, we demonstrate direct atomic-scale structural mapping of FeN4 sites by performing low-voltage 4D-STEM on a model PGM-free catalyst system with many exposed monolayer regions. To accomplish this, we pair a 30 keV aberration-corrected probe with a fast pixelated detector that has optimal performance at low beam voltages [11]. This enables us to simultaneously image light and heavy elements with high signal-to-noise by center-of-mass analysis (Fig. 1c) while minimizing beam-induced atomic displacements at sensitive sites. The monolayer nature of these materials additionally allows for experimental validation by direct comparison with multislice simulations [12] of model structures (Fig. 1d-e). This work demonstrates how low-voltage 4D-STEM will provide new insights into the atomic-scale structure and degradation mechanisms of active sites in PGM-free catalysts, facilitating the development of low-cost hydrogen fuel cells and other energy conversion technologies in the future [13].

Zachman, Michael↗

py4DSTEM: A Software Package for Four-Dimensional Scanning Transmission Electron Microscopy Data Analysis

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 two-dimensional (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 that include data wrangling, calibration, analysis, and 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 also implement 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 and help improve the standards for data and computational methods in electron microscopy, and we invite the community to contribute to this ongoing project.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗