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At least 127 records · Page 7

Deep learning for visualization and novelty detection in large X-ray diffraction datasets

Abstract We apply variational autoencoders (VAE) to X-ray diffraction (XRD) data analysis on both simulated and experimental thin-film data. We show that crystal structure representations learned by a VAE reveal latent information, such as the structural similarity of textured diffraction patterns. While other artificial intelligence (AI) agents are effective at classifying XRD data into known phases, a similarly conditioned VAE is uniquely effective at knowing what it doesn’t know: it can rapidly identify data outside the distribution it was trained on, such as novel phases and mixtures. These capabilities demonstrate that a VAE is a valuable AI agent for aiding materials discovery and understanding XRD measurements both ‘on-the-fly’ and during post hoc analysis.

36 MATERIALS SCIENCE↗

Simulation and Emulation of X-Ray Diffraction from Dynamic Compression Experiments

Many important aspects of the dynamic thermo-mechanical response of materials occur at the mesoscale, i.e. a physical scale of interactions smaller than what can be adequately described by homogenous behaviors, yet larger than the scale of the atomic lattice. Concurrent advancements in computational power, continuum theory, and experimental diagnostics are enabling unprecedented understanding of such interactions. However, we cannot develop a sufficient level of confidence in such mesoscale capability until the constitutive description of the underlying constituents is reliably representative of their actual physical behavior. Therefore, there is a strong need to combine experimental, modeling, and data-science techniques to validate models of the thermomechanical response of individual single crystals. One experimental diagnostic with high potential impact to shock physics and materials science is in-situ x-ray diffraction. This paper is primarily focused on simulation of x-ray diffraction in shock physics, but with an aim toward quantifying parametric uncertainty of simulation models. Here, we develop and demonstrate a data-science and model-driven approach to constrain the parameterization of continuum models of crystal lattice deformation associated with the shock response of crystalline materials. The framework is built around the connection between continuum hydrodynamic simulations of lattice deformation and a new Bragg diffraction simulation code, BarberShop. The dynamic deformation of a crystal lattice is modeled using the DiscoFlux model within an arbitrary Lagrangian-Eulerian hydrodynamic code, FLAG. These detailed continuum simulations of lattice deformation can be computationally slow, thus a statistical model is used to emulate the evolution of lattice deformation fields in time and across the considered model parameter space. Emulated lattice deformation fields can then be generated rapidly for any combination of physics model parameters. In turn, these fields can be fed into BarberShop to realize a rapid prediction of Bragg diffraction patterns associated with particular values of physics model parameters. The framework enables parameterization of the single crystal model to obtain Bragg diffraction patterns that most closely resemble a corresponding measurement. Furthermore, the framework naturally provides sensitivities of the lattice deformation to the physics parameters. We highlight the utility of this framework through the application to a synthetic closed-loop inverse problem leading to the parameterization of a single crystal material model. As a model problem, we consider the dynamic response of the energetic molecular crystal, cyclotrimethylenetrinitramine (or RDX), under dynamic compression induced by simulated flyer plate impact experiments.

36 MATERIALS SCIENCE↗

Propagation of acoustic shock waves between parallel rigid boundaries and into shadow zones

The study of acoustic shock propagation in complex environments is of great interest for urban acoustics, but also for source localization, an underlying problematic in military applications. To give a better understanding of the phenomenon taking place during the propagation of acoustic shocks, laboratory-scale experiments and numerical simulations were performed to study the propagation of weak shock waves between parallel rigid boundaries, and into shadow zones created by corners. In particular, this work focuses on the study of the local interactions taking place between incident, reflected, and diffracted waves according to the geometry in both regular or irregular – also called Von Neumann – regimes of reflection. In this latter case, an irregular reflection can lead to the formation of a Mach stem that can modify the spatial distribution of the acoustic pressure. Short duration acoustic shock waves were produced by a 20 kilovolts electric spark source and a schlieren optical method was used to visualize the incident shockfront and the reflection/diffraction patterns. Experimental results are compared to numerical simulations based on the high-order finite difference solution of the two dimensional Navier-Stokes equations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

A Denoising Autoencoder for Improved Kikuchi Pattern Quality and Indexing in Electron Backscatter Diffraction

The rapid collection and indexing of electron diffraction patterns as produced via electron backscatter diffraction (EBSD) has enabled crystallographic orientation and structural determination, as well as additional property-determining strain and dislocation density information with increasing speed, resolution, and efficiency. Pattern indexing quality is reliant on the noise of the collected electron diffraction patterns, which is often convoluted by sample preparation and data collection parameters. EBSD acquisition is sensitive to many factors and thus can result in low confidence index (CI), poor image quality (IQ), and improper minimization of fit, which can result in noisy datasets and misrepresent the microstructure. In an attempt to enable both higher speed EBSD data collection and enable greater orientation fit accuracy with noisy datasets, an image denoising autoencoder was implemented to improve pattern quality. Here, we show that EBSD data processed through the autoencoder results in a higher CI, IQ, and a more accurate degree of fit. In addition, using denoised datasets in HR-EBSD cross correlative strain analysis can result in reduced phantom strain from erroneous calculations due to the increased indexing accuracy and improved correspondence between collected and simulated patterns.

36 MATERIALS SCIENCE↗

Effects of Residual DMSO Adduct on Photonically Cured MAPbI 3 Solar Cells

Defects and impurities in halide perovskite solar cells can negatively impact device performance and long-term stability. In this study, we identify photonically cured methylammonium (MA) lead iodide films contain a residual adduct, MA 2 Pb 3 I 8 (dimethyl sulfoxide) 2 (MA 2 Pb 3 I 8 (DMSO) 2 ), that reduces photocurrent generation in perovskite solar cells (PSCs). This is evidenced by a decrease in the external quantum efficiency (EQE) near 400 nm. Similar EQE reductions were observed in PSCs produced using high-speed processes but have not been thoroughly examined. Through X-ray diffraction patterns and Fourier transform infrared spectroscopy, we establish the photo-inactive MA 2 Pb 3 I 8 (DMSO) 2 as the culprit for the EQE reduction. Combined experimental and simulation results reveal that the MA 2 Pb 3 I 8 (DMSO) 2 phase is located at the hole transport layer/ interface, not on the surface, resulting in lower quantum efficiency and surface photovoltage in the short wavelength region. The residual adduct is kinetically trapped due to the short processing time (20 ms) and crystallization direction but can be removed by an additional photonic pulse. Furthermore, this study highlights the need for careful examination of resulting materials beyond device efficiency when transitioning from laboratory processing to industrial high-speed methods.

14 SOLAR ENERGY↗

Microstructural Evolution and the Precipitation Behavior in X90 Linepipe Steel During Isothermal Processing

Thermomechanical controlled processing of 560-MPa (X90) linepipe steel was simulated in the laboratory using a thermomechanical simulator to study the microstructural evolution and precipitation behavior during isothermal holding. The results indicated that martensite was obtained when the steels were isothermally held for 5 s at 700 °C. Subsequently, granular bainite and acicular ferrite transformation occurred with increased holding time. Different amount of polygonal ferrite formed after isothermally holding for 600-3600 s. Pearlite nucleated after isothermally holding for 3600 s. Precipitation occurred after isothermal holding for 5 s and continuous precipitation occurred at grain boundaries after isothermally holding for 600 s. After isothermally holding for 3600 s, large Nb/Ti carbide precipitated. The presence of MX-type precipitates was confirmed by diffraction pattern. The interphase precipitation (IP) occurred between 5 and 30 s. Maximum hardness was obtained after isothermally holding for 600 s when IP occurred and rapidly decreased to a low value, mainly because polygonal ferrite dominated the microstructure after isothermally holding for 3600 s.

36 MATERIALS SCIENCE↗

Optical Control of Adaptive Nanoscale Domain Networks

Adaptive networks can sense and adjust to dynamic environments to optimize their performance. Understanding their nanoscale responses to external stimuli is essential for applications in nanodevices and neuromorphic computing. However, it is challenging to image such responses on the nanoscale with crystallographic sensitivity. Here, the evolution of nanodomain networks in (PbTiO 3 ) n /(SrTiO 3 ) n superlattices (SLs) is directly visualized in real space as the system adapts to ultrafast repetitive optical excitations that emulate controlled neural inputs. The adaptive response allows the system to explore a wealth of metastable states that are previously inaccessible. Their reconfiguration and competition are quantitatively measured by scanning x-ray nanodiffraction as a function of the number of applied pulses, in which crystallographic characteristics are quantitatively assessed by assorted diffraction patterns using unsupervised machine-learning methods. The corresponding domain boundaries and their connectivity are drastically altered by light, holding promise for light-programable nanocircuits in analogy to neuroplasticity. Phase-field simulations elucidate that the reconfiguration of the domain networks is a result of the interplay between photocarriers and transient lattice temperature. The demonstrated optical control scheme and the uncovered nanoscopic insights open opportunities for the remote control of adaptive nanoscale domain networks.

36 MATERIALS SCIENCE↗

Focusing a round coherent beam by spatial filtering the horizontal source

This paper illustrates the use of spatial filtering with a horizontal slit near the source to enlarge the horizontal coherence in an experimental station and produce a diffraction-limited round focus at an insertion device beamline for X-ray photon correlation spectroscopy experiments. Simple expressions are provided to guide the optical layout, and wave propagation simulations confirm their applicability. The two-dimensional focusing performance of Be compound refractive lenses to produce a round diffraction-limited focus at 11 keV capable of generating a high-contrast speckle pattern of an aerogel sample is demonstrated. The coherent scattering patterns have comparable speckle sizes in both horizontal and vertical directions. The focal spot sizes are consistent with hybrid ray-tracing calculations. Producing a two-dimensional focus on the sample can be helpful to resolve speckle patterns with modern pixel array detectors with high visibility. This scheme has now been in use since 2019 for the 8-ID beamline at the Advanced Photon Source, sharing the undulator beam with two separate beamlines, 8-ID-E and 8-ID-I at 7.35 keV, with increased partially coherent flux, reduced horizontal spot sizes on samples, and good speckle contrast.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Conditional diffusion machine-learning framework for mapping valence electron distribution from convergent beam electron diffraction

Quantitative convergent beam electron diffraction (CBED) enables determination of aspherical valence electron distributions through refinement of low-order structure factors, which are highly sensitive to chemical bonding and charge density variations. However, conventional quantitative CBED (QCBED) requires solving a highly nonlinear inverse problem with many coupled parameters, and computationally intensive dynamical diffraction calculations, making it time-consuming and difficult to apply to complex systems. More broadly, reconstructing charge density and orbital electron distribution from diffraction data has long been a central challenge in both x-ray and electron crystallography. Here, in this study, we introduce an artificial-intelligence (AI)-based framework that replaces traditional refinement with a data-driven inverse solver. Using a large synthetic CBED dataset generated by Bloch-wave simulations, we train a conditional diffusion model to directly infer crystal structural parameters and multipole density formalism parameters, and hence valence electron distributions, from CBED patterns alone. By learning from forward simulations across realistic parameter space, the model effectively solves the inverse problem. Compared with direct regression approaches, the diffusion-based framework provides posterior parameter distributions for rigorous uncertainty quantification while preserving quantitative fidelity and reducing analysis time by orders of magnitude. By eliminating the need for external single-crystal x-ray diffraction data and complex nonlinear refinement, this approach enables practical, high-throughput, and in situ quantitative CBED, enabling real-time mapping of valence electron distributions and their correlation with functional responses in quantum and energy materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Synthesis of polycaprolactone-titanium oxide multilayer films by nanosecond laser pulses and electrospinning technique for better implant fabrication

In this study, a combination of electrospinning and laser texturing is introduced as a novel method for increasing the biocompatibility of metal implants. Besides having a rough laser treated surface, the implant benefits from the high porosity and better wettability of an electrospun fibrous structure, which is a more favorable environment for cell proliferation. Titanium samples were patterned using a nanosecond laser beam and were placed as collectors in an electrospinning machine. They were then soaked in simulated body fluid for four weeks. Energy Dispersive X-ray and X-Ray Diffraction results indicate significantly more hydroxyapatite formation on laser treated samples with nanoscale fibers deposited on their surface. This shows that having a laser treated surface underneath the fibrous layer can improve short-term biocompatibility even before degradation of fibers. The thermal conductivity of the electrospun layer, measured using a Hot Disk Transient Plane Source instrument and computer code, was shown to be considerably lower than that of titanium and very close to bone. The presence of this layer can therefore be beneficial in making the implant more compatible to a biological medium. In case of dental implants, it was shown that this layer can act as a thermal barrier while a hot beverage is consumed and it can decrease the temperature rise by about 60%, which avoids any possible damage to newly formed cells during the healing period.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Morphology, Deformations, and Photocatalytic Activity of Thermally Treated Brookite Titanium Dioxide Thin Films

Metastable states, in which the coupling between long-range lattice deformations and electronic properties can be controlled, provide a pathway to tailoring the behavior of photocatalytic materials by directing the flow of photoinduced charge carriers. Brookite is a metastable polymorph of earth-abundant TiO 2 that exhibits photocatalytic function and, due to its high energy relative to the anatase and rutile polymorphs, may serve as a precursor for the formation of transitional metastable structures. In this work, facile thermal annealing is employed to promote the formation of predominantly brookite-phase films, regulate the brookite lattice distortions, and determine the effect of these distortions on charge separation, ultimately directed at enhancing photocatalytic activity. Profile fitting of X-ray diffraction patterns and peak shifts in Raman spectra revealed structural distortions of the brookite lattice. Structural defects, including lattice gliding, dislocations, stacking faults, and twin boundaries, were observed using scanning transmission electron microscopy. First-principles simulations reveal how the lattice distortions associated with stacking faults induce band bending, thus increasing the photocatalytic activity of brookite. In conclusion, this study provides insight into the microstructural tuning of metastable phases to enhance their unique functionalities.

band bending↗

Single-view phase retrieval of an extended sample by exploiting edge detection and sparsity

We propose a new approach to robustly retrieve the exit wave of an extended sample from its coherent diffraction pattern by exploiting sparsity of the sample's edges. This approach enables imaging of an extended sample with a single view, without ptychography. We introduce nonlinear optimization methods that promote sparsity, and we derive update rules to robustly recover the sample's exit wave. We test these methods on simulated samples by varying the sparsity of the edge-detected representation of the exit wave. Finally, our tests illustrate the strengths and limitations of the proposed method in imaging extended samples.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

X-ray diffraction from nonuniformly stretched helical molecules

The fibrous proteins in living cells are exposed to mechanical forces interacting with other subcellular structures. X-ray fiber diffraction is often used to assess deformation and movement of these proteins, but the analysis has been limited to the theory for fibrous molecular systems that exhibit helical symmetry. However, this approach cannot adequately interpret X-ray data from fibrous protein assemblies where the local strain varies along the fiber length owing to interactions of its molecular constituents with their binding partners. To resolve this problem a theoretical formulism has been developed for predicting the diffraction from individual helical molecular structures nonuniformly strained along their lengths. This represents a critical first step towards modeling complex dynamical systems consisting of multiple helical structures using spatially explicit, multi-scale Monte Carlo simulations where predictions are compared with experimental data in a `forward' process to iteratively generate ever more realistic models. Here the effects of nonuniform strains and the helix length on the resulting magnitude and phase of diffraction patterns are quantitatively assessed. Examples of the predicted diffraction patterns of nonuniformly deformed double-stranded DNA and actin filaments in contracting muscle are presented to demonstrate the feasibly of this theoretical approach.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Examining Infrasound Propagation at High Spatial Resolution Using a Nodal Seismic Array

Infrasound—acoustic waves in the atmosphere below 20 Hz—is a useful monitoring tool. Topography and atmospheric structure strongly control infrasound propagation, and at common source–receiver distances neither of these effects can be ignored when quantitative source constraints are sought. Detailed spatial measurements of the infrasound wavefield would inform propagation models and improve source estimates. However, the “large-N” deployment strategy now well-known in seismology has not yet been realized for infrasound studies. Here, we use the 900-node seismic array from the 2014 Imaging Magma Under St. Helens (iMUSH) experiment as a proxy for a large-N infrasound network, by leveraging acoustic–seismic coupled arrivals. The active-source component of iMUSH consisted of 23 shallowly buried explosions around Mount Saint Helens volcano; these explosions produced epicentral infrasound recorded on the nodes. We find that the bulk presence of ground-coupled infrasound on the nodes is controlled by wind noise and source–receiver distance, with observed arrivals for eight explosions. Explosions with the most extensive coupling produce complex spatial waveform patterns across the array. These patterns are related to both topographic and atmospheric propagation effects, as well as spatially variable site (coupling) effects. We compare our observations to simple topographic diffraction and high-resolution wind advection models, and full-wave numerical simulations. We find strong spatial correlations between (a) coupled arrival strength and modeled topographic obstruction and (b) coupled arrival time and along-path winds. Our seismoacoustic analyses and results are applicable to other existing and future nodal seismic data sets and can expand the utility of such deployments.

58 GEOSCIENCES↗

Atomic resolution convergent beam electron diffraction analysis using convolutional neural networks

Two types of convolutional neural network (CNN) models, a discrete classification network and a continuous regression network, were trained to determine local sample thickness from convergent beam diffraction (CBED) patterns of SrTiO 3 collected in a scanning transmission electron microscope (STEM) at atomic column resolution. Acquisition of atomic resolution CBED patterns for this purpose requires careful balancing of CBED feature size in pixels, acquisition speed, and detector dynamic range. The training datasets were derived from multislice simulations, which must be convolved with incoherent source broadening. Sample thicknesses were also determined using quantitative high-angle annular dark-field (HAADF) STEM images acquired simultaneously. The regression CNN performed well on sample thinner than 35 nm, with 70% of the CNN results within 1 nm of HAADF thickness, and 1.0 nm overall root mean square error between the two measurements. The classification CNN was trained for a thicknesses up to 100 nm and yielded 66% of CNN results within one classification increment of 2 nm of HAADF thickness. Our approach depends on methods from computer vision including transfer learning and image augmentation.

36 MATERIALS SCIENCE↗

Microstructure of Friction Stir Welded AlSi9Mg Cast with 5083 and 2017A Wrought Aluminum Alloys

Wrought aluminum alloys 5083 and 2017A were each joined with cast aluminum alloy AlSi9Mg through friction stir welding in butt weld configurations. For each material system, the wrought and cast alloy positions, i.e., the advancing side or the retreating side, were exchanged between welding trials. The produced weldments were free from cracks and discontinuities. For each alloy configuration, a well-defined nugget comprised of alternating bands of the welded alloys characterized the microstructure. The degree of mixing, however, strongly depended on which wrought alloy was present and on its position during processing. In all cases, the cast AlSi9Mg alloy dominated the weld center regardless of its position during welding. Electron backscattered diffraction analysis showed that the grain size in both alloys (bands) constituting the nugget was similar and that the majority of grain boundaries exhibited a high angle character (20°-60°). Regardless of the alloy, however, all grains were elongated along the direction of the material plastic flow during welding. A numerical simulation of the joining process visualized the material flow patterns and temperature distribution and helped to rationalize the microstructural observations. The hardness profiles across the weld reflected the microstructure formed during welding and correlated well with the temperature changes predicted by the numerical model. Tensile specimens consistently fractured in the cast alloy near the weld nugget.

36 MATERIALS SCIENCE↗

Anisotropy effects on the kinetics of colloidal crystallization and melting: comparison of spheres and ellipsoids

We use alternating current (AC) electric field assisted self-assembly to produce two-dimensional, millimeter scale arrays of ellipsoidal colloids and study the kinetics of their phase reconfiguration by means of confocal microscopy, light scattering, and computer simulation. We find that the kinetics of orientational and positional ordering can be manipulated by changing the shape of the colloids: ellipsoids with aspect ratio 2.0 melt into disordered structures 5.7 times faster compared to spheres. On the other hand, ellipsoids self-assemble into ordered crystals at a similar rate to spheres. Confocal microscopy is used to directly visualize defects in the self-assembled structures. Small-angle light scattering (SALS) quantifies the light diffraction response, which is sensitive to the kinetics of positional and orientational ordering in the self-assembled anisotropic structures. We find three different light diffraction patterns: a phase with high orientational order (with chain-like structure in real space), a phase with high positional and orientational order (characteristic of a close-packed structure), and a phase that is disordered in position but with intermediate orientational order. The large influence of aspect ratio on the kinetics of the positionally and orientationally ordered phase is explored through simulation; it is found that the number of particle degrees of freedom controls the difference between the melting rates of the ellipsoids and spheres. This research contributes to the understanding of reconfiguration kinetics and optical properties of colloidal crystals produced from anisotropic colloids.

36 MATERIALS SCIENCE↗