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Results for “diffraction pattern simulation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Structural and vibrational study of 2-MethoxyEthylAmmonium Nitrate (2-OMeEAN): Interpretation of experimental results with ab initio molecular dynamics

In this work we report an analysis of the bulk phase of 2-methoxyethylammonium nitrate based on ab initio molecular dynamics. The structural and dynamical features of the ionic liquid have been characterized and the computational findings have been compared with the experimental X-ray diffraction patterns, with infrared spectroscopy data, and with the results obtained from molecular dynamics simulations. The experimental infrared spectrum was interpreted with the support of calculated vibrational density of states as well as harmonic frequency calculations of selected gas phase clusters. Particular attention was addressed to the high frequency region of the cation (ω > 2000 cm{sup −1}), where the vibrational motions involve the NH{sub 3}+ group responsible for hydrogen bond formation, and to the frequency range 1200-1400 cm{sup −1} where the antisymmetric stretching mode (ν{sub 3}) of nitrate is found. Its multiple absorption lines in the liquid arise from the removal of the degeneracy present in the D{sub 3h} symmetry of the isolated ion. Our ab initio molecular dynamics leads to a rationalization of the frequency shifts and splittings, which are inextricably related to the structural modifications induced by a hydrogen bonding environment. The DFT calculations lead to an inhomogeneous environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Influence of molecular clustering on the interpretation of diffractograms of hydrocarbon films from tokamak T-10

The influence of molecular clustering on the formerly suggested interpretation of diffraction patterns of hydrocarbon films formed in the vacuum vessel of the tokamak T-10 is analyzed numerically. The simulation of clustering of simple hydrocarbon molecules C(D, H){sub 4}, C{sub 2}(D, H){sub 4}, and C{sub 6}(D, H){sub 6} and molecules composed of curved graphene (fullerenes and toroidal nanotubes) is carried out with the rigid body molecular dynamics method. It is shown that formerly neglected atomic correlations C–C and C–D(H) in the amorphous hydrocarbon component decrease the calculated values of the scattered intensity in the range of scattering vector modulus 5 < q < 20 nm{sup –1} because of homogenization of scatters on the spatial scale of ∼1 nm. The allowance for these correlations does not change the diffraction patterns in the range q > 20 nm{sup –1}. The results suggest the necessity to introduce to the procedure of determining the structural content of the films, similar to those from the tokamak T-10, the clusters formed by the van der Waals adhesion of hydrocarbon molecules to “graphene” nanoparticles. This simplifies the mathematical optimization to the former level of complexity—but for an extended ensemble of objects—and makes it possible to calculate the diffraction patterns of these objects using the distributed computing resources. A modified algorithm of structural content identification on the basis of joint X-ray and neutron diffractometry is suggested.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Optimizing disk registration algorithms for nanobeam electron diffraction strain mapping

Scanning nanobeam electron diffraction strain mapping is a technique by which the positions of diffracted disks sampled at the nanoscale over a crystalline sample can be used to reconstruct a strain map over a large area. However, it is important that the disk positions are measured accurately, as their positions relative to a reference are directly used to calculate strain. Here in this study, we compare several correlation methods using both simulated and experimental data in order to directly probe susceptibility to measurement error due to non-uniform diffracted disk illumination structure. We found that prefiltering the diffraction patterns with a Sobel filter before performing cross correlation or performing a square-root magnitude weighted phase correlation returned the best results when inner disk structure was present. Lastly, we have tested these methods both on simulated datasets, and experimental data from unstrained silicon as well as a twin grain boundary in 304 stainless steel.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Iron Carbides in Fischer–Tropsch Synthesis: Theoretical and Experimental Understanding in Epsilon-Iron Carbide Phase Assignment

As active phases in low-temperature Fischer–Tropsch synthesis for liquid fuel production, epsilon iron carbides are critically important industrial materials. However, the precise atomic structure of epsilon iron carbides remains unclear, leading to a half-century of debate on the phase assignment of the ε-Fe 2 C and ε’-Fe 2.2 C. Here, we resolve this decades-long question by a combining theoretical and experimental investigation to assign the phases unambiguously. First, we have investigated the equilibrium structures and thermal stabilities of ε-Fe x C, (x = 1, 2, 2.2, 3, 4, 6, 8) by first-principles calculations. We have also acquired X-ray diffraction patterns and Mössbauer spectra for these epsilon iron carbides, and compared them with the simulated results. These analyses indicate that the unit cell of ε-Fe 2 C contains only one type of chemical environment for Fe atoms, while ε’-Fe 2.2 C has six sets of chemically distinct Fe atoms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Three-dimensional imaging using coherent x rays at grazing incidence geometry

We have developed a three-dimensional coherent diffraction imaging algorithm to retrieve phases of diffraction patterns of samples in grazing incidence small angle x-ray scattering experiments. The algorithm interprets the diffraction patterns using the distorted-wave Born approximation instead of the Born approximation, as in this case, the existence of a reflected beam from the substrate causes the diffraction pattern to deviate significantly from the simple Fourier transform of the object. Detailed computer simulations show that the algorithm works. Verification with real experiments is planned.

Yang, Yi↗
DOE OSTI2025

Broadband unidirectional visible imaging using wafer-scale nano-fabrication of multi-layer diffractive optical processors

We present a broadband and polarization-insensitive unidirectional imager that operates at the visible part of the spectrum, where image formation occurs in one direction, while in the opposite direction, it is blocked. This approach is enabled by deep learning-driven diffractive optical design with wafer-scale nano-fabrication using high-purity fused silica to ensure optical transparency and thermal stability. Our design achieves unidirectional imaging across three visible wavelengths (covering red, green, and blue parts of the spectrum), and we experimentally validated this broadband unidirectional imager by creating high-fidelity images in the forward direction and generating weak, distorted output patterns in the backward direction, in alignment with our numerical simulations. This work demonstrates wafer-scale production of diffractive optical processors, featuring 16 levels of nanoscale phase features distributed across two axially aligned diffractive layers for visible unidirectional imaging. This approach facilitates mass-scale production of ~0.5 billion nanoscale phase features per wafer, supporting high-throughput manufacturing of hundreds to thousands of multi-layer diffractive processors suitable for large apertures and parallel processing of multiple tasks. Beyond broadband unidirectional imaging in the visible spectrum, this study establishes a pathway for artificial-intelligence-enabled diffractive optics with versatile applications, signaling a new era in optical device functionality with industrial-level, massively scalable fabrication.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Thrust #3: Simulation and Measurement of Bio-enabled Lensless Infrared Focusing and Spatially-Controlled Multiwavelength IR reflection

The path to quantitatively characterizing the possible impact of refractive index on the focusing behavior of C. Wailesii diatoms frustules and their replicas first required the careful design of a control experiment that allowed for judicious control of both material and hole pattern variables, which are thought to govern focusing behavior. This was accomplished by FIB-milling synthetic hole patterns with well-controlled dimensions into thin SiO 2 and MgO/Si films. In doing so, an ideal sample is obtained whose pore patterns can be simulated via beam propagation methods and whose relatively simple interference patterns can be measured by far-field diffraction imaging. Together with refractive index measurements via ellipsometry on SiO 2 and MgO/Si thin films, these results showed that the focusing distances at six different wavelengths over 550-1050 nm are strongly correlated with the dispersion of the refractive index in this range. Although assertions have been made to this point in the literature, the results given here presented an improved characterization of the role that refractive index played in focusing behavior in a “diatom-like” hole pattern that otherwise is missing in earlier works due to inadequate characterization of refractive index dispersion and poor control over hole pattern dimensions. These results provided a reference point with which measurements and simulations of diatoms and their replicas can be compared.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Identifying point defects and ordering in the high-entropy layered oxide Li 1.5 MO 3-δ (M=Mn, Al, Fe, Co, Ni) for energy storage applications

High-entropy layered oxides (HELOs) represent a very promising class of next-generation battery cathodes, combining the well-studied properties of layered cathode materials such as LiCoO 2 with the chemical tunability and stability of high-entropy materials. HELO materials often form particles with complex defects and domain structures, complicating accurate characterization of structure and cation mixing. Understanding disorder and order in HELO materials is necessary for understanding their performance and utility as cathodes. Here we demonstrate the characterization of the HELO Li 1.5 MO 3-δ (M = Mn, Al, Fe, Co, Ni), wherein X-ray powder diffraction, transmission electron microscopy imaging, and electron diffraction patterns are analyzed to reveal the presence of ordering in the HELO, with imaging and diffraction simulation employed to compare experimental results to atomic modeling. Finally, without rigorous characterization at the atomic scale, important features such as defect ordering can be easily overlooked and therefore remain unconsidered when interpreting experimental results.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Release dynamics of nanodiamonds created by laser-driven shock-compression of polyethylene terephthalate

Laser-driven dynamic compression experiments of plastic materials have found surprisingly fast formation of nanodiamonds (ND) via X-ray probing. This mechanism is relevant for planetary models, but could also open efficient synthesis routes for tailored NDs. We investigate the release mechanics of compressed NDs by molecular dynamics simulation of the isotropic expansion of finite size diamond from different P-T states. Analysing the structural integrity along different release paths via molecular dynamic simulations, we found substantial disintegration rates upon shock release, increasing with the on-Hugnoiot shock temperature. We also find that recrystallization can occur after the expansion and hence during the release, depending on subsequent cooling mechanisms. Our study suggests higher ND recovery rates from off-Hugoniot states, e.g., via double-shocks, due to faster cooling. Laser-driven shock compression experiments of polyethylene terephthalate (PET) samples with in situ X-ray probing at the simulated conditions found diamond signal that persists up to 11 ns after breakout. In the diffraction pattern, we observed peak shifts, which we attribute to thermal expansion of the NDs and thus a total release of pressure, which indicates the stability of the released NDs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Integrated analysis of X-ray diffraction patterns and pair distribution functions for machine-learned phase identification

Abstract To bolster the accuracy of existing methods for automated phase identification from X-ray diffraction (XRD) patterns, we introduce a machine learning approach that uses a dual representation whereby XRD patterns are augmented with simulated pair distribution functions (PDFs). A convolutional neural network is trained directly on XRD patterns calculated using physics-informed data augmentation, which accounts for experimental artifacts such as lattice strain and crystallographic texture. A second network is trained on PDFs generated via Fourier transform of the augmented XRD patterns. At inference, these networks classify unknown samples by aggregating their predictions in a confidence-weighted sum. We show that such an integrated approach to phase identification provides enhanced accuracy by leveraging the benefits of each model’s input representation. Whereas networks trained on XRD patterns provide a reciprocal space representation and can effectively distinguish large diffraction peaks in multi-phase samples, networks trained on PDFs provide a real space representation and perform better when peaks with low intensity become important. These findings underscore the importance of using diverse input representations for machine learning models in materials science and point to new avenues for automating multi-modal characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2016

A comprehensive simulation model of the performance of photochromic films in absorbance-modulation-optical-lithography

Optical lithography is the most prevalent method of fabricating micro-and nano-scale structures in the semiconductor industry due to the fact that patterning using photons is fast, accurate and provides high throughput. However, the resolution of this technique is inherently limited by the physical phenomenon of diffraction. Absorbance-Modulation-Optical Lithography (AMOL), a recently developed technique has been successfully demonstrated to be able to circumvent this diffraction limit. AMOL employs a dual-wavelength exposure system in conjunction with spectrally selective reversible photo-transitions in thin films of photochromic molecules to achieve patterning of features with sizes beyond the far-field diffraction limit. We have developed a finite-element-method based full-electromagnetic-wave solution model that simulates the photo-chemical processes that occur within the thin film of the photochromic molecules under illumination by the exposure and confining wavelengths in AMOL. This model allows us to understand how the material characteristics influence the confinement to sub-diffraction dimensions, of the transmitted point spread function (PSF) of the exposure wavelength inside the recording medium. The model reported here provides the most comprehensive analysis of the AMOL process to-date, and the results show that the most important factors that govern the process, are the polarization of the two beams, the ratio of the intensities of the two wavelengths, the relative absorption coefficients and the concentration of the photochromic species, the thickness of the photochromic layer and the quantum yields of the photoreactions at the two wavelengths. The aim of this work is to elucidate the requirements of AMOL in successfully circumventing the far-field diffraction limit.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Comprehensive characterization of ball-milled powders simulating a tribofilm system

A model system was used to simulate the properties of tribofilms which form during automotive braking. The model system was prepared by ball milling of a blend of 70 vol.% iron oxides, 15 vol.% molybdenum disulfide and 15 vol.% graphite. The resulting mixture was characterized by X-ray powder diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and various transmission electron microscopic (TEM) methods, including energy dispersive X-ray spectroscopy (EDXS), high resolution investigations (HRTEM) with corresponding simulation of the HRTEM images, diffraction methods such as scanning nano-beam electron diffraction (SNBED) and selected area electron diffraction (SAED). It could be shown that the ball milling caused a reduction of the grain size of the initial components to the nanometer range. Sometimes even amorphization or partial break-down of the crystal structure was observed for MoS{sub 2} and graphite. Moreover, chemical reactions lead to a formation of surface coverings of the nanoparticles by amorphous material, molybdenum oxides, and iron sulfates as derived from XPS. - Highlights: • Ball milling of iron oxides, MoS{sub 2}, and graphite to simulate a tribofilm • Increasing coefficient of friction after ball milling of the model blend • Drastically change of the diffraction pattern of the powder mixture • TEM & XPS showed the components of the milled mixture and the process during milling. • MoS{sub 2} and graphite suffered a loss in translation symmetry or became amorphous.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Simulations of X-ray diffraction of shock-compressed single-crystal tantalum with synchrotron undulator sources

Polychromatic synchrotron undulator X-ray sources are useful for ultrafast single-crystal diffraction under shock compression. Here, simulations of X-ray diffraction of shock-compressed single-crystal tantalum with realistic undulator sources are reported, based on large-scale molecular dynamics simulations. Purely elastic deformation, elastic–plastic two-wave structure, and severe plastic deformation under different impact velocities are explored, as well as an edge release case. Transmission-mode diffraction simulations consider crystallographic orientation, loading direction, incident beam direction, X-ray spectrum bandwidth and realistic detector size. Diffraction patterns and reciprocal space nodes are obtained from atomic configurations for different loading (elastic and plastic) and detection conditions, and interpretation of the diffraction patterns is discussed.

Tang, M. X.↗
DOE OSTI2015

Tailored multivariate analysis for modulated enhanced diffraction

Modulated enhanced diffraction (MED) is a technique allowing the dynamic structural characterization of crystalline materials subjected to an external stimulus, which is particularly suited for in situ and operando structural investigations at synchrotron sources. Contributions from the (active) part of the crystal system that varies synchronously with the stimulus can be extracted by an offline analysis, which can only be applied in the case of periodic stimuli and linear system responses. In this paper a new decomposition approach based on multivariate analysis is proposed. The standard principal component analysis (PCA) is adapted to treat MED data: specific figures of merit based on their scores and loadings are found, and the directions of the principal components obtained by PCA are modified to maximize such figures of merit. As a result, a general method to decompose MED data, called optimum constrained components rotation (OCCR), is developed, which produces very precise results on simulated data, even in the case of nonperiodic stimuli and/or nonlinear responses. The multivariate analysis approach is able to supply in one shot both the diffraction pattern related to the active atoms (through the OCCR loadings) and the time dependence of the system response (through the OCCR scores). When applied to real data, OCCR was able to supply only the latter information, as the former was hindered by changes in abundances of different crystal phases, which occurred besides structural variations in the specific case considered. To develop a decomposition procedure able to cope with this combined effect represents the next challenge in MED analysis.

Caliandro, Rocco↗
DOE OSTI2018

Welding Defect Classification from Simulated Ultrasonic Signals

Nondestructive testing is widely used to detect and to size up discontinuities embedded in a material. Among the several ultrasonic techniques, time of flight diffraction (TOFD) combines high speed inspection, high sizing reliability and low rate of incorrect results. However, the classification of defects through ultrasound signals acquired by the TOFD technique depends heavily on the knowledge and experience of the operator and thus, this classification is still frequently questioned. Besides, this task requires long processing time due to the large amount of data to be analyzed. Nevertheless, computational tools for pattern recognition can be employed to analyze a high amount of data with large efficiency. In the present work, simulation of ultrasound propagation in two-dimensional media containing, each one, different kinds of modeled discontinuities which mimic defects in welded joints were performed. Clustering (k-means) and classification (principal component analysis and k-nearest neighbors) algorithms were employed to associate each simulated ultrasound signal with its corresponding modeled defects. The results for each method were analyzed, discussed and compared. The results are very promising.

42 ENGINEERING↗
DOE OSTI2024

Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks

As an optical processor, a diffractive deep neural network (D2NN) utilizes engineered diffractive surfaces designed through machine learning to perform all-optical information processing, completing its tasks at the speed of light propagation through thin optical layers. With sufficient degrees of freedom, D2NNs can perform arbitrary complex-valued linear transformations using spatially coherent light. Similarly, D2NNs can also perform arbitrary linear intensity transformations with spatially incoherent illumination; however, under spatially incoherent light, these transformations are nonnegative, acting on diffraction-limited optical intensity patterns at the input field of view. Here, we expand the use of spatially incoherent D2NNs to complex-valued information processing for executing arbitrary complex-valued linear transformations using spatially incoherent light. Through simulations, we show that as the number of optimized diffractive features increases beyond a threshold dictated by the multiplication of the input and output space-bandwidth products, a spatially incoherent diffractive visual processor can approximate any complex-valued linear transformation and be used for all-optical image encryption using incoherent illumination. The findings are important for the all-optical processing of information under natural light using various forms of diffractive surface-based optical processors.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Computational approaches to model X-ray photon correlation spectroscopy from molecular dynamics

X-ray photon correlation spectroscopy (XPCS) allows for the resolution of dynamic processes within a material across a wide range of length and time scales. X-ray speckle visibility spectroscopy (XSVS) is a related method that uses a single diffraction pattern to probe ultrafast dynamics. Interpretation of the XPCS and XSVS data in terms of underlying physical processes is necessary to establish the connection between the macroscopic responses and the microstructural dynamics. To aid the interpretation of the XPCS and XSVS data, we present a computational framework to model these experiments by computing the X-ray scattering intensity directly from the atomic positions obtained from molecular dynamics simulations. We compare the efficiency and accuracy of two alternative computational methods: the direct method computing the intensity at each diffraction vector separately, and a method based on fast Fourier transform that computes the intensities at all diffraction vectors at once. Furthermore, the computed X-ray speckle patterns capture the density fluctuations over a range of length and time scales and are shown to reproduce the known properties and relations of experimental XPCS and XSVS for liquids.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Learning neural representations for X-ray ptychography reconstruction with unknown probes

X-ray ptychography provides exceptional nanoscale resolution and is widely applied in materials science, biology, and nanotechnology. However, its full potential is constrained by the critical challenge of accurately reconstructing images when the illuminating probe is unknown. Conventional iterative methods and deep learning approaches are often suboptimal, particularly under the low-signal conditions inherent to low-dose and high-speed experiments. These limitations compromise reconstruction fidelity and restrict the broader adoption of the technique. In this work, we introduce the Ptychographic Implicit Neural Representation (PtyINR), a self-supervised framework that simultaneously addresses the object- and probe-recovery problem. By parameterizing both as continuous neural representations, PtyINR performs end-to-end reconstruction directly from raw diffraction patterns without requiring any pre-characterization of the probe. Extensive evaluations demonstrate that PtyINR achieves superior reconstruction quality on both simulated and experimental data, with remarkable robustness under challenging low-signal conditions. Furthermore, PtyINR offers a generalizable, physics-informed framework for addressing probe-dependent inverse problems, making it applicable to a wide range of computational microscopy problems.

36 MATERIALS SCIENCE↗