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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine learning for domain transfer between simulated and experimental 2D X-ray diffraction patterns using generative adversarial networks

X-ray diffraction (XRD) is a well-established technique for analyzing materials at an atomic level. Dynamic compression experiments (DCE), in which materials are subject to extreme pressures, can provide fundamental understanding to pressure-induced phase transitions and compression of the crystal lattice. The analysis of XRD patterns from highly compressed samples is non-trivial given the sparsity of data, high experimental costs, and the fact that the data is often marred with X-ray background and other artifacts. While accurate computational frameworks exist, they solve the forward problem—from structures and orientations to XRD patterns. Solving the inverse problem for 2D experimental diffraction patterns is currently a complex manual process of matching and comparing experimentally observed patterns to computationally generated ones. Machine learning is a promising tool for automating the matching process but often requires data-intensive architectures. Here, in this study, we use a CycleGAN to translate the domain of limited experimental data to a domain in which there is readily available simulated data. This domain shift allows data-intensive machine learning models that have only been trained on simulated XRD patterns to be used in the analysis of experiments.

Brozak, Samantha Jean [Sandia National Laboratorie↗
DOE OSTI2025

X-ray and neutron diffraction patterns of the AlCrTiV high-entropy alloy and quaternary Heusler structures

The quaternary alloy AlCrTiV has been proposed as both a lightweight high-entropy alloy and also a functional spin filter material based on the Heusler structure. Experimental investigations to-date, based on X-ray diffraction, offer conflicting interpretations of the structure. Here we simulate diffraction patterns of the various proposed structures to show that neutron diffraction, in particular, can reveal the nature of long-range chemical order and discriminate among distributions of the refractory transition metals. Magnetic contributions to the neutron diffraction are also discussed.

Kalliney, Nedjma [Carnegie Mellon Univ., Pittsburg↗
DOE OSTI2022

Extracting information from X-ray diffraction patterns containing Laue oscillations

The presence of Laue oscillations in a film grown on a solid surface is broadly taken as indicating a high quality, crystallographically aligned film of the targeted compound. In this paper we briefly review the origins of both Laue oscillations and Kiessig fringes and show how they can be used together to determine if extra thickness exists above or below the coherently diffracting domains. The differences between experimental and “ideal” films are discussed and the effect of structural features (roughness, different thickness coherently diffracting domains and thickness in addition to the coherently diffracting domains) are illustrated with experimental and simulated data for metal and mixed-metal chalcogenide films of titanium, bismuth, vanadium/iron, and bismuth/molybdenum. Examples are given showing how quantitative information can be extracted from experimental diffraction patterns.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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 OSTI2021

Automated prediction of lattice parameters from X-ray powder diffraction patterns

A key step in the analysis of powder X-ray diffraction (PXRD) data is the accurate determination of unit-cell lattice parameters. This step often requires significant human intervention and is a bottleneck that hinders efforts towards automated analysis. This work develops a series of one-dimensional convolutional neural networks (1D-CNNs) trained to provide lattice parameter estimates for each crystal system. A mean absolute percentage error of approximately 10% is achieved for each crystal system, which corresponds to a 100- to 1000-fold reduction in lattice parameter search space volume. The models learn from nearly one million crystal structures contained within the Inorganic Crystal Structure Database and the Cambridge Structural Database and, due to the nature of these two complimentary databases, the models generalize well across chemistries. A key component of this work is a systematic analysis of the effect of different realistic experimental non-idealities on model performance. It is found that the addition of impurity phases, baseline noise and peak broadening present the greatest challenges to learning, while zero-offset error and random intensity modulations have little effect. However, appropriate data modification schemes can be used to bolster model performance and yield reasonable predictions, even for data which simulate realistic experimental non-idealities. In order to obtain accurate results, a new approach is introduced which uses the initial machine learning estimates with existing iterative whole-pattern refinement schemes to tackle automated unit-cell solution.

42 ENGINEERING↗
DOE OSTI2024

Bragg Spot Finder (BSF): a new machine-learning-aided approach to deal with spot finding for rapidly filtering diffraction pattern images

Macromolecular crystallography contributes significantly to understanding diseases and, more importantly, how to treat them by providing atomic resolution 3D structures of proteins. This is achieved by collecting X-ray diffraction images of protein crystals from important biological pathways. Spotfinders are used to detect the presence of crystals with usable data, and the spots from such crystals are the primary data used to solve the relevant structures. Having fast and accurate spot finding is essential, but recent advances in synchrotron beamlines used to generate X-ray diffraction images have brought us to the limits of what the best existing spotfinders can do. This bottleneck must be removed so spotfinder software can keep pace with the X-ray beamline hardware improvements and be able to see the weak or diffuse spots required to solve the most challenging problems encountered when working with diffraction images. In this paper, we first present Bragg Spot Detection (BSD), a large benchmark Bragg spot image dataset that contains 304 images with more than 66 000 spots. We then discuss the open source extensible U-Net-based spotfinder Bragg Spot Finder (BSF), with image pre-processing, a U-Net segmentation backbone, and post-processing that includes artifact removal and watershed segmentation. Finally, we perform experiments on the BSD benchmark and obtain results that are (in terms of accuracy) comparable to or better than those obtained with two popular spotfinder software packages ( Dozor and DIALS ), demonstrating that this is an appropriate framework to support future extensions and improvements.

36 MATERIALS SCIENCE↗
DOE OSTI2020

DFT simulation of the X-ray diffraction pattern of aluminum-ion-intercalated graphite used as the cathode material of the aluminum-ion battery

Many breakthroughs have been achieved in rechargeable aluminum-ion battery technologies in recent years. Most recently, operando X-ray diffraction (XRD) combined with density functional theory (DFT) calculations was reported to study the chloroaluminate anion (AlCl 4 -)-intercalated graphite cathode of the battery. However, there are quite a few discrepancies between the measured and simulated XRD patterns. This work is focused on the simulation of XRD patterns of graphite intercalation compounds (GICs) with DFT calculations. Our results reveal that both the geometry of AlCl 4 - in graphite and the gallery height of GICs are dependent on the intercalant density. At low intercalant density, the gallery height keeps constant, but at high intercalant densities, the gallery height is linearly related to the intercalant density. Our simulated XRD patterns are highly consistent with the measured operando XRD patterns. Not only do the angles of the peaks match very well, but also the relative intensities and the corresponding electrode capacities show reasonable agreement with the experimental results. The DFT simulation of the XRD pattern provides significant information on the stage index and the charge capacity of the GIC electrode.

25 ENERGY STORAGE↗
NASA NTRS2013

The First X-ray Diffraction Patterns of Clay Minerals from Gale Crater

The Mars Science Laboratory (MSL) Rover, Curiosity spent approx 150 sols at Yellowknife Bay (YKB) studying a section of fluvio-lacustrine sedimentary rocks (with potential indications of volcanic influence), informally known as the Yellowknife Bay formation. YKB lies in a distal region of the Peace Vallis alluvial fan, which extends from the northern rim of Gale Crater toward the dune field at the base of Mt Sharp. Sedimentological and stratigraphic observations are consistent with the Yellowknife Bay formation being part of a distal fan deposit, which could be as young as middle Hesperian to even early Amazonian in age (approx 3.5 to 2.5 Ga). The Yellowknife Bay formation hosts a unit of mudstone called the Sheepbed member. Curiosity obtained powdered rock samples from two drill holes in the Sheepbed Member, named John Klein and Cumberland, and delivered them to instruments in Curiosity. Data from CheMin, a combined X-ray diffraction (XRD)/X-ray fluorescence instrument (XRF), has allowed detailed mineralogical analysis of mudstone powders revealing a clay mineral component of approx 20 wt.% in each sample. The clay minerals are important indicators of paleoenvironmental conditions and sensitive recorders of post-depositional alteration processes. The XRD pattern of John Klein reveals a 021 band consistent with a trioctahedral phyllosilicate. A broad peak at approx 10A with a slight inflexion at approx 12A indicates the presence of 2:1 type clay minerals in the John Klein sample. The trioctahedral nature of the clay minerals, breadth of the basal reflection, and presence of a minor component with larger basal spacing suggests that John Klein contains a trioctahedral smectite (probably saponite), whose interlayer is largely collapsed because of the low-humidity conditions. The XRD patterns show no evidence of corrensite (mixed-layer chlorite/smectite) or chlorite, which are typical diagenetic products of trioctahedral smectites when subjected to burial and heating >60degC in the presence of water. Given estimated geothermal gradients on Mars temperatures <60 degC might still be consistent with (but do not require) moderate burial. However, our ability to identify interstratified minerals is greatly limited by the lack of access to traditional treatments methods used in the lab (e.g., ethylene glycol solvation). Our preferred explanation for the origin of trioctahedral smectites in Sheepbed mudstone is in situ production via reaction of olivine, water and Si-bearing amorphous material, an important mudstone component detected by XRD. Elevated levels of magnetite in the Sheepbed and the trioctahedral monomineralic nature of the clay minerals support this model. These observations, combined with previous studies of olivine stability, support the persistence of circum-neutral hydrous conditions for thousands of years at YKB.

Bristow, Thomas↗
DOE OSTI2022

Towards a machine-readable literature: finding relevant papers based on an uploaded powder diffraction pattern

A prototype application for machine-readable literature is investigated. The program is called pyDataRecognition and serves as an example of a data-driven literature search, where the literature search query is an experimental data set provided by the user. The user uploads a powder pattern together with the radiation wavelength. The program compares the user data to a database of existing powder patterns associated with published papers and produces a rank ordered according to their similarity score. The program returns the digital object identifier and full reference of top-ranked papers together with a stack plot of the user data alongside the top-five database entries. The paper describes the approach and explores successes and challenges.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1981

Real-time observation of X-ray diffraction patterns with the Lixiscope

The feasibility of the Lixiscope (Low Intensity X-ray Imaging Scope) is demonstrated for real-time observation of transmission Laue patterns. Making use of the high-gain capability of microchannel plate (MCP) visible-light image intensifier tubes, X-ray images are converted to visible-light images by a scintillator. Pb discs are taped to the center of the Lixiscope input face, and crystal samples are held on a goniometer stage with modeling clay. With a compact size to facilitate off axis viewing, and real-time viewing to allow instantaneous response, the Lixiscope may prove useful in dynamic studies of the effects of plastic flows, stresses, high pressures, and low temperatures.

Chung, D. Y.↗