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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 OSTI2020

BinaryCIF and CIFTools—Lightweight, efficient and extensible macromolecular data management

3D macromolecular structural data is growing ever more complex and plentiful in the wake of substantive advances in experimental and computational structure determination methods including macromolecular crystallography, cryo-electron microscopy, and integrative methods. Efficient means of working with 3D macromolecular structural data for archiving, analyses, and visualization are central to facilitating interoperability and reusability in compliance with the FAIR Principles. We address two challenges posed by growth in data size and complexity. First, data size is reduced by bespoke compression techniques. Second, complexity is managed through improved software tooling and fully leveraging available data dictionary schemas. To this end, we introduce BinaryCIF, a serialization of Crystallographic Information File (CIF) format files that maintains full compatibility to related data schemas, such as PDBx/mmCIF, while reducing file sizes by more than a factor of two versus gzip compressed CIF files. Moreover, for the largest structures, BinaryCIF provides even better compression—factor ten and four versus CIF files and gzipped CIF files, respectively. Herein, we describe CIFTools, a set of libraries in Java and TypeScript for generic and typed handling of CIF and BinaryCIF files. Together, BinaryCIF and CIFTools enable lightweight, efficient, and extensible handling of 3D macromolecular structural data.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Automated identification of deformation twin systems in Mg WE43 from SEM DIC

In this study, the application of machine learning and computer vision approaches to microscale deformation data for the automated identification of deformation twinning systems, and their associated twin area, is introduced. Deformation data was obtained during in-situ SEM compression testing of a WE43 Mg alloy using digital image correlation (DIC) modified for use with electron microscopy. A 5.7 mm × 3.4 mm area of interest was analyzed, generating ~100 million data points of deformation. Experimental twin trace directions were determined by applying k-means clustering, morphological thinning, and Hough transforms to the deformation data. The identification of deformation twinning was achieved by consideration of the twin trace direction, its strain value, and its evolution. The deformation map was divided into areas corresponding to individual active twin systems, enabling the analysis of microstructure dependence of deformation behavior and twinning activity. The performance of the proposed twinning identification approach was evaluated by accuracy, precision, and sensitivity metrics. The effect of the tuning parameters used in the algorithm on the performance is also discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

DISTRIBUTION OF ION-EXCHANGEABLE RARE EARTH ELEMENTS AND CRITICAL MINERALS IN COAL-HOSTED CLAYS OF THE POWDER RIVER BASIN, WY

Increasing demand for a more robust domestic supply of rare earth elements (REE) and critical minerals (CM) has led to significant investigation into unconventional sources. Coal and coal byproducts from the Powder River Basin (PRB) of Wyoming are potential sources of REE but lack complete characterization of mineral hosts including ion-adsorption clays (IACs). IACs, which provide a substantial portion of the global REE supply, form when hydrated REE complexes are physiosorbed to negatively charged sites on clay surfaces. Typical IAC minerals include kaolinite, illite, and smectite. REE complexes are easily leached from IACs via ion exchange, making them economically attractive. However, research regarding the partitioning behaviors of strategic metals into PRB coal-hosted clays is limited. This project aims to quantify the modes of occurrence and distribution of REE and CM in clay minerals derived from PRB coals. Strategic metals investigated in this project have historic production or promising deposits within the PRB and/or high anticipated supply risk, and include: REE (lanthanides and Y), V, Ti, platinum group elements (PGE; Pt, Pd, Rh, Ru, Ir, Os), Al, Co, Ga, Zr, and W. Semi-quantitative compositional data from portable x-ray fluorescence spectroscopy (pXRF) on coal core from the PRB is consistent with the known correlation between REE and Al content, suggesting increased REE abundance will be found in clay-rich units. pXRF data also identifies elevated V, Ti, Co, and Zr correlated with Al. Preliminary x-ray diffraction (XRD) data indicates kaolinite, illite, and dickite are abundant minerals in overburden and clay-rich partings. Quantitative analysis via inductively coupled plasma mass spectrometry (ICP-MS) provides insights into REE and CM fractionation in coals as we compare trace metal occurrence to variations in mineralogy. Additionally, we apply scanning electron microscopy (SEM) and energy dispersive x-ray spectroscopy (EDS) data to correlate clay size and morphology to trace metal occurrence. Our findings supplement a broader effort to couple REE and CM extraction with existing domestic coal production and contribute to the fundamental understanding of adsorption processes that occur in low-temperature basin environments.

Stuart, Sophia↗
DOE OSTI2020

RockFlow: Fast Generation of Synthetic Source Rock Images Using Generative Flow Models

Image-based evaluation methods are a valuable tool for source rock characterization. The time and resources needed to obtain images has spurred development of machine-learning generative models to create synthetic images of pore structure and rock fabric from limited image data. While generative models have shown success, existing methods for generating 3D volumes from 2D training images are restricted to binary images and grayscale volume generation requires 3D training data. Shale characterization relies on 2D imaging techniques such as scanning electron microscopy (SEM), and grayscale values carry important information about porosity, kerogen content, and mineral composition of the shale. Here, we introduce RockFlow, a method based on generative flow models that creates grayscale volumes from 2D training data. We apply RockFlow to baseline binary micro-CT image volumes and compare performance to a previously proposed model. We also show the extension of our model to 2D grayscale data by generating grayscale image volumes from 2D SEM and dual modality nanoscale shale images. The results show that our method underestimates the porosity and surface area on the binary baseline datasets but is able to generate realistic grayscale image volumes for shales. With improved binary data preprocessing, we believe that our model is capable of generating synthetic porous media volumes for a very broad class of rocks from shale to carbonates to sandstone.

58 GEOSCIENCES↗
DOE OSTI2022

Cluster Analysis of Combined EDS and EBSD Data to Solve Ambiguous Phase Identifications

A common problem in analytical scanning electron microscopy (SEM) using electron backscatter diffraction (EBSD) is the differentiation of phases with distinct chemistry but the same or very similar crystal structure. X-ray energy dispersive spectroscopy (EDS) is useful to help differentiate these phases of similar crystal structures but different elemental makeups. However, open, automated, and unbiased methods of differentiating phases of similar EBSD responses based on their EDS response are lacking. This paper describes a simple data analytics-based method, using a combination of singular value decomposition and cluster analysis, to merge simultaneously acquired EDS + EBSD information and automatically determine phases from both their crystal and elemental data. I use hexagonal TiB 2 ceramic contaminated with multiple crystallographically ambiguous but chemically distinct cubic phases to illustrate the method. Code, in the form of a Python 3 Jupyter Notebook, and the necessary data to replicate the analysis are provided as Supplementary material.

47 OTHER INSTRUMENTATION↗
NASA NTRS2009

Proceedings of the 40th Lunar and Planetary Science Conference

The 40th Lunar and Planetary Science Conference included sessions on: Phoenix: Exploration of the Martian Arctic; Origin and Early Evolution of the Moon; Comet Wild 2: Mineralogy and More; Astrobiology: Meteorites, Microbes, Hydrous Habitats, and Irradiated Ices; Phoenix: Soil, Chemistry, and Habitability; Planetary Differentiation; Presolar Grains: Structures and Origins; SPECIAL SESSION: Venus Atmosphere: Venus Express and Future Missions; Mars Polar Caps: Past and Present; SPECIAL SESSION: Lunar Missions: Results from Kaguya, Chang'e-1, and Chandrayaan-1, Part I; 5 Early Nebula Processes and Models; SPECIAL SESSION: Icy Satellites of Jupiter and Saturn: Cosmic Gymnasts; Mars: Ground Ice and Climate Change; SPECIAL SESSION: Lunar Missions: Results from Kaguya, Chang'e-1, and Chandrayaan-1, Part II; Chondrite Parent-Body Processes; SPECIAL SESSION: Icy Satellites of Jupiter and Saturn: Salubrious Surfaces; SNC Meteorites; Ancient Martian Crust: Primary Mineralogy and Aqueous Alteration; SPECIAL SESSION: Messenger at Mercury: A Global Perspective on the Innermost Planet; CAIs and Chondrules: Records of Early Solar System Processes; Small Bodies: Shapes of Things to Come; Sulfur on Mars: Rocks, Soils, and Cycling Processes; Mercury: Evolution and Tectonics; Venus Geology, Volcanism, Tectonics, and Resurfacing; Asteroid-Meteorite Connections; Impacts I: Models and Experiments; Solar Wind and Genesis: Measurements and Interpretation; Mars: Aqueous Processes; Magmatic Volatiles and Eruptive Conditions of Lunar Basalts; Comparative Planetology; Interstellar Matter: Origins and Relationships; Impacts II: Craters and Ejecta Mars: Tectonics and Dynamics; Mars Analogs I: Geological; Exploring the Diversity of Lunar Lithologies with Sample Analyses and Remote Sensing; Chondrite Accretion and Early History; Science Instruments for the Mars Science Lander; . Martian Gullies: Morphology and Origins; Mars: Dunes, Dust, and Wind; Mars: Volcanism; Early Solar System Chronology; Seek Out and Explore: Upcoming and Future Missions; Mars: Early History and Impact Processes; Mars Analogs II: Chemical and Spectral; Achondrites and their Parent Bodies; and Planning for Future Exploration of the Moon The poster sessions were: Lunar Missions: Results from Kaguya, Chang'e-1, and Chandrayaan-1; LRO and LCROSS; Geophysical Analysis of the Lunar Surface and Interior; Remote Observation and Geologic Mapping of the Lunar Surface; Lunar Spectroscopy; Venus Geology, Geophysics, Mapping, and Sampling; Planetary Differentiation; Bunburra and Buzzard Coulee: Recent Meteorite Falls; Meteorites: Terrestrial History; CAIs and Chondrules: Records of Early Solar System Processes; Volatile and Organic Compounds in Chondrites; Crashing Chondrites: Impact, Shock, and Melting; Ureilite Studies; Petrology and Mineralogy of the SNC Meteorites; Martian Meteorites; Phoenix Landing Site: Perchlorate and Other Tasty Treats; Mars Polar Atmospheres and Climate Modeling; Mars Polar Investigations; Mars Near-Surface Ice; Mars: A Volatile-Rich Planet; Mars: Geochemistry and Alteration Processes; Martian Phyllosilicates: Identification, Formation, and Alteration; Astrobiology; Instrument Concepts, Systems, and Probes for Investigating Rocks and Regolith; Seeing is Believing: UV, VIS, IR, X- and Gamma-Ray Camera and Spectrometer Instruments; Up Close and Personal: In Situ Analysis with Laser-Induced Breakdown Spectroscopy and Mass Spectrometry; Jupiter and Inscrutable Io; Tantalizing Titan; Enigmatic Enceladus and Intriguing Iapetus; Icy Satellites: Cryptic Craters; Icy Satellites: Gelid Geology/Geophysics; Icy Satellites: Cool Chemistry and Spectacular Spectroscopy; Asteroids and Comets; Comet Wild 2: Mineralogy and More; Hypervelocity Impacts: Stardust Models, LDEF, and ISPE; Presolar Grains; Early Nebular Processes: Models and Isotopes; Solar Wind and Genesis: Measurements and Interpretation; Education and Public Outreach; Mercury; Pursuing Lunar Exploration; Sources and Eruptionf Lunar Basalts; Chemical and Physical Properties of the Lunar Regolith; Lunar Dust and Transient Surface Phenomena; Lunar Databases and Data Restoration; Meteoritic Samples of the Moon; Chondrites, Their Clasts, and Alteration; Achondrites: Primitive and Not So Primitive; Iron Meteorites; Meteorite Methodology; Antarctic Micrometeorites; HEDs and Vesta; Dust Formation and Transformation; Interstellar Organic Matter; Early Solar System Chronology; Comparative Planetology; Impacts I: Models and Experiments; Impacts II: Craters and Ejecta; Mars: Volcanism; Mars: Tectonics and Dynamics; Martian Stratigraphy: Understanding the Geologic History of Mars Through the Sedimentary Rock Record; Mars: Valleys and Valley Networks; Mars: Aqueous Processes in Valles Marineris and the Southern Highlands; Mars: Aqueous Geomorphology; Martian Gullies: Morphology and Origins; Mars: Dunes, Dust, and Wind; Mars: Remote Sensing; Mars: Geologic Mapping, Photogrammetry, and Cratering; Martian Mineralogy: Constraints from Missions and Laboratory Investigations; Mars Analogs: Chemical and Physical; Mars Analogs: Sulfates and Sulfides; Missions: Approaches, Architectures, Analogs, and Actualities; Not Just Skin Deep: Electron Microscopy, Heat Flow, Radar, and Seismology Instruments and Planetary Data Systems, Techniques, and Interpretation.

Source record↗
DOE OSTI2023

RadVolViz: An Information Display-Inspired Transfer Function Editor for Multivariate Volume Visualization

In volume visualization transfer functions are widely used for mapping voxel properties to color and opacity. Typically, volume density data are scalars which require simple 1D transfer functions to achieve this mapping. If the volume densities are vectors of three channels, one can straightforwardly map each channel to either red, green or blue, which requires a trivial extension of the 1D transfer function editor. Here, we devise a new method that applies to volume data with more than three channels. These types of data often arise in scientific scanning applications, where the data are separated into spectral bands or chemical elements. Our method expands on prior work in which a multivariate information display, RadViz, was fused with a radial color map, in order to visualize multi-band 2D images. In this work, we extend this joint interface to blended volume rendering. The information display allows users to recognize the presence and value distribution of the multivariate voxels and the joint volume rendering display visualizes their spatial distribution. We design a set of operators and lenses that allow users to interactively control the mapping of the multivariate voxels to opacity and color. This enables users to isolate or emphasize volumetric structures with desired multivariate properties. Furthermore, it turns out that our method also enables more insightful displays even for RGB data. We demonstrate our method with three datasets obtained from spectral electron microscopy, high energy X-ray scanning, and atmospheric science.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Modeling Nanoconfinement Effects Using Active Learning

Predicting the spatial configuration of gas in nanopores of is relevant in applications such as fluid flow forecasting and hydrocarbon reserves estimation. For example, shale reservoirs have suffered from computationally intractable multiscale problems, since fluid properties such as viscosity, density, and adsorption must be calculated by using expensive molecular dynamics (MD) simulations within each nanopore, whereas flow through these connected nanopores must be simulated at the micrometer scale. We utilize machine learning techniques to quickly and accurately model nanoscale confinement effects as an important step toward bridging the nano and micro scales. Our workflow is based on building and training physics-based deep-neural-networks models by learning from a database of MD calculations. The model accounts for the adsorption phenomenon by predicting the statistical distribution of gas inside nanopores. Because large databases of MD calculations are expensive to create, we investigate active learning (AL) as a data set construction strategy. In this workflow, new data are selected based on the model uncertainty via the query-by-committee approach. We show that our workflow obtains accurate models that generalize to real scanning electron microscopy geometries with 1/10th of the number of MD calculations required vs random data set generation. Our method enables the possibility of modeling nanoconfinement effects at the mesoscale, where complex connected sets of nanopores affect flow.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Confined Room-Temperature Ferromagnetism in Kagome (Fe 3 Sn 2 /CoSn) Superlattices

We reveal the presence of robust ferromagnetism in topological kagome (Fe 3 Sn 2 /CoSn) ×n superlattices via polarized neutron reflectivity (PNR) and magnetization measurements. Using molecular beam epitaxy, we have synthesized superlattices of alternating ferromagnetic Fe 3 Sn 2 and paramagnetic CoSn layers, the interfacial integrity of which is confirmed using scanning transmission electron microscopy. Magnetization depth profiles, obtained by fitting the PNR data, confirm the presence of ferromagnetism in the confined Fe 3 Sn 2 layers at room temperature, with minimal thickness dependence down to 2 nm. A net magnetization is found in the near-surface region of CoSn, which we attribute to surface oxidation. Our results demonstrate that superlattice engineering is a means to confine ferromagnetism in kagome metals, enabling future studies of thickness-dependent anomalous Hall and Nernst effects as well as spin-dependent transport and tunneling in device structures.

kagome metals↗
DOE OSTI2022

Fabrication, thermal analysis, and heavy ion irradiation resistance of epoxy matrix nanocomposites loaded with silane-functionalized ceria nanoparticles

This paper describes a detailed understanding of how nanofillers function as radiation barriers within the polymer matrix, and how their effectiveness is impacted by factors such as composition, size, loading, surface chemistry, and dispersion. Here, we designed a comprehensive investigation of heavy ion irradiation resistance in epoxy matrix composites loaded with surface-modified ceria nanofillers, utilizing tandem computational and experimental methods to elucidate radiolytic damage processes and relate them to chemical and structural changes observed through thermal analysis, vibrational spectroscopy, and electron microscopy. A detailed mechanistic examination supported by FTIR spectroscopy data identified the bisphenol A moiety as a primary target for degradation reactions. Results of computational modeling by the Stopping Range of Ions in Matter (SRIM) Monte Carlo simulation were in good agreement with damage analysis from surface and cross-sectional SEM imaging. All metrics indicated that ceria nanofillers reduce the damage area in polymer nanocomposites, and that nanofiller loading and homogeneity of dispersion are key to effective damage prevention. The results of this study represent a significant pathway for engineered irradiation tolerance in a diverse array of polymer nanocomposite materials. Numerous areas of materials science can benefit from utilizing this facile and effective method to extend the reliability of polymer materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Integrative illustration of a JCVI-syn3A minimal cell

Abstract Data from genomics, proteomics, structural biology and cryo-electron microscopy are integrated into a structural illustration of a cross section through an entire JCVI-syn3.0 minimal cell. The illustration is designed with several goals: to inspire excitement in science, to depict the underlying scientific results accurately, and to be feasible in traditional media. Design choices to achieve these goals include reduction of visual complexity with simplified representations, use of orthographic projection to retain scale relationships, and an approach to color that highlights functional compartments of the cell. Given that this simple cell provides an attractive laboratory for exploring the central processes needed for life, several functional narratives are included in the illustration, including division of the cell and the first depiction of an entire cellular proteome. The illustration lays the foundation for 3D molecular modeling of this cell.

Goodsell, David S.↗
DOE OSTI2024

Elucidating the formation mechanisms of zeolites using data-driven modelling and in-situ characterization

Zeolites are the main solid catalysts used by the chemical industry. The use of zeolites in separations and as shape selective catalysts requires control of the width and connectivity of their pores. 235 distinct zeolite frameworks have been synthesized to date, of over 2 million that have been proposed. Recent work indicates that the limitation is in large part kinetic: new synthetic pathways are required to access new zeolites. Organic cations are used to direct the synthesis towards specific zeolites. However, the molecular mechanisms by which cations direct the nucleation towards specific zeolites is not known. Elucidating these mechanisms is key to realize new zeolites for catalysis and separations, and is the focus of this project. This project developed and implemented a synergistic, data-driven computational and experimental approach to resolve the molecular pathways of nucleation, growth, and polymorph selection of zeolites and the role of organic cations in directing their formation. The project developed computationally efficient and accurate models for the study of the nucleation and growth of pure silica zeolites in molecular simulations, using machine learning with data from experiments. Simulations with these models were integrated with scanning tunneling electron microscopy, computer vision, and deep learning to unveil the molecular pathways of formation of a zeolite. Of particular interest in this project was to elucidate the role of amorphous precursors in the nucleation of the zeolite. Previous experiments indicate that zeolites are born within non-crystalline aggregates in which the silicates and organic cations have local and medium range order similar to that of the zeolite. The organic cations that direct the formation of zeolites and those that direct the formation of ordered mesoporous silicas are similar. We hypothesized that the frustrated attraction that for large organic cations leads to the formation of stable mesophases that direct the synthesis of mesoporous silicas, could promote the formation of metastable mesophases that can assist in the nucleation and polymorph selection of zeolites. The simulations resolved how structure directing agents build crystalline order and showed that mesoscopic pre-ordering occurs is not required to facilitate the nucleation of zeolites, because the synthesis occurs at high driving forces, where the barriers for nucleation are negligible. This project unveiled that polymorph selection in zeolite synthesis occurs after nucleation, opening a distinct area of control through the kinetics of growth and not through nucleation barriers.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Momentum-Space Observation of Optically Excited Nonthermal Electrons in Graphene with Persistent Pseudospin Polarization

The unique optical properties of graphene, with broadband absorption and ultrafast response, make it a critical component of optoelectronic and spintronic devices. Using time-resolved momentum microscopy with high data rate and high dynamic range, we report momentum-space measurements of electrons promoted to the graphene conduction band with visible light and their subsequent relaxation. Here, we observe a pronounced nonthermal distribution of nascent photoexcited electrons with lattice pseudospin polarization in remarkable agreement with results of simple tight-binding theory. By varying the excitation fluence, we vary the relative importance of electron-electron vs electron-phonon scattering in the relaxation of the initial distribution. Increasing the excitation fluence results in increased noncollinear electron-electron scattering and reduced pseudospin polarization, although up-scattered electrons retain a degree of polarization. These detailed momentum-resolved electron dynamics in graphene demonstrate the capabilities of high-performance time-resolved momentum microscopy in the study of 2D materials and can inform the design of graphene devices.

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

Probing the in situ volumes of Arabidopsis leaf plastids using three‐dimensional confocal and scanning electron microscopy

SUMMARY Leaf plastids harbor a plethora of biochemical reactions including photosynthesis, one of the most important metabolic pathways on Earth. Scientists are eager to unveil the physiological processes within the organelle but also their interconnection with the rest of the plant cell. An increasingly important feature of this venture is to use experimental data in the design of metabolic models. A remaining obstacle has been the limited in situ volume information of plastids and other cell organelles. To fill this gap for chloroplasts, we established three microscopy protocols delivering in situ volumes based on: (i) chlorophyll fluorescence emerging from the thylakoid membrane, (ii) a CFP marker embedded in the envelope, and (iii) calculations from serial block‐face scanning electron microscopy (SBFSEM). The obtained data were corroborated by comparing wild‐type data with two mutant lines affected in the plastid division machinery known to produce small and large mesophyll chloroplasts, respectively. Furthermore, we also determined the volume of the much smaller guard cell plastids. Interestingly, their volume is not governed by the same components of the division machinery which defines mesophyll plastid size. Based on our three approaches, the average volume of a mature Col‐0 wild‐type mesophyll chloroplasts is 93 μm 3 . Wild‐type guard cell plastids are approximately 18 μm 3 . Lastly, our comparative analysis shows that the chlorophyll fluorescence analysis can accurately determine chloroplast volumes, providing an important tool to research groups without access to transgenic marker lines expressing genetically encoded fluorescence proteins or costly SBFSEM equipment.

Knoblauch, Jan↗
DOE OSTI2021

Data association algorithm for large-scale multi-object tracking with complex interactions

We present an online multi-object tracking algorithm to track multiple objects across a large number of image frames. Our work is motivated by the need to study evolution of nanoscale objects by transmission electron microscopy. The proposed approach is based on the existing multi-way data association tracking algorithm that is capable of tracking interacting objects with complex behaviors (i.e., merge, split, overlap, and appearance or disappearance). The multi-way data association is an offline algorithm to associate objects across all image frames at one step with a global optimization, which does not scale very well for large number of image frames. The proposed online tracking algorithm processes image frames as they arrive by detecting all objects in the newly arrived image frame and making the associations of the objects to those detected from the previous frame by the multi-way data association. This frameby-frame association scheme can cause fragmented traces of the objects that are occasionally misdetected for some image frames. We overcome this issue by allowing previously unassociated objects to be associated when the objects reappear within a fixed number of future image frames, namely the frame-delayed association. We combine the multi-way data association with the frame-delayed association to be able to track interacting objects with accurate handling of object disappearance events. The proposed method is validated through applications to simulated multi-object tracking problem and a real multi-object tracking problem. Here, the outcome of the proposed method is compared with four state-of-the-art algorithms.

36 MATERIALS SCIENCE↗
DOE OSTI2021

KS4A-Omics1.0_FspDS682

Soil fungi facilitate the translocation of inorganic nutrients from soil minerals to other microorganisms and plants. This ability is particularly advantageous in impoverished soils, because fungal mycelial networks can bridge otherwise spatially disconnected and inaccessible nutrient hotspots. However, the molecular mechanisms underlying fungal mineral weathering and transport through soil remains poorly understood. Here, we addressed this knowledge gap by directly visualizing nutrient acquisition and transport through fungal hyphae in a mineral doped soil micromodel using a multimodal imaging approach. Here, we observed how a representative of common saprotrophic soil fungi, Fusarium sp. DS 682, exhibited a mechanosensory response (thigmotropism) around obstacles and through pore spaces (~12 μm) in the presence of minerals.This study establishes the significance of fungal biology and nutrient translocation mechanisms in maintaining fungal growth under water and nutrient limitations in a soil-like microenvironment, using a high-throughput multi-omic analysis approach. Data package KS4A-Omics.1.0_FspDS682 (Publication: Fungal Mineral Weathering Mechanisms Revealed Through Direct Molecular Visualization) contents reported here are the first version (1.0) and contain pre- and post-processed data using high throughput data capture technologies for multi-omic analysis and integration, this data package contains raw and post-processed experimental data for X-Ray Absorption Near Edge Structure Spectroscopy (XANES/XRF), Optical Microscopy, Proteomics, Scanning Electron Microscope (SEM), Time-of-Flight Secondary Ion Mass Spectroscopy (ToF-SIMS), X-Ray Diffraction Spectroscopy (XRD) files, and X- Ray Photoelectron Spectroscopy (XPS) using EMSL capabilities. This data package DOI contains a comprehensive collection of high-throughput multi-omics data and process metadata catalog. Support files include additional data download contents “Read Me” with dataset descriptor information and data source method application ontologies (see data dictionary section). Reported data download content is structured for compliance with reported guidelines provided by community standard initiatives and publisher stakeholder policies supporting FAIR data principles.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Deep learning ferroelectric polarization distributions from STEM data via with and without atom finding

Over the last decade, scanning transmission electron microscopy (STEM) has emerged as a powerful tool for probing atomic structures of complex materials with picometer precision, opening the pathway toward exploring ferroelectric, ferroelastic, and chemical phenomena on the atomic scale. Analyses to date extracting a polarization signal from lattice coupled distortions in STEM imaging rely on discovery of atomic positions from intensity maxima/minima and subsequent calculation of polarization and other order parameter fields from the atomic displacements. Here, we explore the feasibility of polarization mapping directly from the analysis of STEM images using deep convolutional neural networks (DCNNs). In this approach, the DCNN is trained on the labeled part of the image (i.e., for human labelling), and the trained network is subsequently applied to other images. We explore the effects of the choice of the descriptors (centered on atomic columns and grid-based), the effects of observational bias, and whether the network trained on one composition can be applied to a different one. This analysis demonstrates the tremendous potential of the DCNN for the analysis of high-resolution STEM imaging and spectral data and highlights the associated limitations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders *

Abstract Recent advances in scanning tunneling and transmission electron microscopies (STM and STEM) have allowed routine generation of large volumes of imaging data containing information on the structure and functionality of materials. The experimental data sets contain signatures of long-range phenomena such as physical order parameter fields, polarization, and strain gradients in STEM, or standing electronic waves and carrier-mediated exchange interactions in STM, all superimposed onto scanning system distortions and gradual changes of contrast due to drift and/or mis-tilt effects. Correspondingly, while the human eye can readily identify certain patterns in the images such as lattice periodicities, repeating structural elements, or microstructures, their automatic extraction and classification are highly non-trivial and universal pathways to accomplish such analyses are absent. We pose that the most distinctive elements of the patterns observed in STM and (S)TEM images are similarity and (almost-) periodicity, behaviors stemming directly from the parsimony of elementary atomic structures, superimposed on the gradual changes reflective of order parameter distributions. However, the discovery of these elements via global Fourier methods is non-trivial due to variability and lack of ideal discrete translation symmetry. To address this problem, we explore the shift-invariant variational autoencoders (shift-VAEs) that allow disentangling characteristic repeating features in the images, their variations, and shifts that inevitably occur when randomly sampling the image space. Shift-VAEs balance the uncertainty in the position of the object of interest with the uncertainty in shape reconstruction. This approach is illustrated for model 1D data, and further extended to synthetic and experimental STM and STEM 2D data. We further introduce an approach for training shift-VAEs that allows finding the latent variables that comport to known physical behavior. In this specific case, the condition is that the latent variable maps should be smooth on the length scale of the atomic lattice (as expected for physical order parameters), but other conditions can be imposed. The opportunities and limitations of the shift VAE analysis for pattern discovery are elucidated.

97 MATHEMATICS AND COMPUTING↗