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DOE OSTI2025

Energy-resolved neutron imaging and diffraction including grain orientation mapping using event camera technology

Time-of-flight neutron diffraction and energy-resolved imaging each provide unique perspectives into material properties. Neutron diffraction is useful for assessing microstructural parameters such as phase composition, texture, and dislocation densities, though it typically provides averaged data over the sampled volume. Energy-resolved imaging, on the other hand, offers both spatial and spectral information by detecting Bragg edges and neutron absorption resonances, which enables detailed mapping of microstructure and isotopic composition. When combined, these techniques have the potential to enrich our understanding of material behavior across different scales, enhancing our understanding of complex materials. Traditionally, these modalities are conducted on separate instruments, which is time-consuming and poses challenges for data integration. Here, we report the integration of the LumaCam, an event-mode energy-resolved neutron imaging camera with the HIPPO time-of-flight diffractometer at LANSCE. This integration enables simultaneous diffraction and imaging across the full spectrum, with analysis optimized for diffraction and Bragg-edge imaging in the thermal range (0.45–10 Å) and resonance imaging in the epithermal range (0.5–3000 eV), facilitating comprehensive multi-modal analysis. We demonstrate its capabilities through case studies, including spatial mapping of grain orientations in a steel sample and accurate thickness estimations for irregular samples including a depleted uranium cylinder and a natural silver-containing mineral specimen. The combined setup enhances real-time sample alignment and provides comprehensive data for crystal structure, texture, and isotopic composition analysis. This approach opens new possibilities for advanced applications in nuclear engineering, archaeology, and materials science.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Micropolar deep material network

This study extends the Deep Material Network (DMN), a physics-informed machine learning framework, to predict the homogenized mechanical response of composite materials with micropolar (Cosserat-type) constitutive behavior. This extension incorporates microstructure-dependent size effects, enabling accurate, efficient, and size-aware predictions for composites with complex internal architectures. While traditional, direct numerical simulation micropolar models effectively capture size effects by introducing extra local degrees of freedom, they bring significant computational challenges, particularly for multiscale analyses relevant to engineering applications. The micropolar DMN developed in this paper achieves high accuracy while significantly reducing computation time compared to micropolar direct numerical simulations. This advancement enables multiscale analyses and parameter studies that were previously impractical, such as high-cycle fatigue simulations and comprehensive investigations of internal length scale effects notably in size-dependent plastic response and the optimization of lattice structures. By uniting microstructure-sensitive modeling, physics-driven learning, and scalable surrogate modeling, the micropolar DMN paves the way for accelerated material design, large-scale parametric studies, and the reliable incorporation of size-dependent effects across a wide range of engineering applications, including optimization and next-generation composite design.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Czochralski Growth and Characterization of a Compositionally Complex Rare Earth Aluminum Garnet Scintillator: (Gd 1/4 Y 1/4 Tb 1/4 Lu 1/4 ) 3 Al 5 O 12 :Ce

Compositionally complex oxides have garnered increasing interest for their enhanced phase stability and tunable functional properties, yet their development as bulk single crystal scintillators remains limited. Herein, we report the Czochralski growth and characterization of (Gd 1/4 Y 1/4 Tb 1/4 Lu 1/4 ) 3 Al 5 O 12 :Ce (GYTLAG), a compositionally complex garnet incorporating four dodecahedrally coordinated principal rare earth elements. The garnet phase was confirmed by powder and single crystal X-ray diffraction, and macroscopic defects are described. X-ray absorption near-edge structure measurements confirm the 3+ oxidation state of all rare earths and support their occupation of the same crystallographic site; white line intensity variations correlate with the anticipated segregation behavior. Elemental segregation is quantified by SEM/EDS and ICP-OES, and a linear trend was established between the segregation coefficient and the difference between each rare earth’s ionic radius (r) and the average ionic radius (AIR) of the dodecahedral site. This trend offers a predictive framework for compositional control in future REAG crystals grown by the Czochralski method. Photoluminescence and radioluminescence measurements reveal both Ce 3+ and Tb 3+ emission. Scintillation pulses exhibit four-component decay with dominant ~230 µs and ~1.2 ms components, and the light yield is estimated to be up to 43,000 ph/MeV under 137Cs γ-ray excitation. GYTLAG also demonstrates a strong radioluminescence efficiency and 50% lower afterglow at 20 ms compared to a LuAG:Ce reference, underscoring its promise for scintillator applications.

Compositionally complex oxide, high entropy oxide,↗

DOE OSTI2025

DuctGPT: A Generative Transformer for Forward Screening of Ductile Refractory Multi-Principal Element Alloys

Designing ductile materials for extreme environments such as fusion reactors requires a deep understanding of the complex interplay between electronic structure, mechanical stability, and wide compositional space. Here, in this work, we introduce DuctGPT, a physics-informed, GPT-powered machine learning platform that enables rapid and accurate prediction of ductility across a wide range of refractory multi-principal element alloys (MPEAs). Trained on both experimental and high-fidelity computational data, DuctGPT integrates descriptors such as density of states at the Fermi level, elastic constants, and valence electron concentration to capture the fundamental mechanisms governing ductile versus brittle behavior. Using this framework, we screen over 1000 compositions in of body-centered cubic (BCC) MPEAs, including two new alloy classes, i.e., NbTa-rich (NbTa $>$ 50 at.%) NbTa-Ti-V and W-rich ($>$ 50 at.%) W-Ti-V MPEAs, to rapidly identify promising alloy compositions with enhanced ductility. Validation against experimental data confirms the model's ability to predict ductility with high fidelity and low uncertainty. By leveraging conversational AI and robust physical modeling, DuctGPT provides a blueprint for the next generation of alloy design assistants, enabling human-AI collaboration in the accelerated discovery of ductile, high-performance materials for fusion, aerospace, and advanced manufacturing.

AI/ML↗

DOE OSTI2026

The Use of Microelectrodes in Molten Salt Electrochemistry

Molten salts have attracted considerable interest as essential media for advanced high-temperature technologies, including molten salt reactors, thermal energy storage, high-temperature electrolysis, and pyrochemical processing. Their ability to remain stable in liquid form at elevated temperatures, combined with favorable thermophysical properties and wide electrochemical windows, makes them highly suitable for applications involving heat transfer, energy storage, and hightemperature electrochemical processing. However, despite these advantages, molten salts present challenges due to their chemically reactive nature at high-temperatures, especially in the presence of oxidizing impurities. Salt chemistry can fluctuate through interactions with impurities over time, or fuel burnup in the case of molten salt reactors, often leading to the dissolution of metal species. This dynamic environment not only results in complex redox behavior but also promotes corrosion, which is rarely uniform and frequently manifests as localized degradation driven by structural materials’ compositional differences, electrochemical imbalances, and microstructural susceptibilities.

Kim, Changkyu [University of Wisconsin-Madison, WI↗

DOE OSTI2025

Mapping Rare Earths and Toxics in E-Waste via Hyperspectral Imaging and Machine Learning

Electronic waste (e-waste) presents a mounting challenge to environmental sustainability due to its complex composition, which includes high-value rare earth elements, hazardous organic compounds, and non-recyclable plastics. Accurate and scalable material classification is essential for enabling efficient resource recovery and safe recycling practices. This study introduces a confidence-aware classification pipeline that combines mid-infrared hyperspectral imaging (HSI), spectral angle mapping (SAM), and iterative machine learning to perform pixel-level material identification across e-waste devices. A curated spectral library encompassing artificial materials (e.g., plastic iron oxide, galvanized metals), minerals (e.g., allanite, hematite), and organic compounds (e.g., benzanthracene, toluene) was used to generate pseudo-labels, each assigned a confidence score based on SAM-derived spectral similarity. High-confidence samples from seven consumer electronics—digital cameras, keyboards, laptop fans, modems, motherboards, TV remotes, and speakers—were iteratively expanded and classified using models such as Support Vector Machine (SVM), Random Forest, Gradient Boosting Classifier, Partial Least Squares Discriminant Analysis (PLSDA) and Logistic Regression. The best-performing classifiers achieved macro F1 scores approaching 1.0. Results revealed widespread plastic content (dominated by plastic iron oxide), the presence of rare earth-bearing minerals like cerium-containing allanite, and pervasive detection of hazardous organics such as benzanthracene. Principal Component Analysis (PCA) visualizations and confusion matrices confirmed high separability and robust classification performance. This methodology enables precise, non-destructive, and scalable classification of heterogeneous e-waste streams. It supports automated, hazard-aware sorting in recycling workflows, facilitating selective recovery of critical materials and compliance with circular economy goals. The confidence-aware framework provides a foundation for real-time deployment in industrial settings, offering significant implications for smart e-recycling infrastructure and policy-driven material stewardship.

Circular economy↗

DOE OSTI2025

Micro-Mechanically Guided High-Throughput Alloy Design Exploration Towards Metastability-Induced H Embrittlement Resistance

We develop a high-throughput approach for studying H embrittlement (HE)-resistance in alloys, which is based on combinatorial compositional screening of metastability effects by in situ scanning electron microscopy H-analyses. The project objective included: (i) technique development of high-throughput screening (HTS) for HE-resistance, (ii) discovery of new metallic materials with superior HE-resistance, (iii) Multiscale verification of HE-resistance of the new alloys and H-barrier layers, from atomic scale to an engineering scale. The investigation focused on a model system that enables exploration of different metastable states in Fe-based complex-concentrated alloys. Composition spread islands with hundreds of varying compositions are fabricated on a single substrate using a combinatorial co-sputtering technique. To screen these alloys, we developed and employed a home-built SEM-based integrated analysis system capable of characterizing H-permeability, H-trapping, H-influence on mechanical properties, and other material properties, as well as atomistic to continuum simulations to study the underlying physics.

08 HYDROGEN↗

DOE OSTI2026

Co-Design of Charge Transport Superhighways to Connect Catalytic Sites in Soft Photoelectrochemical Systems

Efficient photon-to-electron-to-molecule conversion requires multi-length scale control over charge transport pathways, where electronic charges are delivered to catalytic sites under high mass transport flux. A fundamental question is how can we co-design charge transport pathways to promote efficient charge transfer to/from catalytic sites in complex three-dimensional architectures? Soft conducting polymer systems offer exceptional promise to provide three-dimensional charge transport networks, where electrolyte (ion and solvent) can interdiffuse to promote long-lived charge carriers and the molecular nature allows for strategic synthetic design of catalytic sites. Herein we combine theoretical and experimental approaches to investigate the earliest stages of photoelectrochemical deposition of near-surface catalytic sites (Pt) on soft bulk heterojunction polymeric semiconductors composed of a prototype donor (PTB7-Th) and a prototype acceptor (N2200) as a model system towards better understanding molecular catalyst-polymer site interactions. We focus initially on photoelectrochemical deposition of low Pt loadings, nanoparticle sizes (formed by progressive nucleation) below 20 nm, for both density functional theory (DFT) modeling studies and for spectroscopic characterization using surface-sensitive X-ray and UV-photoemission (XPS/UPS). DFT modeling of “n-type” N2200 slabs reveal for the first time that sulfur atoms in the thiophene units serve as the lowest-energy adsorption sites for single Pt atoms, while larger Pt clusters engage more complexly with both thiophene and naphthalene diimide (NDI) core sites. Changes in chemical composition observed by X-ray photoelectron spectroscopy (XPS) support the DFT predictions, and the angle-resolved measurements reveal that Pt nucleation initiates at subsurface sites which appear to be localized active domains that promote charge transport/transfer and enable vertical growth toward the surface. These results suggest that light-activated Pt nanoparticle deposition decorates energetically distinct sites, where photoactivity is dictated by the local energetics of those sites, and the fact that they represent the termini of charge transport “super-highways” – a small percentage of the total volume of the donor/acceptor polymeric active layer which carries most of the photocurrent generated during both Pt deposition and photoelectrochemical HER. We posit that these initial studies provide a foundational strategy for design of catalytic sites in the near surface regions of complex polymeric materials and advancing soft semiconductor-based photoelectrochemical systems. Achieving a nanometer-scale understanding of catalyst deposition and the impact of local composition and energetics on that placement, should ultimately provide the design guidelines (co-design) for a broad array of catalysts at sites that optimize that efficiency and maximize platform durability.

14 SOLAR ENERGY↗

DOE OSTI2025

AI‐Driven Defect Engineering for Advanced Thermoelectric Materials

Thermoelectric materials offer a promising pathway to directly convert waste heat to electricity. However, achieving high performance remains challenging due to intrinsic trade-offs between electrical conductivity, the Seebeck coefficient, and thermal conductivity, which are further complicated by the presence of defects. This review explores how artificial intelligence (AI) and machine learning (ML) are transforming thermoelectric materials design. Advanced ML approaches including deep neural networks, graph-based models, and transformer architectures, integrated with high-throughput simulations and growing databases, effectively capture structure-property relationships in a complex multiscale defect space and overcome the “curse of dimensionality”. This review discusses AI-enhanced defect engineering strategies such as composition optimization, entropy and dislocation engineering, and grain boundary design, along with emerging inverse design techniques for generating materials with targeted properties. Finally, it outlines future opportunities in novel physics mechanisms and sustainability, highlighting the critical role of AI in accelerating the discovery of thermoelectric materials.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Self-assembly of wood-based shape memory composites triggered by solar-thermal energy

Transporting and assembling large, complex structures poses significant challenges due to their size, geometry, and cost. Additionally, the installation sites are often inaccessible or hazardous for humans, necessitating self-assembling capabilities in these structures. To mitigate these challenges, we propose using 3D printing materials with shape memory effect (SME) for both transport and construction. This approach involves developing 3D modular components into flat sheets for easier transportation, and then self-assembling into 3D structures on-site using solar energy. To gain a deeper understanding of the factors influencing material memory performance, we have chosen a composite PLA/WF, which is polylactic acid (PLA) with 20 wt% wood flour (WF) for this purpose, leveraging its high tensile modulus at 0.966 GPa, low cost, and sustainability. Printed shapes with this material can maintain a recovery ratio over 90% after 3 cycles. While traditional composites fillers (e.g. glass or carbon fiber) are added to enhance mechanical and thermal properties, the addition of bio-based fillers like WF accomplish similar goals without compromising sustainability. We conducted multiple experiments to demonstrate how environmental conditions (i.e. temperature) maximize the material’s SME. Although still at an early stage, this study provides initial insights into bridging the gap between the small-scale nature of shape memory polymers (SMPs) and their potential for large-scale additive manufacturing, addressing a critical need for efficient and sustainable construction. In the long term, we hope our study contributes to the design vision of utilizing SMPs for transportation, assembly, and deployment of complex structures, providing a new pathway for sustainable construction and transportation of large-scale structures to hard-to-access locations such as disaster-affected areas and remote deserts, etc.

4D printing↗

DOE OSTI2026

High temperature stabilization of ultrafine grain tungsten alloys through synergistic compositional complexities

Thermally-stabilized fine-grained microstructures in tungsten provide a pathway to harnessing enhanced properties such as a reduced ductile-to-brittle transition temperature and improved strength while mitigating the adverse effects of grain growth and recrystallization. Here, in this study, we employ a material design strategy that relies on grain boundary segregation in the nanocrystalline state driven by alloy thermodynamics balanced with impurity scavenging through in situ formation of kinetically-stabilizing metal carbides. A W-Ti-Cr alloy is designed through lattice Monte Carlo methods and subsequently synthesized in a single-phase nanocrystalline state, which upon annealing, evolves into an ultrafine grained microstructure containing chromium grain boundary segregation collectively with a dispersed TiX (X=C,O) phase through the reaction of titanium with carbon and oxygen impurities. In situ synchrotron X-ray diffraction experiments demonstrate that increased Ti promotes stabilization across a larger temperature range but with diminishing returns above 10 at.% Ti. Long-term stability was confirmed through the retention of the ultrafine grained microstructure for a total of 8 days (192 h) at 1300 °C without grain/carbide growth and/or recrystallization. Our results demonstrate that, through strategic tailoring of composition and microstructure, one can harness the benefits of both thermodynamic and kinetic stabilization mechanisms, opening pathways for future alloy formulations that expand the window of stability.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Accurate and Fast Anomaly Detection in Additive Composite-Based Manufacturing using Thermal Cameras

Today, large-scale additive manufacturing with plastics and composite materials requires continuous monitoring by experienced staff to prevent, detect and correct anomalous events affecting the performance of the printed part. We address the complexity of this demanding task by designing a camera-based anomaly detection system utilizing probabilistic principal component analysis (PPCA). This is a machine learning technique is trained with thermal images collected during normal operation of the large-scale printer (Cincinnati BAAM). This technique is advantageous for practical applications as there is no need to artificially introduce anomalous conditions into model training. During deployment, we challenge this model by introducing deliberate variations of the extruder speed. We reduce extrusion speed to a lower level, between 70 and 95% of the nominal value to collected test images. Our results show that images are easily identified as anomalous for extruder speeds at or below 85% of the nominal speed, meaning that an anomalous reduction of the material deposition rate can be detected within seconds of its onset. We show that our results are robust to (a) camera-to-camera variability and (b) print-to-print variability.

Pike, John [ORNL]↗

DOE OSTI2025

Additive manufacturing of carbon fiber-reinforced thermoset composites via in-situ thermal curing

Fiber-reinforced polymer composites are lightweight structural materials widely used in the transportation and energy industries. Current approaches for the manufacture of composites require expensive tooling and long, energy-intensive processing, resulting in a high cost of manufacturing, limited design complexity, and low fabrication rates. Here, we report rapid, scalable, and energy-efficient additive manufacturing of fiber-reinforced thermoset composites, while eliminating the need for tooling or molds. Use of a thermoresponsive thermoset resin as the matrix of composites and localized, remote heating of carbon fiber reinforcements via photothermal conversion enables rapid, in-situ curing of composites without further post-processing. Rapid curing and phase transformation of the matrix thermoset, from a liquid or viscous resin to a rigid polymer, immediately upon deposition by a robotic platform, allows for the high-fidelity, freeform manufacturing of discontinuous and continuous fiber-reinforced composites without using sacrificial support materials. This method is applicable to a variety of industries and will enable rapid and scalable manufacture of composite parts and tooling as well as on-demand repair of composite structures.

36 MATERIALS SCIENCE↗

DOE OSTI2025

A Novel Manufacturing Process of Lightweight Automotive Seats: Integration of Additive Manufacturing and Reinforced Polymer Composite

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

A Novel Manufacturing Process of Lightweight Automotive Seats (Integration of Additive Manufacturing and Reinforced Polymer Composite)

Lightweight automotive seats offer multiple benefits to original equipment manufacturers in terms of cost savings from various aspects, including less material usage, more integrated processes, and compliance with Corporate Average Fuel Economy Standards. Original equipment manufacturers have been focusing on innovative ways to produce light weight automotive seats. The commercially available automotive seats are currently made of multiple metal components combined through welding and fasteners. The use of additive manufacturing and composite structures is particularly useful for light weighting the automotive components. Additive manufacturing (AM) offers multiple advantages over traditional manufacturing processes such as freedom of design thereby enabling complex structural geometries, mass customization and waste minimization, and control over the fiber alignment through deposition in a predetermined pattern. Combining metal inserts with polymer composites through a novel manufacturing process allows design of lightweight and high-performance materials for automotive components. However, fabricating these metal polymer composite structures through traditional manufacturing processes limits their mechanical properties due to limited design freedom, lack of control over fiber orientation in composite parts, and poor interfacial bonding between the constituent materials. It is essential to develop a novel manufacturing process to enable high throughput production of lightweight automotive seats using metal and polymer composites. As such it is important to design the automotive seat suitable for manufacturing via this process and perform mechanical characterization on various subcomponents of the seat to ensure that the design and performance requirements provided by the auto manufacturer are met. The aim of this project is to develop a novel manufacturing technique to produce lightweight automotive seat by combining AM with conventional manufacturing processes. The car seat back panel will be designed via topology optimization and numerical simulations to minimize the overall weight while ensuring it meets all the performance requirements. The optimization of the seat back structure will be based on computational stress analysis to maximize the stiffness and minimize the weight. Materials currently used by Ford Motor Company will be adopted for a few subcomponents while the in-house composite materials will be used for the rest of the seat back. The composite and metallic materials will be tested to determine their mechanical properties as these are necessary for simulations. A novel manufacturing process will be developed to integrate AM metal inserts with discontinuous reinforced composite through large scale additive manufacturing and compression overmolding processes. The developed manufacturing technique will be used to fabricated various subcomponents suitable for the seat back design and mechanically tested to determine their properties. The manufacturing of the lightweight seat back design through this process involves integrated AM metal inserts with the composite structure for recliner connection. The manufacturing of the entire seat back which is lightweight through the novel manufacturing process will be discussed. The performance of the designed seat back will be investigated through numerical simulations and shown to meet all the requirements provided by the auto manufacturer. The final goal of developing a novel manufacturing process for lightweight automotive seats is met through design optimization of seat back, manufacturing of subcomponents, mechanical characterization, and validation through numerical simulations. The routes to achieve the final goal of the project and the depth in which they were investigated changed throughout the project due to personnel changes and the COVID-19 pandemic. The project resulted in the development of a novel manufacturing process to integrate metal inserts with tailored polymer composite preforms through overmolding. Leveraging this proven manufacturing process, a lightweight seat back was designed through topology optimization and numerical simulations. The designed seat back uses AM metal inserts and compression overmolding of tailored polymer composite preforms obtained via large scale additive manufacturing. The metal polymer composite structures fabricated through this process exhibited enhancement in stiffness and improved ductility upon testing. Overall, the project provided an alternative design and manufacturing technique for automotive seat back that enables weight saving while meeting the safety and performance requirements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

High-throughput synthesis of high-entropy alloys via parallelized electric field assisted sintering

Materials discovery and design is an expensive and time-consuming process, though necessary to advance many engineering fields. In this work, a novel tooling design is utilized in conjunction with electric field assisted sintering (EFAS) to effectively create a new high-throughput synthesis technique: parallelized EFAS. Through this technique, a wide range of material compositions and geometries can be synthesized in parallel as isolated samples or as part of contiguous arrays. Multiple tooling designs are explored to examine both the flexibility and limitations of the technique. A series of increasing complex alloys is produced simultaneously using in situ alloying, beginning with pure Ni and adding equimolar constituents up to the septenary high-entropy alloy AlCoCrCuFeMnNi. Microstructural characterization reveals each sample is effectively fully dense and chemically homogenous while exhibiting phases in agreement with CALPHAD predictions. Scalability of parallelized EFAS is then experimentally demonstrated and the implications for materials discovery and automation are discussed.

36 - MATERIALS SCIENCE↗

DOE OSTI2026

Radiation Effects in Next Generation Used Nuclear Fuel Reprocessing Strategies

With the global community committed to significantly expanding nuclear energy capacity, the development of efficient used nuclear fuel (UNF) management strategies has become more critical than ever. These strategies are vital to fostering the widespread adoption of closed fuel cycles, which are essential for sustainable nuclear energy production and security. Achieving this ambitious goal necessitates a comprehensive understanding of radiation effects on next-generation technologies, as radiolysis can often limit the longevity and performance of these systems. This seminar will provide an overview of next-generation UNF reprocessing strategies, highlighting the latest advancements and innovative approaches in the field. Particular attention will be given to two key areas of recent research: 1. Radiation robustness and performance of advanced sulfur chloride-based chlorination technologies. We will explore the efficacy of sulfur chloride-based chlorination processes in the presence of surrogate cladding materials, specifically aluminum. These processes have shown promise in the dissolution, decontamination, and recovery of cladding materials for reuse. Detailed findings on how the composition and performance of these sulfur chloride solvents respond to radiation exposure will be discussed. 2. Impacts of metal ion complexation and direct dissolution conditions on monoamide-based reprocessing strategies. We will delve into the time-resolved and dose accumulation effects of irradiation on the direct dissolution of voloxidized uranium and rhenium using N,N-di-(2-ethylhexyl) butyramide (DEHBA) or N,N-di-(2-ethylhexyl)isobutyramide (DEHiBA) in pre-equilibrated n-dodecane solvent. The implications of these interactions on dissolution efficiency, radiolytic stability, and overall process performance will be examined. These studies aim to underscore the importance of understanding radiation effects in the development of next-generation UNF reprocessing technologies and the global transition towards more sustainable and efficient nuclear energy systems.

38 - RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCL↗

DOE OSTI2026

Affine Transformations to Enable Machine Learning for Semi-Quantitative EDS Analysis

Energy Dispersive X-ray Spectroscopy (EDS) is an essential technique for determining elemental concentrations and distributions within microstructures, critical for materials discovery, optimization, and qualification. However, most published EDS data is qualitative because current quantitative EDS analysis methods require extensive calibration and post-processing, limiting their practicality and widespread adoption. This work seeks to establish a framework for accelerated EDS characterization and spectrum analysis that can leverage ML to analyze correlations between various elemental compositions and resulting EDS spectra. The complex physics and data result in a high-dimensional problem that grows exponentially with the number of elements in the system and the complexity of the spectrum analysis. ML provides a way to compute and optimize the results of this highly dimensional problem in a flexible way to tailor it to the user’s specific needs and material system. However, the framework emphasizes transparency through a strictly mathematical affine transformation, so the analysis remains understandable and reviewable to facilitate adoption by the scientific community. While currently implemented methods are simplistic and unvalidated, further development and demonstration of this framework could enable high-throughput, accurate, and accessible EDS characterization.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗