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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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47 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Part-scale microstructure prediction for laser powder bed fusion Ti-6Al-4V using a hybrid mechanistic and machine learning model

Laser powder bed fusion (LPBF) Ti-6Al-4V is widely studied for use in structural applications in aerospace and medical industries, but mechanical anisotropy and microstructural inhomogeneity prohibits its wider adoption. Although successful microstructure prediction models have been developed, a remaining challenge is their limited integration across length/time scales and validation by experimental studies. Here, this work proposes a physics-augmented machine learning surrogate model to unite predictions of LPBF temperature, β phase morphology and texture, and α/α’ formation into a single framework that is calibrated and validated with experiments. First, a phase field (PF) model of the martensitic β→α’ transformation is developed and calibrated using data from in-situ synchrotron cyclic heating/cooling studies quantifying the variation of α phase fraction with time. In parallel, an established finite difference-Monte Carlo (FDMC) model predicts the part-scale temperature profile and β grain formation during solidification. A dataset is developed using LPBF cyclic temperature descriptors from the FDMC model as inputs and corresponding α/α’ phase fraction and width from the PF model as outputs. Five machine learning (ML) regression models are tested and optimized, having mean absolute error in testing ≤ 4 %, and the k-nearest neighbors (KNN) model is selected as the best performing. The KNN model is called at the nodal level during post-processing of the FDMC model to replace and downscale the response of the PF model. The combined agility and accuracy of the hybrid FDMC-ML model enables part-scale microstructure predictions that can be further used for property predictions to accelerate AM process optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Invariant discovery of features across multiple length scales: Applications in microscopy and autonomous materials characterization

Physical imaging is a foundational characterization method in areas from condensed matter physics and chemistry to astronomy and spans length scales from atomic to universe. Images encapsulate crucial data regarding atomic bonding, materials microstructures, and dynamic phenomena such as microstructural evolution and turbulence, among other phenomena. The challenge lies in effectively extracting and interpreting this information. Variational Autoencoders (VAEs) have emerged as powerful tools for identifying the underlying factors of variation in image data, providing a systematic approach to distilling meaningful patterns from complex data sets. However, a significant hurdle in their application is the definition and selection of appropriate descriptors reflecting local structures. Here, we introduce the scale-invariant VAE approach (SI-VAE) based on the progressive training of the VAE with the descriptors sampled at different length scales. The SI-VAE allows the discovery of the length scale-dependent factors of variation in the system. Here, we illustrate this approach using the ferroelectric domain images and generalize it to the movies of the electron-beam induced phenomena in graphene and topography evolution across combinatorial libraries. This approach can further be used to initialize the decision making in automated experiments including structure–property discovery and can be applied across a broad range of imaging methods. This approach is universal and can be applied to any spatially resolved data including both experimental imaging studies and simulations, and can be particularly useful for exploration of phenomena such as turbulence and scale-invariant transformation fronts.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Quantifying the impact of operating temperature on cracking in battery electrodes, using super-resolution of microscopy images and stereology

There are numerous factors that can have an impact on the degradation behavior of batteries, such as the number of recharge cycles or the charge rate. Here, we investigate the influence of operating temperature on the structural degradation of the microstructure in lithium-ion positive electrodes. For that purpose, the microstructure is characterized for cathodes which have been cycled for 200 cycles under 6C (10-minute) charging at different operating temperatures, namely, 20°C, 30°C, 40°C, and 50°C. For each operating condition scanning electron microscopy (SEM) images of cross-sectioned Li x Ni 0.5 Mn 0.3 Co 0.2 O 2 (NMC532) electrodes have been analyzed, to determine structural descriptors such as global particle porosity, crack size/length/width distribution, and porosity and specific surface area distribution of individual particles. Additionally, a stereological method has been deployed to investigate the local particle porosity as a function of distance to the particle center. Results show that particle porosity increases with increasing cycling temperature. Particle porosity is greatest at the particle center and decreases along the particle radius to the exterior. Particle surface area is similar across the four cycling-temperature aging conditions.

25 ENERGY STORAGE↗
DOE OSTI2024

Enhanced Radiation Damage Tolerance of Amorphous Interphase and Grain Boundary Complexions in Cu-Ta

Amorphous interfacial complexions are particularly resistant to radiation damage and have been primarily studied in alloys with good glass-forming ability, yet recent reports suggest that these features can form even in immiscible alloys such as Cu-Ta under irradiation. In this work, the mechanisms of damage production and annihilation due to primary knock-on atom collisions are investigated for amorphous interphase and grain boundaries in a Cu-Ta alloy using atomistic simulations. Amorphous complexions, in particular amorphous interphase complexions that separate Cu and Ta grains, result in less residual defect damage than their ordered counterparts. Stemming from the nanophase chemical separation in this alloy, the amorphous complexions exhibit a highly heterogeneous distribution of atomic excess volume, as compared to a good glass former like Cu-Zr. Complexion thickness, a tunable structural descriptor, plays a vital role in damage resistance. Thicker interfacial films are more damage-tolerant because they alter the defect production rate due to differences in intrinsic displacement threshold energies during the collision cascade. Overall, the findings of this work highlight the importance of interfacial engineering in enhancing the properties of materials operating in radiation-prone environments and the promise of amorphous complexions as particularly radiation damage-tolerant microstructural features.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Structure–Property Relationships of Recycled Lithium-Ion Battery Cathodes: Microstructure Optimization Using Virtual Materials Testing

The increasing demand for sustainable battery technologies requires effective recycling strategies for end-of-life lithium-ion battery cathodes. In this study, virtual materials testing, a well-established framework for modeling conventionally manufactured NMC-based cathodes, is applied to partially recycled cathodes. To this end, virtual cathodes consisting of mixtures of pristine and recycled NMC particles are utilized to systematically analyze structure–property relationships depending on mixing ratios and different spatial arrangement strategies. For this purpose, a stochastic 3D model is developed that is capable of generating virtual cathodes with arbitrary volume fractions of active materials and mixing ratios of pristine and recycled NMC particles. Particularly, the stochastic 3D model can mimic the different size distributions of pristine and recycled particles that are observed in image data. Additionally, the model allows the structuring of pristine and recycled NMC either uniformly mixed or layer-wise arranged, mimicking single- and dual-layer cathodes. Subsequently, a systematic computational analysis is conducted to assess the influence of increasing active material ratios of recycled particles, ranging from 0 % to 100 %, while maintaining a constant overall active material volume fraction. The impact of particle mixing on cathode performance is evaluated by examining transport-relevant geometrical descriptors and effective properties, such as geodesic tortuosity, specific surface area, and tortuosity factor.

25 ENERGY STORAGE↗
DOE OSTI2026

Physics-informed machine learning exploration of Na storage mechanisms in disordered carbon

Sodium-ion batteries are a cost-effective, sustainable alternative to lithium-ion systems for large-scale energy storage. However, optimizing sodium storage in carbon-based anodes with microstructural complexity and atomic disorder remains a major challenge. The intrinsic inhomogeneity of these materials produces diverse local environments, making it difficult for conventional methods to predict and control ion dynamics. Hard carbon (HC) anodes, composed of ranges of ordered-to-disordered graphitic and amorphous nanodomains, offer tunable ion storage and rate capacity, yet rationale design remains a challenge due to poorly understood correlation between local atomic feature and ion transport mechanism. Here, to address this challenge, we introduce a data-driven framework that integrates validated machine-learned interatomic potentials, large-scale molecular dynamics simulations, and machine learning to elucidate sodium transport mechanisms as a function of carbon and sodium loading densities. By computing per-ion structural descriptors and applying unsupervised learning, we identify distinct diffusion modes governed by microscopic features. Supervised analysis and correlation mapping then establish quantitative links between these transport regimes and processing variables such as bulk carbon density and sodium content. This physics-informed approach establishes quantitative structure–transport relationships and offers actionable design principles for engineering high-performance HC anodes.

Data-driven framework↗
DOE OSTI2022

Size-Dependent Nucleation in Crystal Phase Transition from Machine Learning Metadynamics

In this Letter, we present a framework that combines machine learning potential (MLP) and metadynamics to investigate solid-solid phase transition. Here, based on the spectral descriptors and neural networks regression, we develop a scalable MLP model to warrant an accurate interpolation of the energy surface where two phases coexist. Applying it to the simulation of B4–B1 phase transition of GaN under 50 GPa with different model sizes, we observe sequential change of the phase transition mechanism from collective modes to nucleation and growths. When the size is at or below 128 000 atoms, the nucleation and growth appear to follow a preferred direction. At larger sizes, the nuclei occur at multiple sites simultaneously and grow to microstructures by passing the critical size. The observed change of the atomistic mechanism manifests the importance of statistical sampling with large system size in phase transition modeling.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Rapid discovery of high hardness multi-principal-element alloys using a generative adversarial network model

Multi-principal element alloys (MPEAs) continue to gain research prominence due to their promising high-temperature microstructural and mechanical properties. Recently, machine learning (ML) and materials informatics have been used extensively for screening MPEAs, however, most of these efforts were focused on constructing classification and regression models for predicting phase stability and mechanical properties of known compositions. These approaches may accelerate the screening process but optimizing new compositions with desirable properties within a practical time frame from an infinitely large design space of MPEA systems remains a grand challenge. To tackle this composition optimization challenge, a generative adversarial network coupled with a neural-network ML model was utilized to design MPEAs by filtering compositions that have high hardness. Even in a high-dimensional space with 18 elements as descriptors, the ML model was able to generate optimized compositions from which one composition was found to have 10% higher hardness (941 HV) than the maximum in the training data (857 HV). Density-functional theory was used to provide thermodynamic and electronic insights to higher hardness of the new MPEA found. The present work can optimize compositions from a wide design space of 18 elements (including W, Ta and Nb) that presents an opportunity to synthesize new compositions for applications ranging from corrosion-resistant alloys to nuclear materials. Here the findings suggest that generative ML can greatly accelerate materials discovery by identifying novel compositions, which can serve as a data-informed tool to guide experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Editors’ Choice—Flooded by Success: On the Role of Electrode Wettability in CO 2 Electrolyzers that Generate Liquid Products

Economic operation of carbon dioxide (CO 2 ) electrolyzers generating liquid products will likely require high reactant conversions and product concentrations, conditions anticipated to challenge existing gas diffusion electrodes (GDEs). Notably, electrode wettability will increase as lower surface tension products (e.g., formic acid, alcohols) are introduced into electrolyte streams, potentially leading to flooding. To understand the hydraulically stable operating envelopes in mixed aqueous-organic liquid domains, we connect intrinsic electrode wettability descriptors to operating parameters such as electrolyte flow rate and current. We first measure contact angles of water-organic product dilutions on polytetrafluoroethylene (PTFE) and graphite surfaces as planar analogues for GDE components. We then use material balances around the reactive gas-liquid-solid interface to calculate product mass fractions as functions of water sweep rate and current. Product composition maps visualize the extent to which changes in cell performance influence capillary pressure, a determinant of GDE saturation. Analyses suggest that formic acid mixtures pose little risk for GDE flooding across a wide range of conditions, but effluents containing <30% alcohol by mass may cause flooding. This study reveals opportunities to integrate microstructural features and oleophobic surface treatments into GDEs to repel aqueous-organic mixtures and expand the window of stable operating conditions.

25 ENERGY STORAGE↗
DOE OSTI2024

Elucidation of Ce/Zr ratio effects on the physical properties and catalytic performance of CuO x /Ce y Zr 1− y O 2 catalysts

Although cerium oxide (CeO 2 ) is widely used as a catalyst support, its limited defect sites and surface oxygen vacancy/mobility should be improved. The incorporation of zirconium (Zr) in the cerium (Ce) lattice is shown to increase the number of oxygen vacancies and improve catalytic activity. Using a fixed surface density (SD) of copper (∼2.3 Cu atoms per nm 2 ) as a surface species, the role of the support (Ce y Zr 1−y O 2 (y = 1.0, 0.9, 0.6, 0.5, and 0.0)) and defect site effects in the CO oxidation reaction was investigated. Spectroscopic (e.g., Raman, XRD, XPS) and microscopic (e.g., SEM-EDX, HR-TEM) characterization techniques were applied to evaluate the defect sites, crystallite size, lattice parameters, chemical composition, oxidation states of elements and microstructure of the catalysts. Here, the CO oxidation reaction with varied CO : O 2 ratios (1 : 5, 1 : 1, and 1 : 0.5 (stoichiometric)) was used as a model reaction to describe the relationship between the structure and the catalytic performance of each catalyst. Based on the characterization results of Ce y Zr 1−y O 2 materials, the addition of Zr causes physical and chemical changes to the overall material. The inclusion of Zr into the structure of CeO 2 decreased the overall lattice parameter of the catalyst and increased the number of defect sites. The prepared catalysts were able to reach complete CO conversion (∼100%) at low temperature conditions (<200 °C), each showing varied reaction activity. The difference in CO oxidation activity was then analyzed and related to the structure, wherein Cu loading, surface oxygen vacancies, reduction–oxidation ability, CuO x –support interaction and oxygen mobility in the catalyst were the crucial descriptors.

36 MATERIALS SCIENCE↗
DOE OSTI2021

High-Entropy Ceramic Coatings: Transformative New Materials for Environmentally-Compatible Thin-Film Insulators against High-T Molten Salts

An emerging new class of promising materials, High-Entropy Ceramics (HECs) or Compositionally-Complex Ceramics (CCCs), have been successfully fabricated in the last three years. This project evaluates HECs/CCCs in the concentrated solar power (CSP) context. In this project, we have identified a couple of simple, and key descriptors governing thermal conductivity in HECs. Size disorder among the cations was found to have a strong influence on the thermal conductivity across multiple systems and throughout a vast compositional space. Since identifying this, we have optimized previous compositions to achieve low thermal conductivities of $k$~$1-1.4Wm{-1}K{-1}$. We have further reduced our selection of promising compositions based on project guidelines and applied a theory that can potentially predict reactivity with molten salts to identify a small set of HECs that are suitable for further molten salt heat transfer media (HTM) stability testing. Many compositions reveal poor resistance to molten chloride salt after being exposed for 24 h compared to the reference structural alloy, Haynes 230. However, after 100 h, a few HECs/CCCs show strong parabolic rate constant effects and provide greater resistance to chloride salt compared to Haynes 230 alloy, which shows more linear kinetics. Postmortem characterization of the microstructure reveals the parabolic rate constant originates from a dense reaction layer form at the interface. This feature is absent in Haynes 230, which only shows grain boundary infiltration. The HECs/CCCs were also put in contact with the metal alloy under Ar for 800 h at 800°C and show negligible interaction. Lastly, parabolic rate constant HECs/CCCs also have low thermal conductivity $(<2 Wm^{-1} K^{-1})$ at 800°C. These specimens satisfy all project requirements and demonstrate their promising application in next-generation CSP.

14 SOLAR ENERGY↗