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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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At least 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Machine learning in materials science: From explainable predictions to autonomous design

The advent of big data and algorithmic developments in the field of machine learning (and artificial intelligence, in general) have greatly impacted the entire spectrum of physical sciences, including materials science. Materials data, measured or computed, combined with various techniques of machine learning have been employed to address a myriad of challenging problems, such as, development of efficient and predictive surrogate models for a range of materials properties, screening and down-selection of novel candidate materials for targeted applications, new methodologies to improve and further expedite molecular and atomistic simulations, with likely many more important developments to come in the foreseeable future. While the applications thus far have provided a glimpse of the true potential data-enabled routes have to offer, it has also become clear that further progress in this direction hinges on our ability to understand, explain and rationalize findings of a machine learning model in light of the domain-knowledge. This focused review provides an overview of the main areas where machine learning has been widely and successfully used in materials science. Subsequently, a brief discussion of several techniques that have been helpful in extracting physically-meaningful insights, causal relationships and design-centric knowledge from materials data is provided. Finally, we identify some of the imminent opportunities and challenges that materials community faces in this exciting and rapidly growing field.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine learning elastic constants of multi-component alloys

The present manuscript explores application of machine learning methods for determining elastic constants and other derived mechanical properties of multi-component alloys. Here, a number of machine learning models, including linear regression, neural network and random forest based models, are trained and tested on a dataset of binary alloys generated using density functional theory (DFT) calculations and spanning over a large number of elemental species in the periodic table. Starting with a wide range of simple and easily accessible compositionally-averaged elemental features, a correlation-based feature selection strategy was used to systematically down-select a set of most relevant features towards the prediction of the elasticity tensor components. The true predictive performance and the associated uncertainties of the models were established by testing on unseen data and bootstrapping, respectively. A single and pair-wise feature partial dependence analysis was performed to visualize the average property trends in the multi-dimensional feature space in order to further understand the achieved predictive performance. The utility of the trained model is further demonstrated by obtaining sufficiently accurate yet highly efficient approximations for bulk modulus, Young’s modulus, shear modulus and Poisson’s ratio for alloys beyond the binary space (i.e., two-component alloys) on which the model was originally trained. More importantly, we test and validate the predictive performance of the developed model directly against the experimentally measured elastic constants of technologically relevant multi-component alloys (such as, Ni- and Ti-based alloys). Finally, utility of such a data-enabled route is demonstrated by predicting the possible range of various elastic properties for vast composition space available within the five component Ni-Cr-Fe-Mo-W alloy system in a high-throughput manner.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Design of a graphical user interface for few-shot machine learning classification of electron microscopy data

The recent growth in data generation by modern electron microscopes requires rapid, scalable, and flexible approaches to image segmentation and analysis. Few-shot machine learning, which can richly classify images from a handful of user-provided examples, is a promising route to high-throughput analysis. However, current command-line implementations of such approaches can be slow and unintuitive to use, lacking the real-time feedback necessary to perform effective classification. Here we report on the development of a Python-based graphical user interface that enables end users to easily conduct and visualize the output of few-shot learning models. This interface is portable and can be hosted locally or on the web, providing the opportunity to reproducibly conduct, share, and crowd-source few-shot analyses.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Hydrogen Localization and Cluster Formation in α-Zr from First-Principles Investigations

We report alpha-zirconium (α-Zr) is a hydrogen atom host material used in nuclear energy and semiconducting applications due to Zr's excellent properties such as corrosion resistance, low thermal neutrons cross-section, and thermodynamically favorable interstitial sequestering of H atoms. Herein we use density functional theory simulations to systematically analyze the initial stages of H insertion in the Zr matrix, and determine optimal hydrogen localization, distribution, and clustering tendencies from a thermodynamics and kinetics perspective. Our results suggest that H occupation of tetrahedral sites is strongly favored, consistent with previous experimental and theoretical studies. Further, a cooperative effect in H localization is shown where H atoms favor clustering. The H pair is found to be the most stable by occupying tetrahedral sites with pairs at second nearest-neighbor positions (TTn2). Among the four H atom clusters, the most favorable configuration has two TTn2 pairs forming "box " or "zig-zag" shaped-cluster with the pairs separated by 3.03 or 3.25 Å, respectively. Nudged elastic band simulations suggest that the formation of TTn2 is facile and is limited by the small diffusion barrier of the monomer of less than 0.1 eV. Further, the formation routes for the 4H atom clusters are also presented. The current study can serve as a foundation to systematically model H atom clustering in metals and alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Out-of-distribution detection with non-parametric density estimation for models predicting processing history of uranium ore concentrates

The rapid advancement in machine learning (ML) and computer vision (CV) coincides with the growth of interest in deploying these ML/CV models in numerous fields from medicine to social science. Similar to those areas, we have witnessed a great number of works in materials science employing ML/CV models – neural networks in particular – in their studies in recent years. These models have proven to obtain accurate performance in various tasks. However, these models struggle to attain a similar performance when encountering test samples coming from a distribution that is different from the training set. More importantly, they fail without providing any warning to the users. Therefore, we propose a framework for detecting out-of-distribution (OOD) samples to alert users when a human intervention might be necessary in this work. Specifically, we explore the use of a non-parametric density estimation method to detect OOD samples. Here, we assess OOD detection capability of the proposed framework on ML models developed for categorizing precipitation routes of U 3 O 8 when encountering OOD datasets that contain samples (1) undergone different imaging acquisition process, (2) undergone different material synthesis process, and (3) different materials than ID set. Through those experiments, we achieve an average area under the receiver operating characteristic (AUROC) of at least 91% on average in detecting OOD samples. With minimal overhead cost and superior performance, the proposed framework enables a reliable and safe system when deploying in real-world scenarios.

Convolutional neural networks↗
DOE OSTI2026

Explainable machine learning reveals that local structural motifs encode the thermodynamic state across the CuZr metallic glass-forming range

Metallic glasses derive their properties from the statistics of local atomic motifs rather than from long-range order, yet a quantitative, chemistry-specific link between motif populations and the underlying glassy state has remained elusive. In this work we combine large-scale molecular dynamics, Voronoi tessellation, deep neural networks, and SHapley Additive exPlanations (SHAP) to identify which local structural motifs define the glassy state of Cu—Zr metallic glasses. A dataset of 17,180 atomistic configurations spanning ten compositions (Cu 20 Zr 80 –Cu 80 Zr 20 ) and four quench rates (10 9 –10 12 K/s) is used to train a feed-forward neural network that regresses temperature across the 50–2000 K liquid–supercooled–glass range, achieving a mean absolute error of 19.89 K and R 2 = 0.9974, confirming that the local structural state is faithfully encoded in motif-level structure. SHAP analysis then reveals that a tightly coupled near-icosahedral family of motifs (coordination numbers (CN) 11–13, including the full icosahedron 001200 and its single-atom-perturbation sibling 10930) collectively encodes the thermodynamic state of the system across the full glass-forming range. The CN = 11–13 ordered members carry negative SHAP values at high populations, tracking the most deeply-quenched configurations, while 10930 shows the reversed signature consistent with its role as a soft-spot host whose population shrinks as the icosahedral network deepens. The analysis demonstrates that explainable machine learning can isolate the minimal motif vocabulary defining the glassy state and recovers the near-icosahedral building blocks previously identified by data-driven analyses of Cu—Zr. The approach provides a general, chemistry-specific route for characterizing the structural state of disordered materials.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Quantum computing based hybrid solution strategies for large-scale discrete-continuous optimization problems

Quantum computing (QC) has gained popularity due to its unique capabilities that are quite different from that of classical computers in terms of speed and methods of operations. This paper proposes hybrid models and methods that effectively leverage the complementary strengths of deterministic algorithms and QC techniques to overcome combinatorial complexity for solving large-scale mixed-integer programming problems. Four applications, namely the molecular conformation problem, job-shop scheduling problem, manufacturing cell formation problem, and the vehicle routing problem, are specifically addressed. Large-scale instances of these application problems across multiple scales ranging from molecular design to logistics optimization are computationally challenging for deterministic optimization algorithms on classical computers. To address the computational challenges, hybrid QC-based algorithms are proposed and extensive computational experimental results are presented to demonstrate their applicability and efficiency. The proposed QC-based solution strategies enjoy high computational efficiency in terms of solution quality and computation time, by utilizing the unique features of both classical and quantum computers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Analysis of tow architecture variability in biaxially braided composite tubes

Spatial deviations in composite braid unit cell dimensions limit mechanical performance and are challenging to model. Full-field measurements of the irregular tow placement in biaxially braided composite tubes were obtained by digital image correlation. Analysis of the reconstructed tow paths showed long-range defects along the hoop direction with short-range defects dominating the biased tow direction. A route toward numerical simulation of the composite response is established by developing a 3D model of the composite using the measured tow trajectories. Finally, a comparison to previously-published triaxial braid results indicates axial tows work to constrain unit cells dimensions along the hoop direction.

3-Dimensional reinforcement↗
DOE OSTI2024

High plasticity in refractory composite fabrication by ultrasonic additive manufacturing

Refractory metal composites are desirable for use in extreme environments that require materials with high specific strengths and resilience to external environments such as that found in nuclear and aerospace. However, due to the high melting temperature of refractories, liquid state joining processes such as welding remains difficult. Ultrasonic additive manufacturing (UAM) provides a potential route for processing refractory composites because it is a solid-state (i.e., no-melting) process and allows for intermittent machining operations to be performed between welds. Further, to demonstrate refractory composite fabrication, this study utilized UAM to machine a cavity to locate and sequester a Mo foil in a Zircaloy-4 (Zry-4) baseplate and additively build over the top with Zry-4 foils, thereby embedding the Mo in Zry-4 matrix. Significant deformation of the Zry-4 microstructure was observed: this deformation caused adiabatic heating and subsequent dynamic recrystallization through the transformation from α→β and then back to α as the material cooled. Flexural testing of the Zr–Mo composite revealed no delamination or failure, but the strength was not as expected, falling lower than a cold-worked Zry-4 sample. Finite element analysis supported that some bonding must have existed between the Zry-4 and Mo. There was indeed an interdiffusion zone at the Zry-4 foil–Mo foil interface, observing a metastable body-centered cubic β-Zr lathe. It was determined that sufficient strain energy was present to encourage the nucleation of the β-Zr grain along α-Zr grains. Future work is warranted to investigate UAM for refractory composite fabrication.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Initial assessment of alternative carbon fiber geometries for design of cost-effective compressive performance: Size effect studies

Carbon fiber provides opportunity to reduce weight in structural composites, including wind turbine blades, due to the material's superior specific stiffness and specific strength compared to alternatives. Despite these advantages, cost and compressive performance are considered weaknesses for carbon fiber products available today. Studies to produce low-cost carbon fiber alternatives, including the use of textile-derived precursor systems, have shown progress and merit through the DOE/ORNL low-cost carbon fiber initiatives. Here, this work focuses on enabling increases in compressive strength through design of the carbon fiber geometry, applicable to both textile and conventional precursor systems, while also providing opportunities to reduce carbon fiber processing costs. Fiber-resin interface and fiber alignment are among the most frequently cited factors controlling composite compressive performance. However, it is believed that there is opportunity in traditionally unexplored routes to increasing compressive strength through alteration of the carbon fiber geometry by increasing the fiber area moment of inertia and/or the fiber perimeter and interfacial area. This paper presents initial results from manufacturing carbon fiber materials to assess the impacts of carbon fiber size on tested composite compressive performance with projected neutral or even beneficial impact on fiber and composite manufacturing economics. Carbon fiber systems with increasing size illustrate a favorable correlation for compressive performance greater than predicted from a micromechanical failure model. The manufacturing and mechanical test results support the hypothesis of this work that alterations to fiber geometry can be used to produce improvements of the compressive strength of carbon fiber reinforced polymers and provide incentive for related work in designing alternative shapes to further enhance compressive performance.

42 ENGINEERING↗
DOE OSTI2024

Characterization of coal extracts and their performances as binder for new carbon-based structural units

Coal production and usage have been declining in the past decade due to the transition to renewable energies, environmental regulations to reduce environmental pollution due to direct coal combustion from coal-fired power stations, and administrative policies. This downturn has created tremendous challenges for the coal industry, and many states rely on revenue from coal mining. Developing new valorization routes with high coal demand is essential and using coal-derived materials to make engineered products does offer an eco-friendly, attractive, and value-added use. This paper presents a brief introduction to the extraction process to derive coal deposits, extracts, and residuals (CDERs) and a comprehensive characterization analysis to determine the suitability of CDERs as binders to produce an innovative construction material named the carbon-based structural unit (CSU). CDERs are used to prepare the CSU with or without using mesophase pitch (MP) as binders and selected CDERs are heat-treated and pressure-heat-treated to improve their binding capabilities. The performances of the CSU specimens are evaluated based on compressive strength, density, and thermal conductivity. The CSU specimens mixed with distilled residue (DR) to pyrolysis char (PC) ratio of 1:2 achieve a compressive strength of 54.85 MPa, which is higher than the compressive strength of 30 MPa of normal concrete. The CSU specimens with deposit (De) and tetralin insoluble (TI) require the addition of MP to obtain compressive strengths greater than 30 MPa. Furthermore, these CSU specimens possess a relatively low density of about 1 g/cm 3 and low thermal conductivity of less than 0.25 W/m.K.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Chain elongators, friends, and foes

Bioproduction of medium chain carboxylic acids has recently emerged as an alternative strategy to valorize low-value organic waste and side-streams. Key to this route is chain elongation, an anaerobic microbial process driven by ethanol, lactic acid, or carbohydrates. Additionally, because these technologies use wastes as feedstocks, mixed microbial communities are often considered as biocatalysts. Understanding and steering these microbiomes is key to optimize bioprocess performance. From a meta-analysis of publicly available sequencing data, we (i) explore how the current collection of isolated chain elongators compares to microbiome members, (ii) discuss the main beneficial and antagonistic interactions with community partners, and (iii) identify the key research gaps and needs to help understand chain elongation microbiomes, and design/steer these novel bioproduction processes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Overcoming barriers to medium-chain fatty alcohol production

Medium-chain fatty alcohols ( mc FaOHs) are aliphatic primary alcohols containing six to twelve carbons that are widely used in materials, pharmaceuticals, and cosmetics. Microbial biosynthesis has been touted as a route to less-abundant chain-length molecules and as a sustainable alternative to current petrochemical processes. Several metabolic engineering strategies for producing mc FaOHs have been demonstrated in the literature, yet processes continue to suffer from poor selectivity and mc FaOH toxicity, leading to reduced titers, rates, and yields of the desired compounds. Here, this opinion examines the current state of microbial mc FaOH biosynthesis, summarizing engineering efforts to tailor selectivity and improve product tolerance by implementing engineering strategies that circumvent or overcome mc FaOH toxicity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Stochastic agent-based model for predicting turbine-scale raptor movements during updraft-subsidized directional flights

Rapid expansion of wind energy development across the world has highlighted the need to better understand turbine-caused avian mortality. The risk to golden eagles (Aquila chrysaetos) is of particular concern due to their small population size and conservation status. Golden eagles subsidize their flight in part by soaring in orographic updrafts, which can place them in conflict with wind turbines utilizing the same low-altitude wind resource. Understanding the behavior of soaring raptors in varying atmospheric conditions can therefore be relevant to predicting and mitigating their risk of collision. We present a predictive movement model that simulates individual paths of golden eagles during directional flight (such as migration) that is subsidized by orographic updraft. We modeled eagles in a 50 km by 50 km study area in Wyoming containing three wind power plants with documented golden eagle collisions with turbines. The movement model is applicable to any region where ground elevation is known at turbine scale (50 m) and wind conditions are known at facility scale (3 km). For a given set of atmospheric conditions, the model simulates movements of thousands of orographic soaring eagles to produce a density map quantifying the relative probability of eagle presence. We validated the simulated tracks with GPS telemetry data showing four directional tracks made by golden eagles transiting through the area in 2019 and 2020. For each eagle track, validation was performed using the ratio of the model-simulated eagle presence likelihood with uniform eagle presence and the presence computed using directed random-walk movements. We found that the predictive performance of the model was significantly better (likelihood ratio 1) for low-altitude movements than high-altitude movements that can involve thermal-soaring. We employed the model to produce seasonal presence maps for migrating golden eagles. We found significant turbine-level variations in eagle presence between northerly and southerly migration routes through the study area. Overall, the proposed model offers a generalizable, probabilistic, and predictive tool to assist wind energy developers, ecologists, wildlife managers, and industry consultants in estimating the potential for conflict between soaring birds and wind turbines, thereby reducing the need for site-specific data on golden eagle movements.

17 WIND ENERGY↗
DOE OSTI2025

One-step atmospheric microplasma synthesis of an NMC-type lithium-ion battery cathode

The manufacture of battery cathode materials is the most energy-intensive step in the production of commercial lithium-ion batteries; specifically, the synthesis of the widely used transition metal oxide cathodes can require tens of hours at temperatures exceeding 700 °C. Attempts to limit the reaction time and energy required to form crystalline cathode materials often still include a heating or calcination step. This communication aims to highlight a nascent yet novel synthesis route: a one-step atmospheric microplasma process for synthesizing cathode particles in less than one second. The hollow-tube reactor employed produces crystalline particles measuring 0.1–3 μm in diameter, displays narrow XRD peaks corresponding to the 003, 104, and 101 planes, and exhibits anodic redox behavior at 3.75 V vs. lithium—characteristic of transition-metal oxide cathode materials—all without requiring an additional calcination step.

25 ENERGY STORAGE↗
DOE OSTI2020

Novel structured Sm 0.5 Sr 0.5 CoO 3-δ cathode for intermediate and low temperature solid oxide fuel cells

The performance of solid oxide fuel cell (SOFC) can be significantly enhanced using nanostructured cathode obtained by infiltration/impregnation method. However, this method has been limited due to multi-step preparation process and instability of the obtained nanoscale catalyst under long-term operation. We report here a simple, cost-effective and reproducible approach to fabricate nanostructured Sm 0.5 Sr 0.5 CoO 3-δ (SSC) cathode with enhanced performance and stability. The SSC powders were prepared by a freeze-drying method through a combustion route using Pluronic F-127 as the template. The SSC freeze-drying powders have unique morphology and a significant phase change at 750 °C. The oxygen non-stoichiometry (δ) of the SSC powders is 0.18 at room temperature, while the δ values are 0.66, 0.75 and 0.90 at 600 °C, 700 °C and 800 °C, respectively. The novel structured SSC cathode was prepared by screen-printing and one-step sintering process using the freeze-drying SSC powders. Maximum cell power densities of 0.83 and 1.13 W cm -2 are achieved at 550 °C and 600 °C, respectively for single cells with the novel structured SSC cathode. Moreover, single cells show excellent stability at 600 °C under a current density of 600 mA cm -2 over 250 h. This work expands opportunities to exploit nanotechnology in a wide range of intermediate and low temperature energy conversion devices.

25 ENERGY STORAGE↗
DOE OSTI2023

C@SnS 2 core-shell 0D/2D nanocomposite with excellent electrochemical performance as lithium-ion battery anode

C@SnS 2 core-shell 0D/2D nanocomposite was successfully prepared by a one-step hydrothermal method. The SnS 2 nanosheets were heterogeneously nucleated and grown on the surface of carbon spheres. As an anode for lithium-ion batteries, the electrochemical performance of the C@SnS 2 composite outperforms that of SnS 2 nanoflowers. After 100 cycles, the reversible discharge specific capacity reaches an impressive value of 802 mAh g -1 at a current density of 100 mA g -1 . Even after 600 cycles, the discharge specific capacity remains a value of 442 mAh g -1 , under a high current density of 1 A g -1 . Further, this remarkable lithium-ion storage performance can be attributed to the unique core-shell nanostructure and the synergy between SnS 2 nanosheets and carbon spheres. This study advances our understanding of the vital role of carbon in fabricating nano-heterojunction or composite electrodes and provides a feasible route to significantly improve the electrochemical properties of SnS 2 and other metal sulfides.

25 ENERGY STORAGE↗
DOE OSTI2024

Aqueous solution-based synthesis approach for carbon-disordered rocksalt composite cathode development and its limitations

Disordered rocksalt cathodes exhibit high specific capacities and high energy density; however, their low electronic conductivity poses a great challenge. Herein, we explored an aqueous-solution-based synthesis route that involves controlling the surface charges of Li 1.2 Mn 0.6 Ti 0.2 O 1.8 F 0.2 (LMTOF) to be anchored by a few-layer reduced graphene oxide (rGO) for the first time. The uniform rGO wrapping on the surface of the LMTOF particles is achieved by electrostatic attraction between the negatively charged rGO and positively charged LMTOF particles. Although the initial specific capacity of rGO-LMTOF composite increased by 58 % compared to the pristine LMTOF, the composite experienced a severe capacity fade over cycling. The synthesis process in an aqueous medium resulted in Li + /H + exchange and TM dissolution as evidenced from inductively coupled plasmon analysis and X-ray diffraction analysis. Therefore, this work suggests the search for alternative media or conditions for the synthesis of carbon-disordered rock salt cathode composite.

25 ENERGY STORAGE↗