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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Application of chord length distributions and principal component analysis for quantification and representation of diverse polycrystalline microstructures

Highlights: • Microstructure quantification based on directional chord length distribution and principal component analysis is presented • A boundary-based algorithm of calculations of chord length distributions is introduced • Utility of the quantification framework is shown in three case studies on synthetic, experimental, and simulation datasets • Angularly resolved chord length distribution captures essential details of grain size and shape in diverse microstructures • Low-dimensional representations of directional chord length distributions are obtained using principal component analysis - Abstract: Quantification of mesoscale microstructures of polycrystalline materials is important for a range of practical tasks of materials design and development. The current protocols of quantifying grain size and morphology often rely on microstructure metrics (e.g., mean grain diameter) that overlook important details of the mesostructure. In this work, we present a quantification framework based on directionally resolved chord length distribution and principal component analysis as a means of extracting additional information from 2-D microstructural maps. Towards this end, we first present in detail a method for calculating chord length distribution based on boundary segments available in modern digital datasets (e.g., from microscopy post-processing) and their low-rank representations by principal component analysis. The utility of the proposed framework for capturing grain size, morphology, and their anisotropy for efficient visualization, representation, and specification of polycrystalline microstructures is then demonstrated in case studies on datasets from synthetic generation, experiments (on Ni-base superalloys), and simulations (on steel during recrystallization).

36 MATERIALS SCIENCE↗
DOE OSTI2017

Ultrahigh Elastic Strain Energy Storage in Metal-Oxide-Infiltrated Patterned Hybrid Polymer Nanocomposites

Modulus of resilience, the measure of a material’s capacity to store and release elastic strain energy, is critical for realizing advanced mechanical actuation technologies in micro/nanoelectromechanical systems. In general, engineering the modulus of resilience is difficult because it requires asymmetrically increasing yield strength and Young’s modulus against their mutual scaling behavior. This task becomes further challenging if it needs to be carried out at the nanometer scale. Here, we demonstrate organic–inorganic hybrid composite nanopillars with one of the highest modulus of resilience per density by utilizing vapor-phase aluminum oxide infiltration in lithographically patterned negative photoresist SU-8. In situ nanomechanical measurements reveal a metal-like high yield strength (~500 MPa) with an unusually low, foam-like Young’s modulus (~7 GPa), a unique pairing that yields ultrahigh modulus of resilience, reaching up to ~24 MJ/m 3 as well as exceptional modulus of resilience per density of ~13.4 kJ/kg, surpassing those of most engineering materials. The hybrid polymer nanocomposite features lightweight, ultrahigh tunable modulus of resilience and versatile nanoscale lithographic patternability with potential for application as nanomechanical components which require ultrahigh mechanical resilience and strength.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Radiation dermatitis caused by a bolus effect from an abdominal compression device

American Association of Physicists in Medicine (AAPM) Task Group 176 evaluated the dosimetric effects caused by couch tops and immobilization devices. The report analyzed the extensive physics-based literature on couch tops, stereotactic body radiation therapy (SBRT) frames, and body immobilization bags, while noting the scarcity of clinical reports of skin toxicity because of external devices. Here, we present a clinical case report of grade 1 abdominal skin toxicity owing to an abdominal compression device. We discuss the dosimetric implications of the utilized treatment plan as well as post hoc alternative plans and quantify differences in attenuation and skin dose/build-up between the device, a lower-density alternative device, and an open field. The description of the case includes a 66-year-old male with HER2 amplified poorly differentiated distal esophageal adenocarcinoma treated with neoadjuvant chemo-radiation and the use of an abdominal compression device. Radiation was delivered using volumetric modulated arc therapy (VMAT) with 2 arcs using abdominal compression and image guidance. The total dose was 50.4 Gy delivered over 40 elapsed days. With 2 fractions remaining, the patient developed dermatitis in the area of the compression device. The original treatment plan did not include a contour of the device. Alternative post hoc treatment plans were generated, one to contour the device and a second with anterior avoidance. In conclusion, replanning with the device contoured revealed the bolus effect. The skin dose increased from 27 to 36 Gy. planned target volume (PTV) coverage at 45 Gy was reduced to 76.5% from 95.8%. The second VMAT treatment plan with an anterior avoidance sector and more oblique beam angles maintained PTV coverage and spared the anterior wall, however at the expense of substantially increased dose to lung. This case report provides an important reminder of the bolus effect from external devices such as abdominal compression. Special consideration must be given to contour and/or avoiding beam entrance to the device, and to the use of such devices in patients who may have heightened radiosensitivity, such as those who are human immunodeficiency virus (HIV)–positive.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

Accelerating the discovery of low-energy structure configurations: A computational approach that integrates first-principles calculations, Monte Carlo sampling, and Machine Learning

Finding Minimum Energy Configurations (MECs) is essential in fields such as physics, chemistry, and materials science, as they represent the most stable states of the systems. In particular, identifying such MECs in multi-component alloys considered candidate PFMs is key because it determines the most stable arrangement of atoms within the alloy, directly influencing its phase stability, structural integrity, and thermo-mechanical properties. However, since the search space grows exponentially with the number of atoms considered, obtaining such MECs using computationally expensive first-principles DFT calculations often results in a cumbersome task. To escape the above compromise between physical fidelity and computational efficiency, we have developed a novel physics-based data-driven approach that combines Monte Carlo sampling, first-principles DFT calculations, and Machine Learning to accelerate the discovery of MECs in multi-component alloys. More specifically, we have leveraged well-established Cluster Expansion (CE) techniques with Local Outlier Factor models to establish strategies that enhance the reliability of the CE method. In this work, we demonstrated the capabilities of the proposed approach for the particular case of a tungsten-based quaternary high-entropy alloy. However, the method is applicable to other types of alloys and enables a wide range of applications.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Defect detection in atomic-resolution images via unsupervised learning with translational invariance

Abstract Crystallographic defects can now be routinely imaged at atomic resolution with aberration-corrected scanning transmission electron microscopy (STEM) at high speed, with the potential for vast volumes of data to be acquired in relatively short times or through autonomous experiments that can continue over very long periods. Automatic detection and classification of defects in the STEM images are needed in order to handle the data in an efficient way. However, like many other tasks related to object detection and identification in artificial intelligence, it is challenging to detect and identify defects from STEM images. Furthermore, it is difficult to deal with crystal structures that have many atoms and low symmetries. Previous methods used for defect detection and classification were based on supervised learning, which requires human-labeled data. In this work, we develop an approach for defect detection with unsupervised machine learning based on a one-class support vector machine (OCSVM). We introduce two schemes of image segmentation and data preprocessing, both of which involve taking the Patterson function of each segment as inputs. We demonstrate that this method can be applied to various defects, such as point and line defects in 2D materials and twin boundaries in 3D nanocrystals.

36 MATERIALS SCIENCE↗
DOE OSTI2017

The ultimate mineral processing challenge: Recovery of rare earths, phosphorus and uranium from Florida phosphatic clay

We report phosphate beneficiation in Florida generates more than one tonne of phosphatic clay, or slime, per tonne of phosphate rock produced. Since the start of the practice of large-scale washing and desliming for phosphate beneficiation, more than 2 Gt of slime has accumulated, containing approximately 600 Mt of phosphate rock, 600 kt of rare earth elements (REEs) and 80 million kilograms of uranium. The recovery of these valuable elements from the phosphatic clay is one of the most challenging endeavors in mineral processing, because the clay is extremely dilute, with an average solids concentration of 3 percent, and fine in size, with more than 50 percent having particle size smaller than 2 μm, and it contains nearly 50 percent clay minerals as well as large amounts of magnesium, iron and aluminum. With industry support and under funding from the Critical Materials Institute, the Florida Industrial and Phosphate Research Institute in conjunction with the Oak Ridge National Laboratory undertook the task to recover phosphorus, rare earths and uranium from Florida phosphatic clay. This paper presents the results from the preliminary testing of two approaches. The first approach involves three-stage cycloning using cyclones with diameters of 12.4 cm (5 in.), 5.08 cm (2 in.) and 2.54 cm (1 in.), respectively, to remove clay minerals followed by flotation and leaching. The second approach is a two-step leaching process. In the first step, selective leaching was conducted to remove magnesium, thus allowing the production of phosphoric acid suitable for the manufacture of diammonium phosphate (DAP) in the second leaching step. The results showed that multistage cycloning with small cyclones is necessary to remove clay minerals. Finally, selective leaching at about pH 3.2 using sulfuric acid was found to be effective for removing more than 80 percent of magnesium from the feed with minimal loss of phosphorus.

36 MATERIALS SCIENCE↗
DOE OSTI2017

In Situ Coupling Strategy for the Preparation of FeCo Alloys and Co 4 N Hybrid for Highly Efficient Oxygen Evolution

An in situ coupling approach is developed to create a new highly efficient and durable cobalt-based electrocatalyst for the oxygen evolution reaction (OER). Using a novel cyclotetramerization, a task-specific bimetallic phthalocyanine-based nanoporous organic framework is successfully built as a precursor for the carbonization synthesis of a nonprecious OER electrocatalyst. The resultant material exhibits an excellent OER activity with a low overpotential of 280 mV at a current density of 10 mA cm –2 and high durability in an alkaline medium. This impressive result ranks among the best from known Co-based OER catalysts under the same conditions. The simultaneous installation of multiple diverse cobalt-based active sites, including FeCo alloys and Co 4 N nanoparticles, plays a critical role in achieving this promising OER performance. Lastly, this innovative approach not only enables high-performance OER activity to be achieved but simultaneously provides a means to control the surface features, thereby tuning the catalytic property of the material.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Effect of Particle Spreading Dynamics on Powder Bed Quality in Metal Additive Manufacturing

Powder spreading precedes creation of every new layer in powder bed additive manufacturing (AM). The powder spreading process can lead to powder layer defects such as porosity, poor surface roughness and particle segregation. Therefore, the creation of homogeneous layers is the first task for optimal part printing. Discrete element methods (DEM) powder spreading simulations are typically limited to a single layer and/or small number of particles. Therefore, results from such model configurations may not be generalized to multiple layer processes. In this study, a computationally efficient multi-layer powder spreading DEM simulation model is proposed. The model is calibrated experimentally using static Angle of Repose measurements. The adhesion model parameter, cohesive energy density is related to adhesive surface energy and strain energy release rate parameters. The model results show that interaction between particle and the powder spreading rake leads to noticeable variation in packing density, surface roughness, dynamic angle of repose (AOR), particle size distribution, and particle segregation. Finally, the powder model is experimentally validated using a recoater spreading rig to measure the dynamic AOR at spreading speeds consistent with recoating speeds and layer heights used in AM processes.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Data-centric framework for crystal structure identification in atomistic simulations using machine learning

Atomic-level modeling performed at large scales enables the investigation of mesoscale materials properties with atom-by-atom resolution. The spatial complexity of such cross-scale simulations renders them unsuitable for simple human visual inspection. Instead, specialized structure characterization techniques are required to aid interpretation. These have historically been challenging to construct, requiring significant intuition and effort. Here we propose an alternative framework for a fundamental structural characterization task: classifying atoms according to the crystal structure to which they belong. Our approach is data-centric and favors the employment of Machine Learning over heuristic rules of classification. A group of data-science tools and simple local descriptors of atomic structure are employed together with an efficient synthetic training set. We also introduce the first standard and publicly available benchmark data set for evaluation of algorithms for crystal-structure classification. Further, it is demonstrated that our data-centric framework outperforms all of the most popular heuristic methods—especially at high temperatures when lattices are the most distorted—while introducing a systematic route for generalization to new crystal structures. Moreover, through the use of outlier detection algorithms our approach is capable of discerning between amorphous atomic motifs (i.e., noncrystalline phases) and unknown crystal structures, making it uniquely suited for exploratory materials synthesis simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Evaluate Compatibility of Alloy 244 in NaCl-MgCl 2

Alloy 244 is a potential material of interest for molten salt reactor (MSR) developers employing molten chloride as fuel and coolant salts because of its improved mechanical properties compared to the austenitic stainless steel 316H and the Ni-base alloy Hastelloy-N. However, data are limited on the corrosion behavior of alloy 244 in molten chloride salts. Experimental data on the corrosion behavior of 244 in chloride salts will provide valuable data to MSR developers while simultaneously aiding in improving mechanistic understanding of the role of salt chemistry, alloying constituents and microstructure of 244. This task evaluated the compatibility of alloy 244 in two different eutectic NaCl–MgCl 2 salt mixtures. Similar corrosion behavior in terms of depths of attack and compositional changes in the alloy subsurface was observed after exposure in both salts for 500 h at 750°C. However, severe corrosion attack was observed after 500 h at 850°C in one of the chloride salt mixture. This was most likely due to the combined effects of the differences in the two salt chemistries, distinct microstructure of 244 at 850°C compared to 750°C, and the temperature dependence of the chemical interactions (thermodynamic and kinetic) between the alloying constituents.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Score-based denoising for atomic structure identification

We propose an effective method for removing thermal vibrations that complicate the task of analyzing complex dynamics in atomistic simulation of condensed matter. Our method iteratively subtracts thermal noises or perturbations in atomic positions using a denoising score function trained on synthetically noised but otherwise perfect crystal lattices. The resulting denoised structures clearly reveal underlying crystal order while retaining disorder associated with crystal defects. Purely geometric, agnostic to interatomic potentials, and trained without inputs from explicit simulations, our denoiser can be applied to simulation data generated from vastly different interatomic interactions. The denoiser is shown to improve existing classification methods, such as common neighbor analysis and polyhedral template matching, reaching perfect classification accuracy on a recent benchmark dataset of thermally perturbed structures up to the melting point. Demonstrated here in a wide variety of atomistic simulation contexts, the denoiser is general, robust, and readily extendable to delineate order from disorder in structurally and chemically complex materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multi-fidelity equations of state and transport coefficient datasets for pulsed-power applications

Reliably simulating experiments relevant to the National Nuclear Security Administration (NNSA) requires a detailed description of material properties across a wide range of conditions. Such properties include the equations of state, charged-particle transport coefficients, and optical properties like the opacity. Together, these properties make up the material models used in radiation-magnetohydrodynamic simulations of nuclear fusion experiments. Many of these models do not incorporate uncertainties in the data used to produce them. It is unknown whether these uncertainties significantly impact the interpretation of simulation results and diagnostics. The purpose of this work is to quantify how such uncertainties impact simulations of pulsed-power experiments. We accomplished this task by first assessing discrepancies between approaches used to generate the data. This included bringing together members of the high-energy-density community spanning the three NNSA laboratories and multiple universities. Then, using these data, we developed a general framework that systematically incorporates physical uncertainties within the material models suitable for uncertainty quantification analyses. The framework utilizes machine learning, Bayesian inference, and incorporates multi-fidelity datasets. We demonstrated the framework by quantifying the impact that material model uncertainties have on simulations of pulsed-power experiments underway on Z at Sandia National Laboratories. As a result of this work, we discovered that modest uncertainties in material models (roughly 20%) correspond to significant uncertainties in the outputs from simulations. Our framework has enabled rapid construction of material models through an automated procedure and allows for the generation of material models of interest to the NNSA.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Determination of the Charge Materials Range in Multistage Charge Burden Optimization for Foundry Furnaces

Optimization of charge burden for foundry furnaces lies in determining a percentage or mass (kg, t) of the share of each charge material in the burden with the lowest unit cost. A classic task of charge burden optimization requires a definition of an objective function, which most often defines the cost of the charge burden and a system of constraints, including technological assumptions such as the balance of chemical elements for the required chemical composition of the charge or molten metal, limitations of the mass fraction of the individual elements of the charge, and others. The solution for such an optimization task is a set of starting values for the devices which weigh and proportion the charge materials for a foundry furnace. The consequences of deviations from proper values, causes by inaccurate weighing or proportioning and human error as well, can be limited by using an appropriately designed and implemented multistage optimization algorithm. This article presents a mathematical model of such an algorithm and discussion of its practical application. The algorithm as described below can be an important element of computer-aided decision to be made by employees in the process of batch compilation or automatically control devices dosing individual batch components. An IT system with an implemented algorithm may significantly increase the efficiency of charge preparation for a foundry furnace, guaranteeing its correct chemical composition despite batch materials dosing errors. It will contribute to a significant improvement of economic indicators of liquid metal production, with the effect of boosting the competitiveness of the foundry industry.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Discovering equations that govern experimental materials stability under environmental stress using scientific machine learning

Abstract While machine learning (ML) in experimental research has demonstrated impressive predictive capabilities, extracting fungible knowledge representations from experimental data remains an elusive task. In this manuscript, we use ML to infer the underlying differential equation (DE) from experimental data of degrading organic-inorganic methylammonium lead iodide (MAPI) perovskite thin films under environmental stressors (elevated temperature, humidity, and light). Using a sparse regression algorithm, we find that the underlying DE governing MAPI degradation across a broad temperature range of 35 to 85 °C is described minimally by a second-order polynomial. This DE corresponds to the Verhulst logistic function, which describes reaction kinetics analogous to self-propagating reactions. We examine the robustness of our conclusions to experimental variance and Gaussian noise and describe the experimental limits within which this methodology can be applied. Our study highlights the promise and challenges associated with ML-aided scientific discovery by demonstrating its application in experimental chemical and materials systems.

36 MATERIALS SCIENCE↗
DOE OSTI2026

ZENN: A thermodynamics-inspired computational framework for heterogeneous data–driven modeling

Traditional entropy-based methods—such as cross-entropy loss in classification problems—have long been essential tools for representing the information uncertainty and physical disorder in data and for developing artificial intelligence algorithms. However, the rapid growth of data across various domains has introduced new challenges, particularly the integration of heterogeneous datasets with intrinsic disparities. To address this, we introduce a zentropy-enhanced neural network (ZENN), extending zentropy theory into the data science domain via intrinsic entropy, enabling more effective learning from heterogeneous data sources. ZENN simultaneously learns both energy and intrinsic entropy components, capturing the underlying structure of multisource data. To support this, we redesign the neural network architecture to better reflect the intrinsic properties and variability inherent in diverse datasets. We demonstrate the effectiveness of ZENN on classification tasks and energy landscape reconstructions, showing its superior generalization capabilities and robustness-particularly in predicting high-order derivatives. In image and text classification tasks, ZENN demonstrates superior generalization by introducing a learnable temperature variable that models latent multisource heterogeneity, allowing it to surpass state-of-the-art models on CIFAR-10/100, BBC News, and AG News. As a practical application in materials science, we employ ZENN to reconstruct the Helmholtz energy landscape of Fe3Pt using data generated from density functional theory and capture key material behaviors, including negative thermal expansion and the critical point in the temperature–pressure space. Overall, this work presents a zentropy-grounded framework for data-driven machine learning, positioning ZENN as a versatile and robust approach for scientific problems involving complex, heterogeneous datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Data-Efficient Generation of Protein Conformational Ensembles with Backbone-to-Side-Chain Transformers

Excitement at the prospect of using data-driven generative models to sample configurational ensembles of biomolecular systems stems from the extraordinary success of these models on a diverse set of high-dimensional sampling tasks. Unlike image generation or even the closely related problem of protein structure prediction, there are currently no data sources with sufficient breadth to parametrize generative models for conformational ensembles. To enable discovery, a fundamentally different approach to building generative models is required: models should be able to propose rare, albeit physical, conformations that may not arise in even the largest data sets. Here, in this work, we introduce a modular strategy to generate conformations based on “backmapping” from a fixed protein backbone that (1) maintains conformational diversity of the side chains and (2) couples the side-chain fluctuations using global information about the protein conformation. Our model combines simple statistical models of side-chain conformations based on rotamer libraries with the now ubiquitous transformer architecture to sample with atomistic accuracy. Together, these ingredients provide a strategy for rapid data acquisition and hence a crucial ingredient for scalable physical simulation with generative neural networks.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Predicting defects in laser powder bed fusion using in-situ thermal imaging data and machine learning

Variation in the local thermal history during the Laser Powder Bed Fusion (LPBF) process in Additive Manufacturing (AM) can cause micropore defects, which add to the uncertainty of the mechanical properties (e.g., fatigue life, tensile strength) of the built materials. In-situ sensing has been proposed for monitoring the AM process to minimize defects, but successful minimization requires establishing a quantitative relationship between the sensing data and the porosity, which is particularly challenging with a large number of variables (e.g., laser speed, power, scan path, powder property). Physics-based modeling can simulate such an in-situ sensing-porosity relationship, but it is computationally costly. In this work, we develop Machine Learning (ML) models that can use in-situ thermographic data to predict the micropore of LPBF stainless steel materials. This work considers two identified key features from the thermal histories: the time above the apparent melting threshold ($\tau$) and the maximum radiance ( T max ). These features are computed, stored for each voxel in the built material, and then used as inputs. The binary state of each voxel, either defective or normal, is the output. Different ML models are trained and tested for the binary classification task. In addition to using the thermal features of each voxel to predict its own state, the thermal features of neighboring voxels are also included as inputs. This is shown to improve the prediction accuracy, which is consistent with thermal transport physics around each voxel contributing to its final state. Among the models trained, the F1 scores on test sets reach above 0.96 for Random Forests. Feature importance analysis based on the ML models shows that T max is more important to the voxel state than $\tau$ . The analysis also finds that the thermal history of the voxels above the present voxel is more influential than those beneath it. Our study significantly extends the capability of using in-situ thermographic data to predict porosity in LPBF materials. Finally, since ML models are fast, they may play integral roles in the optimization and control of such AM technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks

Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretical data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) patterns of metal–organic frameworks (MOFs). To augment the data for training the model, noise was extracted from experimental data and shuffled; then it was merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra. For the first time, one-to-one material identification was achieved. Theoretical MOFs patterns (1012) were augmented to a whole data set of 72 864 samples. It was then randomly shuffled and split into training (58 292 samples) and validation (14 572 samples) data sets at a ratio of 4:1. For the task of discriminating, the optimized model showed the highest identification accuracy of 96.7% for the top 5 ranking on a test data set of 30 hold-out samples. Neighborhood component analysis (NCA) on the experimental XRD samples shows that the samples from the same material are clustered in groups in the NCA map. Analysis on the class activation maps of the last CNN layer further discloses the mechanism by which the CNN model successfully identifies individual MOFs from the XRD patterns. Furthermore, this CNN model trained by the data augmentation technique would not only open numerous potential applications for identifying XRD patterns for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR spectroscopies.

36 MATERIALS SCIENCE↗