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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Navigating the Noise: Bringing Clarity to ML Parameterization Design With O $\boldsymbol{\mathcal{O}}$(100) Ensembles

Abstract Machine‐learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high‐resolution physics without the cost of explicit simulation. However, uncertainty about the relationship between offline and online performance (i.e., when integrated with a large‐scale general circulation model) hinders their development. Much of this uncertainty stems from limited sampling of the noisy, emergent effects of upstream ML design decisions on downstream online hybrid simulation. Our work rectifies the sampling issue via the construction of a semi‐automated, end‐to‐end pipeline for size ensembles of hybrid simulations, revealing important nuances in how systematic reductions in offline error manifest in changes to online error and online stability. For example, removing dropout and switching from a Mean Squared Error to a Mean Absolute Error loss both reduce offline error, but they have opposite effects on online error and online stability. Other design decisions, like incorporating memory, converting moisture input from specific humidity to relative humidity, using batch normalization, and training on multiple climates do not come with any such compromises. Finally, we show that ensemble sizes of may be necessary to reliably detect causally relevant differences online. By enabling rapid online experimentation at scale, we can empirically settle debates regarding subgrid ML parameterization design that would have otherwise remained unresolved in the noise.

Lin, Jerry [Department of Earth System Sciences Un↗
DOE OSTI2020

Open Source Visualization and Analysis Platform for 3D Reconstructions of Materials by Transmission Electron Microscopy

Three-dimensional characterization of materials at the nano- and meso-scale has become possible with transmission and scanning transmission electron microscopes (S/TEM). Its importance has extended to a wide class of nanomaterials such as hydrogen fuel cells, solar cells, industrial catalysts, new battery materials and semiconductor devices, as well as spanning high-tech industry, universities, and national labs. While capable instrumentation is abundant, this rapidly expanding demand for high-resolution tomography is bottlenecked by software that is instead tailored for lower-dose, biological applications and not optimized for higher-resolution materials applications. Existing tomography fails to utilize the chemical information provided by S/TEM spectrometers. To address this problem, this project delivered a scalable, fully functional, freely-distributable, open Source materials tomography package with a modern user interface that enables automated acquisition, alignment, and real-time reconstruction of raw tomography data, and provides advanced segmentation, three-dimensional chemical visualization and analysis optimized for materials applications. It has established an extendable framework capable of automation for high-throughput for the tomography of materials from data acquisition to visualization. Phase I and II delivered a full-featured, cross-platform, clean and integrated application for S/TEM materials tomography. It can read projection data from the microscope, with graphical tools for alignment of data, tomographic reconstruction, segmentation, and visualization of the reconstructed 3D volume. The entire pipeline can be saved to an XML state file, enabling fully reproducible data processing and analysis with a full Python environment for the development of custom algorithms, processing, and analysis requirements. Phase IIB extended the application to provide real-time tomographic reconstruction, "live updates" as data is processed, analysis of big data, and multi-channel tomography. Here, quantitative assessment of nanomaterials can occur as data is being recorded on a 3D visualization platform that accommodates additional information from multiple channels. We provide unmatched high-throughput tomography, where the complete tomographic pipeline from measurement to 3D visualization can occur rapidly. The project was extended to offer capabilities for micro-CT, X-ray, neutron, focused ion beam, atomic electron tomography and atom probe tomography. With over 600 transmission electron microscopes worldwide and approximately 50 coming online each year, the demand and impact of an open-source tomography tool is large. Significant opportunities exist in high-tech industry, universities, and national labs to enable or enhance three-dimensional imaging at the nanoscale and bring automated high-throughput approaches that will accelerate progress in materials characterization and metrology. The project supports a service based business model by enabling lab-specific acquisition and processing customization and integration-support and development that will be provided into Phase III and beyond.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Forecasting ground-based sensitivity to the Rayleigh scattering of the CMB in the presence of astrophysical foregrounds

The Rayleigh scattering of cosmic microwave background (CMB) photons off the neutral hydrogen produced during recombination effectively creates an additional scattering surface after recombination that encodes new cosmological information, including the expansion and ionization history of the universe. A first detection of Rayleigh scattering is a tantalizing target for next-generation CMB experiments. We have developed a Rayleigh scattering forecasting pipeline that includes instrumental effects, atmospheric noise, and astrophysical foregrounds (e.g., Galactic dust, cosmic infrared background, or CIB, and the thermal Sunyaev-Zel'dovich effect). We forecast the Rayleigh scattering detection significance for several upcoming ground-based experiments, including SPT-3G+, Simons Observatory, CCAT-prime, and CMB-S4, and examine the limitations from atmospheric and astrophysical foregrounds as well as potential mitigation strategies. When combined with Planck data, we estimate that the ground-based experiments will detect Rayleigh scattering with a significance between 1.6 and 3.7, primarily limited by atmospheric noise and the CIB.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

How Climate and Data Quality Impact Photovoltaic Performance Loss Rate Estimations

Different data pipelines and statistical methods are applied to photovoltaic (PV) performance datasets to quantify the performance loss rate (PLR). Since the real values of PLR are unknown, a variety of unvalidated values are reported. As such, the PV industry commonly assumes PLR based on statistically extracted ranges from the literature. However, the accuracy and uncertainty of PLR depend on several parameters including seasonality, local climatic conditions, and the response of a particular PV technology. In addition, the specific data pipeline and statistical method used affect the accuracy and uncertainty. To provide insights, a framework of (≈200 million) synthetic simulations of PV performance datasets using data from different climates is developed. Time series with known PLR and data quality are synthesized, and large parametric studies are conducted to examine the accuracy and uncertainty of different statistical approaches over the contiguous US, with an emphasis on the publicly available and “standardized” library, RdTools . In the results, it is confirmed that PLRs from RdTools are unbiased on average, but the accuracy and uncertainty of individual PLR estimates vary with climate zone, data quality, PV technology, and choice of analysis workflow. Best practices and improvement recommendations based on the findings of this study are provided.

14 SOLAR ENERGY↗
DOE OSTI2020

Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm

Abstract We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk materials. The test suite, Matbench, is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. Tasks include predicting optical, thermal, electronic, thermodynamic, tensile, and elastic properties given a material’s composition and/or crystal structure. The reference algorithm, Automatminer, is a highly-extensible, fully automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning. We test Automatminer on the Matbench test suite and compare its predictive power with state-of-the-art crystal graph neural networks and a traditional descriptor-based Random Forest model. We find Automatminer achieves the best performance on 8 of 13 tasks in the benchmark. We also show our test suite is capable of exposing predictive advantages of each algorithm—namely, that crystal graph methods appear to outperform traditional machine learning methods given ~10 4 or greater data points. We encourage evaluating materials ML algorithms on the Matbench benchmark and comparing them against the latest version of Automatminer.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Incorporating Wind Turbine Choice in High-Resolution Geospatial Supply Curve and Capacity Expansion Models

To achieve national decarbonization goals, U.S. annual deployment of wind energy will need to increase by at least fivefold compared to the recent past. Modeling and analysis frameworks can help inform where and how wind energy deployment might occur and thereby help enable the achievement of decarbonization goals. However, most prior wind energy modeling and analysis studies rely on generalized representations of wind energy technologies. Since wind energy technology advancements are expected to increase the competitiveness of wind energy, it is important to incorporate more detailed representations of turbine technology into wind energy modeling. Here we present a new method that incorporates wind turbine choice into the technology representation of land-based wind energy in long-term planning models. Our method integrates three previously published modeling and analysis capabilities: 1) bottom-up cost modeling to estimate future technology costs, 2) geospatial modeling to represent siting decisions, and 3) power sector modeling to evaluate potential deployment. We refer to this approach as a "customized turbine choice" methodology because it creates a composite turbine scenario by choosing from multiple wind turbine technologies—using site-specific optimized turbine layout and selecting the least-cost technology at each location. We demonstrate the capabilities of this new modeling pipeline by examining how the selection of four different wind turbine configurations might evolve from 2021 through 2040. Our results show that using our customized turbine choice methodology could lead to higher estimates for wind future deployment, which indicates that more simplified modeling might underestimate the role that wind energy could play in meeting decarbonization goals. Future research is needed to further explore the implications of turbine choice and to better inform technology researchers, original equipment manufacturers, and other wind industry stakeholders about the market potential of different wind turbine technologies.

17 WIND ENERGY↗
DOE OSTI2024

Dissolution of Nb(C,N) During Post-weld Heat-Treatment of Electric Resistance Welded X70 Line Pipe

The effect of peak temperature for post-weld heat treatment (PWHT) on the dissolution of Nb-rich carbonitrides, Nb(C,N), in the weldline of an electric resistance welded (ERW) X70 grade pipeline steel was investigated. Selected-area electron diffraction patterns and corresponding electron energy loss spectroscopy mappings revealed both Nb-rich carbonitrides and Ti-rich nitrides, (Ti,Nb)N, in the sample heat-treated to 1080 °C peak temperature. However, only Ti-rich nitrides were revealed in the sample heat-treated to 1220 °C peak temperature. During continuous heating to higher than the Ac3 temperature, i.e., near 1100 °C temperature, the dissolution of Nb(C,N) was observed in dilation and derivative of dilation. Notably, this experimental result agreed with the predictions by a DICTRA model. Furthermore, a phenomenological model was developed to explain the volume shrinkage during the dissolution of Nb(C,N) precipitates.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Deep learning-driven insights into super protein complexes for outer membrane protein biogenesis in bacteria

To reach their final destinations, outer membrane proteins (OMPs) of gram-negative bacteria undertake an eventful journey beginning in the cytosol. Multiple molecular machines, chaperones, proteases, and other enzymes facilitate the translocation and assembly of OMPs. These helpers usually associate, often transiently, forming large protein assemblies. They are not well understood due to experimental challenges in capturing and characterizing protein-protein interactions (PPIs), especially transient ones. Using AF2Complex, we introduce a high-throughput, deep learning pipeline to identify PPIs within the Escherichia coli cell envelope and apply it to several proteins from an OMP biogenesis pathway. Among the top confident hits obtained from screening ~1500 envelope proteins, we find not only expected interactions but also unexpected ones with profound implications. Subsequently, we predict atomic structures for these protein complexes. These structures, typically of high confidence, explain experimental observations and lead to mechanistic hypotheses for how a chaperone assists a nascent, precursor OMP emerging from a translocon, how another chaperone prevents it from aggregating and docks to a β-barrel assembly port, and how a protease performs quality control. This work presents a general strategy for investigating biological pathways by using structural insights gained from deep learning-based predictions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

DiffLense: a conditional diffusion model for super-resolution of gravitational lensing data

Abstract Gravitational lensing data is frequently collected at low resolution due to instrumental limitations and observing conditions. Machine learning-based super-resolution techniques offer a method to enhance the resolution of these images, enabling more precise measurements of lensing effects and a better understanding of the matter distribution in the lensing system. This enhancement can significantly improve our knowledge of the distribution of mass within the lensing galaxy and its environment, as well as the properties of the background source being lensed. Traditional super-resolution techniques typically learn a mapping function from lower-resolution to higher-resolution samples. However, these methods are often constrained by their dependence on optimizing a fixed distance function, which can result in the loss of intricate details crucial for astrophysical analysis. In this work, we introduce DiffLense , a novel super-resolution pipeline based on a conditional diffusion model specifically designed to enhance the resolution of gravitational lensing images obtained from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). Our approach adopts a generative model, leveraging the detailed structural information present in Hubble space telescope (HST) counterparts. The diffusion model, trained to generate HST data, is conditioned on HSC data pre-processed with denoising techniques and thresholding to significantly reduce noise and background interference. This process leads to a more distinct and less overlapping conditional distribution during the model’s training phase. We demonstrate that DiffLense outperforms existing state-of-the-art single-image super-resolution techniques, particularly in retaining the fine details necessary for astrophysical analyses.

Computer Science↗
DOE OSTI2025

Efficient mutagenesis and genotyping of maize inbreds using biolistics, multiplex CRISPR/Cas9 editing, and Indel-Selective PCR

CRISPR/Cas9 based genome editing has advanced our understanding of a myriad of important biological phenomena. Important challenges to multiplex genome editing in maize include assembly of large complex DNA constructs, few genotypes with efficient transformation systems, and costly/labor-intensive genotyping methods. Here we present an approach for multiplex CRISPR/Cas9 genome editing system that delivers a single compact DNA construct via biolistics to Type I embryogenic calli, followed by a novel efficient genotyping assay to identify desirable editing outcomes. We first demonstrate the creation of heritable mutations at multiple target sites within the same gene. Next, we successfully created individual and stacked mutations for multiple members of a gene family. Genome sequencing found off-target mutations are rare. Multiplex genome editing was achieved for both the highly transformable inbred line H99 and Illinois Low Protein1 (ILP1), a genotype where transformation has not previously been reported. In addition to screening transformation events for deletion alleles by PCR, we also designed PCR assays that selectively amplify deletion or insertion of a single nucleotide, the most common outcome from DNA repair of CRISPR/Cas9 breaks by non-homologous end-joining. The Indel-Selective PCR (IS-PCR) method enabled rapid tracking of multiple edited alleles in progeny populations. The ‘end to end’ pipeline presented here for multiplexed CRISPR/Cas9 mutagenesis can be applied to accelerate maize functional genomics in a broader diversity of genetic backgrounds.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

The Breakthrough Listen Search for Intelligent Life: Searching for Technosignatures in Observations of TESS Targets of Interest

Exoplanetary systems are prime targets for the Search for Extraterrestrial Intelligence (SETI). With the recent uptick in the identification of candidate and confirmed exoplanets through the work of missions like the Transiting Exoplanet Survey Satellite (TESS), we are beginning to understand that Earth-like planets are common. In this work, we extend the Breakthrough Listen (BL) search for extraterrestrial intelligence to include targeted searches of stars identified by TESS as potential exoplanet hosts. We report on 113 30 minute cadence observations collected for 28 targets selected from the TESS Input Catalog from among those identified as containing signatures of transiting planets. The targets were searched for narrowband signals from 1 to 11 GHz using the turboSETI pipeline architecture modified for compatibility with the Google Cloud environment. Data were searched for drift rates of ±4 Hz s{sup −1} above a minimum signal-to-noise threshold of 10, following the parameters of previous searches conducted by Price et al. and Enriquez et al. The observations presented in this work establish some of the deepest limits to date over such a wide band (1–11 GHz) for life beyond Earth. We determine that fewer than 12.72% of the observed targets possess transmitters operating at these frequencies with an equivalent isotropic radiated power greater than our derived threshold of 4.9×10{sup 14} W.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

FeGenie: a comprehensive tool for the identification of iron genes and iron gene neighborhoods in genomes and metagenome assemblies

Iron is a micronutrient for nearly all life on Earth. It can be used as an electron donor and electron acceptor by iron-oxidizing and iron-reducing microorganisms and is used in a variety of biological processes, including photosynthesis and respiration. While it is the fourth most abundant metal in the Earth’s crust, iron is often limiting for growth in oxic environments because it is readily oxidized and precipitated. Much of our understanding of how microorganisms compete for and utilize iron is based on laboratory experiments. However, the advent of next-generation sequencing and surge in publicly available sequence data has made it possible to probe the structure and function of microbial communities in the environment. To bridge the gap between our understanding of iron acquisition, iron redox cycling, iron storage, and magnetosome formation in model microorganisms and the plethora of sequence data available from environmental studies, we have created a comprehensive database of hidden Markov models (HMMs) based on genes related to iron acquisition, storage, and reduction/oxidation in Bacteria and Archaea. Along with this database, we present FeGenie, a bioinformatics tool that accepts genome and metagenome assemblies as input and uses our comprehensive HMM database to annotate provided datasets with respect to iron-related genes and gene neighborhood. An important contribution of this tool is the efficient identification of genes involved in iron oxidation and dissimilatory iron reduction, which have been largely overlooked by standard annotation pipelines. We validated FeGenie against a selected set of 28 isolate genomes and showcase its utility in exploring iron genes present in 27 metagenomes, 4 isolate genomes from human oral biofilms, and 17 genomes from candidate organisms, including members of the candidate phyla radiation. We show that FeGenie accurately identifies iron genes in isolates. Furthermore, analysis of metagenomes using FeGenie demonstrates that the iron gene repertoire and abundance of each environment is correlated with iron richness. While this tool will not replace the reliability of culture-dependent analyses of microbial physiology, it provides reliable predictions derived from the most up-to-date genetic markers. FeGenie’s database will be maintained and continually updated as new genes are discovered.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Time-Resolved Geometric Feature Tracking Elucidates Laser-Induced Keyhole Dynamics

In this report, during laser melting of metals, localized metal evaporation resulting in the formation of a keyhole shaped cavity can occur if processing conditions are chosen with high power density. An unstable keyhole can have deleterious effects in certain applications (e.g., laser powder bed fusion) as it increases the likelihood of producing defects such as porosity. In this work, we propose a pipeline that enables complete segmentation and extraction of various geometric features in keyholing conditions. In situ synchrotron high-speed X-ray visualization at the Advanced Photon Source provides large datasets of experimental images with a high spatio-temporal resolution across a range of laser parameters for Ti-6Al- 4 V. Computer vision image processing techniques were used to extract time-resolved quantitative geometric features (e.g., depth, width, front wall angle) throughout keyhole evolution which were subsequently analyzed to understand the relationship between the variation of local keyhole geometry and processing conditions. This analysis is the first to employ a data-driven approach to further our understanding of the keyholing process regime.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A network-enabled pipeline for gene discovery and validation in non-model plant species

Identifying key regulators of important genes in non-model crop species is challenging due to limited multi-omics resources. To address this, we introduce the network-enabled gene discovery pipeline NEEDLE, a user-friendly tool that systematically generates coexpression gene network modules, measures gene connectivity, and establishes network hierarchy to pinpoint key transcriptional regulators from dynamic transcriptome datasets. After validating its accuracy with two independent datasets, we applied NEEDLE to identify transcription factors (TFs) regulating the expression of cellulose synthase-like F6 ( CSLF6 ), a crucial cell wall biosynthetic gene, in Brachypodium and sorghum. Our analyses uncover regulators of CSLF6 and also shed light on the evolutionary conservation or divergence of gene regulatory elements among grass species. These results highlight NEEDLE’s capability to provide biologically relevant TF predictions and demonstrate its value for non-model plant species with dynamic transcriptome datasets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

AI-Accelerated Design of Targeted Covalent Inhibitors for SARS-CoV-2

Direct-acting antivirals for the treatment of the COVID-19 pandemic caused by the SARS-CoV-2 virus are needed to complement vaccination efforts. Given the ongoing emergence of new variants, automated experimentation, and active learning based fast workflows for antiviral lead discovery remain critical to our ability to address the pandemic’s evolution in a timely manner. While several such pipelines have been introduced to discover candidates with noncovalent interactions with the main protease (M pro ), here we developed a closed-loop artificial intelligence pipeline to design electrophilic warhead-based covalent candidates. Here, this work introduces a deep learning-assisted automated computational workflow to introduce linkers and an electrophilic “warhead” to design covalent candidates and incorporates cutting-edge experimental techniques for validation. Using this process, promising candidates in the library were screened, and several potential hits were identified and tested experimentally using native mass spectrometry and fluorescence resonance energy transfer (FRET)-based screening assays. We identified four chloroacetamide-based covalent inhibitors of M pro with micromolar affinities (K I of 5.27 μM) using our pipeline. Experimentally resolved binding modes for each compound were determined using room-temperature X-ray crystallography, which is consistent with the predicted poses. The induced conformational changes based on molecular dynamics simulations further suggest that the dynamics may be an important factor to further improve selectivity, thereby effectively lowering KI and reducing toxicity. These results demonstrate the utility of our modular and data-driven approach for potent and selective covalent inhibitor discovery and provide a platform to apply it to other emerging targets.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Unveiling the Arsenal of Apple Bitter Rot Fungi: Comparative Genomics Identifies Candidate Effectors, CAZymes, and Biosynthetic Gene Clusters in Colletotrichum Species

The bitter rot of apple is caused by Colletotrichum spp. and is a serious pre-harvest disease that can manifest in postharvest losses on harvested fruit. In this study, we obtained genome sequences from four different species, C. chrysophilum, C. noveboracense, C. nupharicola, and C. fioriniae, that infect apple and cause diseases on other fruits, vegetables, and flowers. Our genomic data were obtained from isolates/species that have not yet been sequenced and represent geographic-specific regions. Genome sequencing allowed for the construction of phylogenetic trees, which corroborated the overall concordance observed in prior MLST studies. Bioinformatic pipelines were used to discover CAZyme, effector, and secondary metabolic (SM) gene clusters in all nine Colletotrichum isolates. We found redundancy and a high level of similarity across species regarding CAZyme classes and predicted cytoplastic and apoplastic effectors. SM gene clusters displayed the most diversity in type and the most common cluster was one that encodes genes involved in the production of alternapyrone. Our study provides a solid platform to identify targets for functional studies that underpin pathogenicity, virulence, and/or quiescence that can be targeted for the development of new control strategies. With these new genomics resources, exploration via omics-based technologies using these isolates will help ascertain the biological underpinnings of their widespread success and observed geographic dominance in specific areas throughout the country.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Long- and short-read sequencing methods discover distinct circular RNA pools in Lotus japonicus

Circular RNAs (circRNAs) are covalently closed single-stranded RNAs, generated through a back-splicing process that links a downstream 5′ site to an upstream 3′ end. The only distinction in the sequence between circRNA and their linear cognate RNA is the back splice junction. Their low abundance and sequence similarity with their linear origin RNA have made the discovery and identification of circRNA challenging. We have identified almost 6000 novel circRNAs from Lotus japonicus leaf tissue using different enrichment, amplification, and sequencing methods as well as alternative bioinformatics pipelines. The different methodologies identified different pools of circRNA with little overlap. We validated circRNA identified by the different methods using reverse transcription polymerase chain reaction and characterized sequence variations using nanopore sequencing. We compared validated circRNA identified in L. japonicus to other plant species and showed conservation of high-confidence circRNA-expressing genes. This is the first identification of L. japonicus circRNA and provides a resource for further characterization of their function in gene regulation. CircRNAs identified in this study originated from genes involved in all biological functions of eukaryotic cells. The comparison of methodologies and technologies to sequence, identify, analyze, and validate circRNA from plant tissues will enable further research to characterize the function and biogenesis of circRNA in L. japonicus.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Magnetostrictive materials for enhanced sensors and electronic components

Magnetostriction is a property of magnetic materials that causes them to change their shape or dimensions when their magnetization changes. Low-cost, mechanically robust magnetostrictive sensors would be valuable for a wide range of applications addressing DOE's energy, environmental, and national security missions. Several examples include monitoring internal conditions of pipelines, enhancing implantable systems for the human body, and improving the electrical grid by providing real time sensors for detecting high impedance faults that may act as ignition sources for forest fires. Historically, magnetostrictive materials tended to be expensive and mechanically brittle, but recent developments in iron-based alloys, especially those that include dilute solutions of rare earth elements, have demonstrated useful magnetostriction values, mechanical robustness, and low costs. This project investigated addition of dilute cerium doping on the magnetostrictive properties of Fe-Ga and Fe-Al alloys. Advanced manufacturing techniques were applied to these materials to take advantage of rapid cooling of the alloy to stabilize desired phases and to make near net shape coupons. These coupons develop significant texturing, offering a route to target microstructures with exceptional performance.

36 MATERIALS SCIENCE↗