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At least 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Magnetic contribution of itinerant electrons to neutron diffraction in the topological antiferromagnet CeAlGe

We report a neutron diffraction study of the magnetic structure of CeAlGe, a candidate topological semimetal that hosts a noncollinear, multi-𝐤 magnetic phase. By measuring both low- and high-momentum-transfer magnetic Bragg peaks within a single experimental setup, we refine a magnetic structure model based solely on localized Ce moments. This model, which differs from that obtained using only high-𝑄 data, quantitatively reproduces the observed intensities, including the (000) zeroth-order magnetic satellites that are especially sensitive to subtle components of the modulation. While a contribution from itinerant electrons to the zeroth satellite cannot be definitively excluded, our analysis reveals no unambiguous evidence for such effects within experimental uncertainty. The refined magnetic structures exhibit topologically nontrivial winding patterns, derived from the fitted magnetic parameters, that support localized, particle-like spin textures with half-integer topological charges. These features provide a natural microscopic origin for the observed topological Hall effect, establishing CeAlGe as a model system where magnetism and topology are intimately linked.

Magnetic coupling↗
DOE OSTI2022

Nanostructures evolution assessment and spectroscopic properties modification induced by electronic energy loss in KTaO 3 crystal

Modifications of micro/nanostructure and photoelectric properties under swift ion irradiation (645 MeV Xe 35+ ) of KTaO 3 crystal with varying electronic energy losses (7.2–31.4 keV/nm) and ion velocities (0.18–5.00 MeV/u) have been studied by combining experimental and calculated approaches. The i-TS calculations combined with molecular dynamics simulations are compared with the experimental observations, revealing the inner track fine structures from individual spherical defects to continuous ion tracks with core–shell morphologies, and a quantitative relationship, including the melting (0.42 eV/atom), damage (0.75 eV/atom), and amorphous (1.71 eV/atom) thresholds, is established to successfully predict the inner disorder/amorphous proportions and track damage morphologies. The surface nanostructures of nanohillocks (isolated or partial overlaps) and nanopits (serious overlaps) directly depend on the combined action of the deposited potential and kinetic energies of incident ions, which induce local melting and sublimation in the near-surface region. Owing to the decreased recombination and the increased separation efficiency between electrons and holes, the higher photogenerated charge carrier mobility enhanced the photocurrent effect, further optimizing the photoconductivity performance in Xe 35+ -irradiated KTaO 3 . Therefore, controlled defect engineering using the ion irradiation technique, as an effective strategy, could design tailored nanostructure systems and regulate photoelectric properties, further promoting the development of novel technological applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Resolving the Valence of Iron Oxides by Resonant Photoemission Spectroscopy

Precisely determining the oxidation states of metal cations within variable-valence transition metal oxides remains a significant challenge, yet it is crucial for understanding and predicting the properties of these technologically important materials. Iron oxides, in particular, exhibit a remarkable diversity of electronic structures due to the variable valence states of iron (Fe 2+ and Fe 3+ ). A quantitative analysis using conventional X-ray photoelectron spectroscopy (XPS) is challenging because of the strong overlap of the Fe 2p XPS peaks from different oxidation states. Here, in this study, we show how this problem can be resolved using Resonant Photoemission Spectroscopy (ResPES), which unambiguously distinguishes Fe oxidation states and spectroscopically estimates the composition ratio of Fe cation valence states in the complex Fe oxides. We demonstrate this in the model case of a FeO 2 monolayer film on Pt(111), showing that the FeO 2 film consists of an equal mixture of Fe 2+ and Fe 3+ cations, yielding an average valence of +2.5, contrary to the +3 valence proposed based on density functional theory (DFT).

Chen, Hao [Lawrence Berkeley National Laboratory (↗
DOE OSTI2024

Exciton engineering of 2D Ruddlesden–Popper perovskites by synergistically tuning the intra and interlayer structures

Designing two-dimensional halide perovskites for high-performance optoelectronic applications requires deep understanding of the structure-property relationship that governs their excitonic behaviors. However, a design framework that considers both intra and interlayer structures modified by the A-site and spacer cations, respectively, has not been developed. Here, we use pressure to synergistically tune the intra and interlayer structures and uncover the structural modulations that result in improved optoelectronic performance. Under applied pressure, (BA) 2 (GA)Pb 2 I 7 exhibits a 72-fold boost of photoluminescence and 10-fold increase of photoconductivity. Based on the observed structural change, we introduce a structural descriptor χ that describes both the intra and interlayer characteristics and establish a general quantitative relationship between χ and photoluminescence quantum yield: smaller χ correlates with minimized trapped excitons and more efficient emission from free excitons. Building on this principle, we design a perovskite (CMA) 2 (FA)Pb 2 I 7 that exhibits a small χ and an impressive photoluminescence quantum yield of 59.3%.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Improving precision and accuracy of genetic mapping with genotyping‐by‐sequencing data in outcrossing species

Abstract Genotyping‐by‐sequencing (GBS) is a widely used strategy for obtaining large numbers of genetic markers in model and non‐model organisms. In crop plants, GBS‐derived marker datasets are frequently used to perform quantitative trait locus (QTL) mapping. In some plant species, however, high heterozygosity and complex genome structure mean that researchers must use care in handling GBS data to conduct QTL mapping most effectively. Such outbred crops include most of the perennial grass and tree species used for bioenergy. To identify strategies for increasing accuracy and precision of QTL mapping using GBS data in outbred crops, we conducted an empirical study of SNP‐calling and genetic map‐building pipeline parameters in a Miscanthus sinensis population, and a complementary simulation study to estimate the relationship between genome‐wide error rate, read depth, and marker number. The bioenergy grass Miscanthus is an obligate outcrossing species with a recent (diploidized) whole‐genome duplication. For the study of empirical M. sinensis data, we compared two SNP‐calling methods (one non‐reference‐based and one reference‐based), a series of depth filters (12×, 20×, 30×, and 40×) and two map‐construction methods (i.e., marker ordering: linkage‐only and order‐corrected based on a reference genome). We found that correcting the order of markers on a linkage map by using a high‐quality reference genome improved QTL precision (shorter confidence intervals). For typical GBS datasets of between 1000 and 5000 markers to build a genetic map for biparental populations, a depth filter set at 30× to 40× applied to outbred populations provided a genome‐wide genotype‐calling error rate of less than 1%, improved accuracy of QTL point estimates and minimized type I errors for identifying QTL. Based on these results, we recommend using a reference genome to correct the marker order of genetic maps and a robust genotype depth filter to improve QTL mapping for outbred crops.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Simulating environmentally-sensitive tree recruitment in vegetation demographic models

Vegetation demographic models (VDMs) endeavor to predict how global forests will respond to climate change. This requires simulating which trees, if any, are able to recruit under changing environmental conditions. We present a new recruitment scheme for VDMs in which functional-type-specific recruitment rates are sensitive to light, soil moisture and the productivity of reproductive trees. We evaluate the scheme by predicting tree recruitment for four tropical tree functional types under varying meteorology and canopy structure at Barro Colorado Island, Panama. We compare predictions to those of a current VDM, quantitative observations and ecological expectations. We find that the scheme improves the magnitude and rank order of recruitment rates among functional types and captures recruitment limitations in response to variable understory light, soil moisture and precipitation regimes. Furthermore, our results indicate that adopting this framework will improve VDM capacity to predict functional-type-specific tree recruitment in response to climate change, thereby improving predictions of future forest distribution, composition and function.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Conformational ensembles reveal the origins of serine protease catalysis

Enzymes exist in ensembles of states that encode the energetics underlying their catalysis. Conformational ensembles built from 1231 structures of 17 serine proteases revealed atomic-level changes across their reaction states. By comparing the enzymatic and solution reaction, we identified molecular features that provide catalysis and quantified their energetic contributions to catalysis. Serine proteases precisely position their reactants in destabilized conformers, creating a downhill energetic gradient that selectively favors the motions required for reaction while limiting off-pathway conformational states. The same catalytic features have repeatedly evolved in proteases and additional enzymes across multiple distinct structural folds. In conclusion, our ensemble-function analyses revealed previously unknown catalytic features, provided quantitative models based on simple physical and chemical principles, and identified motifs recurrent in nature that may inspire enzyme design.

Du, Siyuan [Stanford Univ., CA (United States)] (O↗
DOE OSTI2024

MemFriend: Understanding Memory Performance with Spatial-Temporal Affinity

In HPC applications, memory access behavior is one of the main factors affecting performance. Improving an application’s memory access behavior involves optimizing data layout and/or restructuring code, and requires studying spatial-temporal data locality. Existing data locality analyses focus on single-location metrics and are restricted to evaluating temporal locality. We introduce spatial-temporal affinity metrics that quantify temporal access proximity, forward access correlation, and nearby access correlation between pairs of memory locations. We describe methods for distinguishing between potential vs. realized affinity and for reasoning about affinity at multiple resolutions (3D, 2D, 1D). Finally, we construct spatial-temporal affinity signatures that classify memory behavior and that be used to reason about changes in software (data relayout, code refactoring) or hardware (caching, prefetching). We describe methods for signature visualization, interpretation, and quantitative comparison of signatures. We evaluate our methodology using applications with variants that contrast data structures, data layouts and algorithms. We show that spatial-temporal affinity analysis provides novel insights and enables predictive reasoning about application performance when contrasted with reuse distance analysis.

Suriyakumar, Yasodhadevi↗
DOE OSTI2026

braggedgemodeling

braggedgemodeling (bem) is an open-source Python package for modeling neutron Bragg-edge imaging. It computes the wavelength-dependent total neutron cross-section of a material (coherent and incoherent elastic, coherent and incoherent inelastic scattering, and absorption) from its crystal structure, and implements the March-Dollase texture model and the Jorgensen peak profile, supporting quantitative analysis of energy-resolved neutron imaging data (phase, stress/strain, and texture). Published in the Journal of Open Source Software (2018).

Lin, Jiao [Oak Ridge National Laboratory (ORNL), O↗
DOE OSTI2022

Sparse Data Machine Learning Integration with Theory, Experiment and Uncertainty Quantification: Process-Structure-Property-Performance of Friction Deformation Processing

Computer vision and deep learning tools that advance the ability to establish processing-structure-property-performance (PSPP) relations are presented. The Bayesian binning method for image segmentation enables quantitative analysis of microstructural features in an automated way, while the analysis of shapes and relative orientation of these features reveals local deformation maps indicative of both, material flow and residual stresses due to materials processing. The deep learning method leads to the previous knowledge agnostic mapping of empirically observed microstructural zones in friction stir welding (FSW) process and synthetic microstructure generation capability that is statistically equivalent to experimentally collected data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Theory of Novel Superconductors (Final Technical Report)

This Final Technical Report gives the results of a series of problems related to the origin of superconductivity, the quantum mechanical state of matter where electrical resistance vanishes, in several classes of new materials where the conventional mechanism that binds electrons into pairs based on the electron-phonon interaction apparently fails. In several families of quantum materials including cuprates, iron-based superconductors, and heavy fermion materials, it is believed that the repulsive Coulomb interaction between electrons can create effective attractive interactions, generally in higher pair momentum channels. These "unconventional" superconductors include materials with the highest critical temperatures at ambient pressure known, so it is imperative to understand how superconductivity arises to guide the search for materials that superconduct at or near room temperature, which would have enormous technological impact. A second objective has been to construct a theory that can quantitatively explain the properties of existing materials, based on information from first-principles electronic structure calculations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Interface velocity dependent solute trapping and phase selection during rapid solidification of laser melted hypo-eutectic Al-11at.%Cu alloy

Microstructure formation of a hypo-eutectic Al-11Cu (atom percent) alloy during solidification after laser melting has been studied by transmission electron microscopy (TEM). The evolution of the solid-liquid-interface velocity, V SL , during the solidification process has been determined from direct observation by in-situ TEM. This enabled correlating V SL with development of four distinct microstructure zones. Crystal growth mode transitions from planar to cellular, cellular to dendritic, dendritic to cellular, and cellular to planar, have been observed for the accelerating solid-liquid-interface. The transition from coupled two-phase growth to the single-phase growth occurred for V SL = V a = (0.80±0.05) m/s, where V a is the velocity of absolute stability, at the onset of banded morphology grain formation. The in-situ and post-mortem TEM uniquely permitted determination of the non-equilibrium solidus for the rapidly solidifying Al-11Cu alloy. Experimental evidence for solute clustering and chemical ordering tendencies at length scales on the order of ≤ 5nm has been detected in the single-phase regions of the banded grains. The structural features of the single-phase bands have been interpreted as signatures of ‘frozen in’ configurations present in the liquid boundary layer adjacent to the growing crystal, which has a width of about 3nm. Finally, the nano-scale spatiotemporal resolution experimental TEM studies performed here provided quantitative metrics, e.g. the solidification interface velocity dependent solute concentration of the α-Al phase and the near-atomic scale structure in the single-phase bands, that are uniquely suitable for comparison with theory and model predictions for solidification microstructure development in multicomponent alloys after laser melting.

36 MATERIALS SCIENCE↗
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 OSTI2026

Effect of magneto-mechanical synergism in the process-structure correlation in Fe–C alloys: A phase-field modeling approach

Applied magnetic fields can alter phase equilibria and kinetics in steels; however, quantitatively resolving how magnetic, chemical, and elastic driving forces jointly influence the microstructure remains challenging. We develop a quantitative magneto-mechanically coupled phase-field model for the Fe–C system that couples a CALPHAD-based chemical free energy with demagnetization-field magnetostatics and microelasticity. Here, the model reproduces single- and multi-particle evolution during the α → γ inverse transformation at 1023 K under external fields up to 20 T, including ellipsoidal morphologies observed experimentally at 8 T. Chemically driven growth is isotropic; a magnetic interaction introduces an anisotropic driving force that elongates γ precipitates along the field into ellipsoids, while elastic coherency promotes faceting, yielding elongated cuboidal or “brick-like” particles under combined magneto-elastic coupling. Growth kinetics increase with C content, and decrease with field strength and misfit strain. Multi-particle simulations reveal dipolar interaction-mediated coalescence for field-parallel neighbors and ripening for field-perpendicular neighbors. Incorporating field-dependent diffusivity from experiment slows kinetics as expected; a first-principles-motivated anisotropic diffusivity correction is estimated to be small (<2%). These results establish a process-structure link for magnetically assisted heat treatments of Fe–C alloys and provide guidance for microstructure control via chemo-magneto-mechanical synergism.

Magnetic field↗
DOE OSTI2025

Synthetic Scientific Image Generation with VAE, GAN, and Diffusion Model Architectures

Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive comparative analysis of leading generative architectures, ranging from Variational Autoencoders (VAEs) to Generative Adversarial Networks (GANs) on through to Diffusion Models, in the context of scientific image synthesis. We examine each model's foundational principles, recent architectural advancements, and practical trade-offs. Our evaluation, conducted on domain-specific datasets including microCT scans of rocks and composite fibers, as well as high-resolution images of plant roots, integrates both quantitative metrics (SSIM, LPIPS, FID, CLIPScore) and expert-driven qualitative assessments. Results show that GANs, particularly StyleGAN, produce images with high perceptual quality and structural coherence. Diffusion-based models for inpainting and image variation, such as DALL-E 2, delivered high realism and semantic alignment but generally struggled in balancing visual fidelity with scientific accuracy. Importantly, our findings reveal limitations of standard quantitative metrics in capturing scientific relevance, underscoring the need for domain-expert validation. We conclude by discussing key challenges such as model interpretability, computational cost, and verification protocols, and discuss future directions where generative AI can drive innovation in data augmentation, simulation, and hypothesis generation in scientific research.

Generative Adversarial Networks↗
DOE OSTI2022

Quantitative measurements of density in shock-compressed silver up to 330 GPa using x-ray diffraction

This work presents a structural study of shock-compressed states in Ag to 331 GPa, accessed via laser shock compression. Structures of the resulting crystalline phases and the average atomic distribution of the liquid state are determined using in situ x-ray diffraction. We employ structure factor analysis, adapted for a non-monochromatic x-ray source, to determine the density of the liquid phase from analysis of x-ray diffraction data for the first time in shock compressed Ag. We also present a detailed analysis of the crystalline phases, which shows good agreement with the phase boundaries reported in previous experimental and theoretical works. We utilize the whole powder pattern fitting approach implemented in the MAUD package to constrain stacking faults in the face centered cubic structure. We observe the ambient fcc structure up to a stress of 146 GPa before a transition to a body-centered cubic structure and liquid phase at 179 GPa, with full melting above 212 GPa.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Consistent Controls on Trace Metal Micronutrient Speciation in Wetland Soils and Stream Sediments

Trace metal are essential for microbially-mediated biogeochemical processes occurring in anoxic wetland soils and stream bed sediments, but low availability of these elements may inhibit anaerobic element cycling and transformations. Solid-phase speciation is likely a critical control on trace metal availability but has seen limited study in anoxic systems having concentrations similar to geological background levels, where metal limitations may be most prevalent. We have investigated trace metal concentrations and solid-phase speciation in three freshwater subsurface aquatic systems: marsh wetland soils, riparian wetland soils, and the sediments of a streambed. These systems displayed low solid-phase trace metal concentrations, generally at or below geological background levels, which generally followed the trend Zn > Cu ≈ Ni > Co and showed no correlation with major element compositions. All soils and sediments were dominated by quartz but varied in clay mineralogy as well as the organic matter, total sulfur, and total iron contents. X-ray absorption near-edge structure (XANES) spectroscopy shows that sulfur speciation in both wetlands is dominated by organic sulfur. Elemental sulfur and iron sulfides together made up <25% of the sulfur in the wetland soils, but the distribution between inorganic and organic forms was reversed in the stream sediments. Ferrous and ferric iron in clay minerals were common species identified by both XANES and extended X-ray absorption fine structure (EXAFS) spectroscopies at all sites. Iron(III) oxides were substantial components in all but the marsh wetland soils. Quantitative analysis of copper, nickel, and zinc XANES spectra revealed similar metal speciation across all sites. Copper speciation was dominated by sulfides, adsorbed species, and minor amounts of copper bound to organic matter; no metallic copper was detected. Nickel speciation also varied little and was dominated by nickel in clay mineral octahedral sheets and nickel sulfide, with adsorbed species also present. Zinc speciation was slightly more varied, with the marsh wetland soils and stream bed sediments containing adsorbed species, zinc associated with clay mineral structures, and zinc bound to reduced sulfur groups on organic matter, whereas the riparian wetland soils lacked clay-associated zinc but contained zinc sulfide. Trace metals bound to reduced sulfur occurred at every site, with a greater sulfur-bound fraction for copper. The fractional abundance of sulfur-bound species showed no relationship with soil or sediment total sulfur content, which varied by two orders of magnitude. More broadly, the observations in this study suggest that trace metal speciation in freshwater wetland soils and stream sediments is consistently dominated by a small set of recurring components which are distinct for each metal. Furthermore, this may represent a general geochemical phenomenon in anoxic soils and sediments containing trace metals at background concentrations (as low as 3 µg g -1 ) that was not predicted from systems that are contaminated with or naturally-enriched in copper, nickel, or zinc.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Dangling bonds, the charge neutrality level, and band alignment in semiconductors

We present a systematic study of the electronic properties of dangling bonds (DBs) in a variety of semiconductors and examine the relationship between DBs and the charge neutrality level (CNL) in the context of band alignments of semiconductors. We use first-principles calculations based on density functional theory to assess the energetics of DBs in a set of diamond-structure group-IV and III–V or II–VI zinc-blende-structure semiconductors, considering both cation and anion-derived states. We examine the charge-state transition levels of DBs to assess whether they can serve as a CNL to align band structures, by comparing with offsets calculated from interface calculations. Our results show that this approach for evaluating the CNL yields quantitative results for band offsets and provides useful insights. We discuss the relation with alternative approaches for determination of CNLs based on branch-point energies or transition levels of interstitial hydrogen.

36 MATERIALS SCIENCE↗