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At least 703 records · Page 39

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DOE OSTI2025

Improved structure of calcium isotopes from ab initio calculations

The in-medium similarity renormalization group (IMSRG) is a powerful and flexible many-body method to compute the structure of nuclei starting from nuclear forces. Recent developments have extended the IMSRG from its standard truncation at the normal-ordered two-body level, the IMSRG(2), to a precision approximation including normal-ordered three-body operators, the IMSRG(3)-N 7 . This improvement provides a more precise solution to the many-body problem and makes it possible to quantify many-body uncertainties in IMSRG calculations. We explore the structure of 44,48,52 Ca using the IMSRG(3)-N 7 , focusing on understanding existing discrepancies of the IMSRG(2) to experimental results. We find a significantly better description of the first 2 + excitation energy of 48 Ca, improving the description of the shell closure at N=28. At the same time, we find that the IMSRG(3)-N 7 corrections to charge radii do not resolve the systematic underprediction of the puzzling large charge radius difference between 52 Ca and 48 Ca. We present estimates of many-body uncertainties of IMSRG(2) calculations applicable also to other systems based on the size extensivity of the method.

39 ≤ A ≤ 58↗

DOE OSTI2022

Subcomponent Validation of Composite Joints for Marine Energy Structures

The Marine Energy Advanced Materials project addresses the barriers and uncertainties facing marine renewable energy developers in using composite materials for load-bearing structures. Sponsored by the U.S. Department of Energy's Water Power Technologies Office, the multiyear project comprises of collaborators from the National Renewable Energy Laboratory (NREL), Sandia National Laboratories, Pacific Northwest National Laboratory (PNNL), Montana State University (MSU), Florida Atlantic University (FAU), and industry stakeholders. As part of the Marine Energy Advanced Materials project, marine renewable energy industry surveys and assessments were conducted to identify key materials and knowledge gaps that hinder the adoption of composite materials in marine renewable energy structures. Specific knowledge gaps highlighted for composite materials were environmental effects, fatigue strengths, and bonded and bolted interconnects (composite/composite and composite/metal). It was concluded that many of these gaps could be addressed through subcomponent validation; consequently, a program was developed at NREL with the goals of developing subcomponent validation methods for appropriate marine energy materials, which would improve the understanding of design allowables for full-scale structural components and joints. Ultimately, the aim is to reduce timelines and costs associated with full-scale structural validation efforts while also providing near-net-scale static and fatigue data of composite/metal subcomponents for marine renewable energy systems. To approach these goals, a testing program was developed at NREL to investigate a variety of materials and structural design details at the subcomponent scale to understand (a) the effects of harsh and corrosive marine energy environments and (b) the static and fatigue strengths of the complex geometries. The recent study from this testing program is perhaps the largest that has ever been conducted with respect to specimen scale and geographic diversity of underwater environmental conditions that the specimens were subjected to. A variety of specimen geometries were designed by NREL to highlight key features of multimaterial (composite/composite or metal/composite) interconnects that may be used in marine renewable energy structural designs. The designs used several different composite matrices, adhesives, and marine-grade steels, which were highlighted in the surveys as being the most appropriate for harsh marine environments. Composite panels were then manufactured at MSU, and were subsequently manufactured into test specimens by NREL. The specimens were shipped to FAU and PNNL for conditioning in ocean water tanks at various temperatures for an extended period. The specimens were then returned to NREL for structural validation. This presentation will provide an overview of recent advances within the testing program in terms of specimen design and test methods, and will discuss results and key findings of the subcomponent testing program to date at NREL.

adhesives↗

DOE OSTI2022

3D printing of additive structures for nuclear fuels

A method for manufacturing a nuclear fuel compact is provided. The method includes forming an additive structure, consolidating a fuel matrix around the additive structure, and thermally processing the fuel matrix to form a fuel compact in which the additive structure is encapsulated therein. The additive structure optionally includes a vertical segment and a plurality of arm segments that extend generally radially from the vertical segment for conducting heat outwardly toward an exterior of the fuel compact. In addition to improving heat transfer, the additive structure may function as burnable absorbers, and may provide fission product trapping.

Terrani, Kurt A.↗

DOE OSTI2023

3D printing of additive structures for nuclear fuels

Terrani, Kurt A.↗

DOE OSTI2020

The crystal structure of TlMgCl 3 from 290 K to 725 K

The title compound, thallium magnesium trichloride, has been identified as a scintillator with both moderate gamma-stopping power and moderate light yield. Knowledge of its crystal structure is needed for further development. This work determines the crystal structure of TlMgCl 3 to be hexagonal P6 3 /mmc (No. 194) and isostructural with RbMgCl 3 , contrary to previously reported data. This structure was obtained by single-crystal X-ray diffraction and was further confirmed by neutron diffraction measurements. Extending neutron diffraction measurements to high temperature, the data show that TlMgCl 3 maintains this crystal structure from 290 K up through 725 K, approaching the melting point of 770 K. Anisotropic thermal expansion coefficients increase over this temperature range, from 31 to 38 × 10 –6 K –1 along the a axis and from 19 to 34 × 10 –6 K –1 along the c axis.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Cross slip of extended dislocations in face-centered cubic metals through phase-field modeling

Cross slip is a dislocation mechanism that significantly impacts the mechanical behavior of engineering alloys. Here, in this work, we advance a 3D phase-field dislocation dynamics (PFDD) mesoscale technique to simulate cross slip across a broad range of face-centered cubic (FCC) metals. The formulation incorporates elastic anisotropy, an FCC numerical grid, and a high-fidelity representation of the entire γ -surface from density functional theory for eight FCC metals and no adjustable parameters or rules. The relaxed core structures under zero stress for all metals are predicted to extend in plane. The analytical model for stacking fault width agrees well with the PFDD result under the assumption of elastic isotropy but overestimates it under elastic anisotropy, when the degree of anisotropy is large. The dynamic simulations are designed to elucidate the material parameters that influence the propensity for cross slip. Whether cross slip occurs under a non-Schmid stress or to bypass a hard obstacle, the critical stress to cross slip scales strongly with the anisotropic energy coefficient for a screw dislocation.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Exploring baryon resonances with transition generalized parton distributions: status and perspectives

QCD gives rise to a rich spectrum of excited baryon states. Understanding their internal structure is important for many areas of nuclear physics, such as nuclear forces, dense matter, and neutrino-nucleus interactions. Generalized parton distributions (GPDs) are an established tool for characterizing the QCD structure of the ground-state nucleon. They are used to create 3D tomographic images of the quark/gluon structure and quantify the mechanical properties such as the distribution of mass, angular momentum, and forces in the system. Transition GPDs extend these concepts to N → N* transitions and can be used to characterize the 3D structure and mechanical properties of baryon resonances. They can be probed in high-momentum-transfer exclusive electroproduction processes with resonance transitions e + N → e' + M + N*, such as deeply-virtual Compton scattering (M = γ) or meson production (M = π, K, etc.), and in related photon/hadron-induced processes. This White Paper describes a research program aiming to explore baryon resonance structure with transition GPDs. This includes the properties and interpretation of the transition GPDs, theoretical methods for structures and processes, first experimental results from JLab 12 GeV, future measurements with existing and planned facilities (JLab detector and energy upgrades, COMPASS/AMBER, EIC, EicC, J-PARC, LHC ultraperipheral collisions), and the theoretical and experimental developments needed to realize this program.

Experimental Nuclear Physics↗

DOE OSTI2023

Preference for a pressure-induced 3D structure after 1T-HfSe 2

Extensive crystal structure prediction searches provide evidence of two 3D structures with orthorhombic (Immm) and monoclinic (C2/m) space groups as reasonable candidates for the first pressure-induced phase of 1T-HfSe 2 . Our candidates are compared with two recent proposals that keep the 2D nature of the ambient conditions phase and display hexagonal (P6 3 /mmc) and monoclinic (C2/m) symmetry, although the latter has a different structure than our proposed C2/m phase. Both of these 2D-like structures are discarded based on simple thermodynamic and kinetic arguments that can be extended to explain the pressure-induced polymorphic sequence of other transition metal dichalcogenides. The computed observables of our orthorhombic phase are fully consistent with the experimental structural and Raman data observed at low and high-pressure.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Thorium model and weak 5f delocalization

Recently, it was demonstrated that an empirical model based on bremsstrahlung isochromat spectroscopy of elemental thorium (Th) could be used to explain the experimental results and unoccupied 5f electronic structure for simple localized actinide systems with n = 2/3, 2, 3, and 5, where n is the 5f occupancy. In this work, the thorium model will be extended to provide an understanding of the observed behavior and unoccupied 5f electronic structure in the uranium monochalcogenide systems of uranium sulfide and uranium telluride, in terms of weak 5f delocalization.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samples

Comprehensive and unambiguous identification of small molecules in complex samples will revolutionize our understanding of the role of metabolites in biological systems. Existing and emerging technologies have enabled measurement of chemical properties of molecules in complex mixtures and, in concert, are sensitive enough to resolve even stereoisomers. Despite these experimental advances, small molecule identification is inhibited by (i) chemical reference libraries (e.g. mass spectra, collision cross section, and other measurable property libraries) representing <1% of known molecules, limiting the number of possible identifications, and (ii) the lack of a method to generate candidate matches directly from experimental features (i.e. without a library). To this end, we developed a variational autoencoder (VAE) to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. We extended the VAE to include a chemical property decoder, trained as a multitask network, in order to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, with its focus on properties that can be obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involved a cascade of transfer learning iterations. First, molecular representation is learned from a large dataset of structures with m/z labels. Next, in silico property values are used to continue training, as experimental property data is limited. Finally, the network is further refined by being trained with the experimental data. This allows the network to learn as much as possible at each stage, enabling success with progressively smaller datasets without overfitting. Once trained, the network can be used to predict chemical properties directly from structure, as well as generate candidate structures with desired chemical properties. Our approach is orders of magnitude faster than first-principles simulation for CCS property prediction. Additionally, the ability to generate novel molecules along manifolds, defined by chemical property analogues, positions DarkChem as highly useful in a number of application areas, including metabolomics and small molecule identification, drug discovery and design, chemical forensics, and beyond.

Colby, Sean M.↗

DOE OSTI2023

Extended Oxygen Octahedral Tilt Proximity near Oxide Heterostructures

The oxide interfaces between materials with different structural symmetries have been actively studied due to their novel physical properties. However, the investigation of intriguing interfacial phenomena caused by the oxygen octahedral tilt (OOT) proximity effect has not been fully exploited, as there is still no clear understanding of what determines the proximity length and what the underlying control mechanism is. Here, we achieved scalability of the OOT proximity effect in SrRuO 3 (SRO) by epitaxial strain near the SRO/SrTiO 3 heterointerface. We demonstrated that the OOT proximity length scale of SRO is extended from 4 unit cells to 14 unit cells by employing advanced scanning transmission electron microscopy. We also suggest that this variation may originate from changes in phonon dispersions due to electron-phonon coupling in SRO. Here, this study will provide in-depth insights into the structural gradients of correlated systems and facilitate potential device applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

STOCHASTIC OPTIMIZATION FOR LONG TERM CAPITAL STRUCTURES, SYSTEMS, AND COMPONENTS REFURBISHMENT AND REPLACEMENT

As commercial nuclear power plants (NPPs) pursue extended plant operations in the form of Second License Renewals (SLRs), opportunities exist for these plants to provide capital investments to ensure long-term, safe, and economic performance. Several utilities have already announced their intention to pursue extended operations for one or more of their NPPs via SLR2. The goal of this research is to develop a riskinformed approach to evaluate and prioritize plant capital investments made in preparation for, and during the period of, extended plant operations to support decisions in NPP operations. In order to prioritize project selection via a riskinformed approach we developed a single decision-making tool that integrates safety/reliability, cost, and stochastic optimization models to provide users with data analysis capabilities to more cost effectively manage plant assets. Both stochastic analysis methods—such as Monte Carlo-based sampling strategies—and multi-stage stochastic optimization strategies are employed to provide priority lists to decisionmakers in support of risk-informed decisions. We applied the proposed method to a trial application of projected replacement/refurbishment expenditures for plant capital assets (i.e., Structures, Systems, and Components [SSCs]). The objective is to optimize the SSC replacement/refurbishment schedule in terms of economic constraints, data uncertainties, and SSC reliability data, as well to generate a priority list for maximizing returns on investment.

42 ENGINEERING↗

DOE OSTI2026

Geometry, spin coupling, and dielectric control of redox potentials in [4Fe–4S] Clusters

Iron–sulfur (Fe–S) clusters are common biological cofactors that facilitate vital redox reactions. Despite extensive research, the molecular basis of redox potential tuning in ferredoxin-like proteins remains an active area of debate. In this study, we combine statistical analysis of over one thousand [4Fe–4S]-containing protein structures from the Protein Data Bank (PDB) with broken-symmetry and extended broken-symmetry density functional theory to examine how cysteine ligand orientations and environmental screening affect redox properties of the clusters. We identified five main ligand configurations, three of which are predominant in natural structures. Among these, the adiabatic electron affinity differs by less than 0.1 V, indicating that, while geometry plays a secondary role, it allows localized fine-tuning of redox properties. In contrast, electrostatic and solvation effects primarily determine the overall potential range.

Computational Chemistry↗

DOE OSTI2025

Structure and dynamics of open-shell nuclei from spherical coupled-cluster theory

We extend the spherical coupled-cluster ab initio method for open-shell nuclei where two nucleons are removed from a shell subclosure. Following the recent implementation of the two-particle-attached approach [Phys. Rev. C 110, 044306 (2024)], we focus on the two-particle-removed method. Using the equations-of-motion framework, we address both nuclear structure and dipole response functions by coupling coupled-cluster theory with the Lorentz integral transform technique. We perform calculations using chiral interactions, including three-nucleon forces, and estimate many-body uncertainties by comparing different coupled-cluster truncation schemes. Here, we validate our approach by studying ground-state energies, excited states, and electric dipole polarizabilities in the oxygen and calcium isotopic chains. For binding energies and selected low-lying excited states, we achieve an accuracy comparable to that of the established closed-shell coupled-cluster theory and generally agree with experiment. Finally, we underestimate experimental data for electric dipole polarizabilities, particularly in calcium isotopes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

A Novel Non‐Destructive Rapid Tool for Estimating Amino Acid Composition and Secondary Structures of Proteins in Solution

Abstract Amino‐acid protein composition plays an important role in biology, medicine, and nutrition. Here, a groundbreaking protein analysis technique that quickly estimates amino acid composition and secondary structure across various protein sizes, while maintaining their natural states is introduced and validated. This method combines multivariate statistics and the thermostable Raman interaction profiling (TRIP) technique, eliminating the need for complex preparations. In order to validate the approach, the Raman spectra are constructed of seven proteins of varying sizes by utilizing their amino acid frequencies and the Raman spectra of individual amino acids. These constructed spectra exhibit a close resemblance to the actual measured Raman spectra. Specific vibrational modes tied to free amino and carboxyl termini of the amino acids disappear as signals linked to secondary structures emerged under TRIP conditions. Furthermore, the technique is used inversely to successfully estimate amino acid compositions and secondary structures of unknown proteins across a range of sizes, achieving impressive accuracy ranging between 1.47% and 5.77% of root mean square errors (RMSE). These results extend the uses for TRIP beyond interaction profiling, to probe amino acid composition and structure.

Chemistry↗

DOE OSTI2021

Machine learning-enabled prediction of chemical durability of A 2 B 2 O 7 pyrochlore and fluorite

Pyrochlore-structure type and its derivative in a general formula A 2 B 2 O 7 (A = rare earth elements and actinides; B = Ti, Sn, Zr, Hf, Pb, Si, etc.) display excellent structural flexibility and rich crystal chemistry as promising nuclear waste form materials capable of immobilizing actinides and fission products. It is essential to understand these materials’ chemical durability and element release of radionuclides in order to evaluate their performance in near-field environment. However, it is a formidable grand technological challenge to experimentally perform durability testing across hundreds of thousands of possibilities resulting from their extreme compositional complexities due to cation substitutions at both A and B-sites. In this work, we demonstrate a machine learning approach to determine the key materials parameters and structural characteristics governing the leaching behaviors from a small set of selected compositions as model systems, enabling a science-based prediction of their chemical durability that can be extended to a wide range of chemical compositions. The combination of four key structural characteristics and materials parameters, including ionic radius size difference , ionic potential difference , electronegativity difference , and lattice parameter , creates features an optimized prediction of the chemical durability. Two machine learning models, linear regression and Kernel ridge regression models, are trained on the randomly-split training dataset derived from the experimentally-determined elemental release rates, and subsequently tested on the testing dataset. The predicted leaching rates from both machine learning models show an excellent agreement with the experimental data, demonstrating the feasibility of rapidly evaluating the material properties of new compositions. These results highlight the immense potential of synergizing informatics through machine learning-based models and well-controlled experiments of selected model systems to accelerate materials design and discovery with optimized compositions and performance of promising materials for effective nuclear waste management.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Integrated structural model of the palladin–actin complex using XL ‐ MS , docking, NMR , and SAXS

Abstract Palladin is an actin‐binding protein that accelerates actin polymerization and is linked to the metastasis of several types of cancer. Previously, three lysine residues in an immunoglobulin‐like domain of palladin have been identified as essential for actin binding. However, it is still unknown where palladin binds to F‐actin. Evidence that palladin binds to the sides of actin filaments to facilitate branching is supported by our previous study showing that palladin was able to compensate for Arp2/3 in the formation of Listeria actin comet tails. Here, we used chemical crosslinking to covalently link palladin and F‐actin residues based on spatial proximity. Samples were then enzymatically digested, separated by liquid chromatography, and analyzed by tandem mass spectrometry. Peptides containing the crosslinks and specific residues involved were then identified for input to the HADDOCK docking server to model the most likely binding conformation. Small‐angle x‐ray scattering was used to provide further insight into palladin flexibility and the binding interface, and NMR spectra identified potential interactions between palladin's Ig domains. Our final structural model of the F‐actin:palladin complex revealed how palladin interacts with and stabilizes F‐actin at the interface between two actin monomers. Three actin residues that were identified in this study also appear commonly in the actin‐binding interface with other proteins such as myotilin, myosin, and tropomodulin. An accurate structural representation of the complex between palladin and actin extends our understanding of palladin's role in promoting cancer metastasis through the regulation of actin dynamics.

Sargent, Rachel [Department of Chemistry and Bioch↗

DOE OSTI2022

Data Mining and Visualization of High-Dimensional ICME Data for Additive Manufacturing

Integrated computational materials engineering (ICME) methods combining CALPHAD with process-based simulations can produce rich, high-dimensional data for alloy and process design. In ICME methods for metallurgical applications, the visualization and interpretation of such high-dimensional data has previously been through heat maps represented in 2 or 3 dimensions. While such an approach is ideal when one variable is varied at a time, in the case of high-dimensional data with multiple variables varied simultaneously, as is the case in additive manufacturing, interpreting the trends through two- or three-dimensional heat maps becomes challenging. Here, we propose a strategy of mixed visual data mining and quantitative analysis for high-dimensional metallurgical and process data using high-throughput thermodynamic calculations. Two case studies show the application of the proposed approach. The first case study investigated the effects of feedstock chemistry on the δ ferrite formation in 316L stainless steel powders used for binder jet additive manufacturing. The second case study linked Scheil–Gulliver calculations to a process model for dissimilar joining of aluminum alloys 5356 and 6111 during laser hot-wire additive manufacturing. Both cases contained thousands of calculated data points, showcasing the utility of visual data analysis through parallel coordinate plotting, Pearson correlation coefficient matrices, and scatter matrices compared to traditional process maps. These visualization techniques can be extended to many additive manufacturing problems to capture process–structure–property relationships for additively manufactured components.

36 MATERIALS SCIENCE↗