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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Distributed quantum approximate optimization algorithm on a quantum-centric supercomputing architecture

Quantum approximate optimization algorithm (QAOA) has shown promise in solving combinatorial optimization problems by providing quantum speedup on near-term gate-based quantum computing systems. However, QAOA faces challenges for high-dimensional problems due to the large number of qubits required and the complexity of deep circuits, limiting its scalability for real-world applications. In this study, we present a distributed QAOA (DQAOA), which leverages distributed computing strategies to decompose a large computational workload into smaller tasks that require fewer qubits and shallower circuits than are necessary to solve the original problem. These sub-problems are processed using a combination of high-performance and quantum computing resources. The global solution is iteratively updated by aggregating sub-solutions, allowing convergence toward the optimal solution. We demonstrate that DQAOA can handle considerably large-scale optimization problems (e.g., 1000-bit problem), achieving a high solution quality and short time-to-solution, outperforming existing strategies. Furthermore, we realize DQAOA on a quantum-centric supercomputing architecture, paving the way for practical applications of gate-based quantum computers in real-world optimization tasks. To extend DQAOA’s applicability to materials science, we further develop an active learning algorithm integrated with our DQAOA (AL-DQAOA), which involves machine learning, DQAOA, and active data production in an iterative loop. We successfully optimize photonic structures using AL-DQAOA, indicating that solving real-world optimization problems using gate-based quantum computing is feasible. We expect the proposed DQAOA to be applicable to a wide range of optimization problems and AL-DQAOA to find broader applications in material design.

Kim, Seongmin [ORNL] (ORCID:0000000159063004)↗
DOE OSTI2026

Low-energy 17 O(𝑛,𝛾)⁢ 18 O reaction within the microscopic potential model and its role for the weak 𝑟 process

The neutron radiative capture reaction 17 O ⁡(𝑛,𝛾) ⁢18 O plays a pivotal role in both nuclear structure studies and astrophysical nucleosynthesis, particularly in the formation of elements during hydrostatic and explosive stellar environments. We calculated the 17 O ⁡(𝑛,𝛾) ⁢18 O cross section within the Skyrme Hartree-Fock potential model and analyzed electric dipole 𝐸⁢1 transitions to both positive- and negative-parity states below the α-decay threshold in 18 O. Our cross sections are significantly different from the data available in commonly used libraries. We further investigate the impact of the new calculated cross section on weak 𝑟-process nucleosynthesis using large-scale reaction network calculations across a wide range of electron fractions and entropies. Our results show that the 17 O ⁡(𝑛,𝛾) ⁢18 O reaction rate significantly influences the production of first 𝑟-process peak elements, such as strontium, under specific astrophysical conditions. This study highlights the importance of accurate nuclear data for light isotopes in modeling heavy-element synthesis and provides updated reaction rates for future nucleosynthesis simulations.

6 ≤ A ≤ 19↗
DOE OSTI2026

Urban Land Surface Effects on Summertime Clouds and Moist Convection in Houston Under Different Synoptic Conditions

Urban landscapes modify cloud formation and convection through complex thermodynamic and aerodynamic processes; however, their influence under different synoptic regimes remains poorly understood. This study investigates the impact of the Houston metropolitan area on summertime cloud cover and convective cell characteristics using a combination of satellite observations, radar data, and high-resolution process-based modeling. We isolate urban effects by comparing model simulations with realistic urban land cover against hypothetical scenarios where all urban areas are replaced by rural vegetation. Results reveal that Houston's land cover consistently enhances cloud fraction and convective activity relative to surrounding rural areas, altered by large-scale meteorological forcing. Under weakly forced conditions, enhanced surface heat flux primarily contributes to driving low-level convergence and vertical ascent, leading to over 8% cloud fraction increase between 2 and 6 km over the city. Under strongly forced conditions, urban influences manifest differently depending on cloud type. For non-convective clouds, the city acts as a barrier that decelerates and lifts moist southerly inflow, increasing low cloud cover over the urban core, while decreasing it downwind the city. For convective clouds, both synoptic ascent and urbanization modulate moisture redistribution and cloud structure, producing modest cloud enhancement over the city and slight suppression over the downwind area. Urbanization exerts small changes in the intensity of the convective cells; however, it significantly decreases their duration and traveling distance. This work highlights the importance of accounting for land surface heterogeneity in modeling clouds and precipitation and demonstrates that urban impacts on clouds are highly regime-dependent.

Liu, Ye [Pacific Northwest National Laboratory (PN↗
DOE OSTI2026

Anticipating decoherence in quantum systems

Large-scale quantum technologies require coherence across distant nodes, necessitating indistinguishable quantum states. However, environmental disorder, including dephasing, spectral diffusion, and spin-bath interactions, undermines coherence. Using statistical methods, we uncover correlations in decoherence channels induced by slowly varying environments. Spectral diffusion serves as a representative demonstration case that can be extended to other remote, disordered systems such as spins in nitrogen-vacancy centers and quantum-dot spin qubits, as well as flux noise in superconducting qubits. In this work, we employ replica-theory-inspired trajectory analysis to reveal predictable temporal structures in decoherence dynamics, and validate these through an anticipatory systems framework with internal prediction of unseen spectral dynamics in multiple quantum systems, showing that this framework could, if implemented, reduce spectral shift by average factors of approximately 2 to 19, depending on emitter stability, thereby enabling enhanced coherence and multi-node synchronization for scalable quantum communication, computation, imaging, and sensing.

Maan, Pranshu [Purdue University]↗
DOE OSTI2026

Edge dislocation mediated anomalous charge transfer in face centered cubic high entropy alloys

The charge transfer in alloys dictates their structural and functional properties. The presence of the line defect can influence the charge transfer characteristics in alloys. The edge dislocation-volume misfit interaction in high entropy alloys is a critical area of interest concerning the development of accurate solid solution strengthening models for these materials. The evolution of the volume misfit of atoms is either studied empirically using Vegard's law or mechanistically using \emph{ab initio} calculations, without considering the effect of edge dislocation on charge transfer explicitly. In this work, we show the influence of edge dislocation on the charge transfer in CoCrFeMnNi, CoCrNi and CoNi using large-scale \emph{ab initio} calculations. The anomalous charge transfer characteristics, in terms of deviations from electronegativity trends, are demonstrated, which are dictated by electronegativity equalisation rather than pair-atom interactions. The deviation of atomic volume in the compressive and tensile stress regions of the edge dislocation is rationalised in terms of anomalous magneto-volume fluctuations in such alloys.

Ghosh, Swarnava [ORNL] (ORCID:0000000338005264)↗
DOE OSTI2026

Multireference Methods for Chemistry and Materials Science: Automated Active Spaces, Efficient Dynamic Correlation, and Extended Systems

While multiconfigurational approaches have long been relegated to expert practitioners working on a case-by-case basis, recent developments have increasingly made these methods more routine and applicable to broader sets of systems. This article outlines the state-of-the-art in multiconfigurational approaches, with an emphasis on moving from delicate hand-selected pathways through configuration space toward more robust and efficient approaches to treating a host of challenging chemical systems accurately. First, we overview recent work in automated active-space selection, which has enabled increasingly large-scale applications of multireference methods to modeling vertical excitations and reactivity. Second, we highlight the increasingly efficient methods for recovering correlation energy beyond the active space, as headlined by extensions of pair-density functional theory and its role in accurate and efficient treatment of excited-state dynamics and its utilization to train machine-learned potentials. Finally, we highlight recent efforts to treat extended systems that until recently have lied beyond the traditional limits of active-space methods, giving center stage to product-form wave functions of the localized active space family of methods that allow for the computation of multiconfigurational band structures. These recent advancements point to a broader use of multireference approaches for high-impact chemical and materials science applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

An agentic artificially intelligent X-ray scientist

Executing experimental tasks in both normal research laboratories and large-scale scientific facilities often requires extensive human supervision and remains a key challenge on the path to fully autonomous, artificial intelligence (AI)-driven science. Here we demonstrate a large language model-driven agent that autonomously performs X-ray sample alignment on a synchrotron beamline by planning actions, executing instrumental commands, interpreting observations and iterating towards experimental goals. Based on existing large language models with structured tool-use via the model context protocol, our AI X-ray scientist was guided and tested using an in-house-built virtual experimental setup that mirrors a six-circle diffractometer at an operational synchrotron beamline. The agentic workflow developed in the virtual environment was directly deployed on a real beamline, where it correctly identified reference reflections and determined the orientation matrix, an essential first step in any type of single-crystal scattering experiment. Our AI X-ray scientist responded effectively to unexpected experimental conditions, demonstrating adaptive problem-solving and readiness for addressing practical experimental situations. Our study provides a step towards autonomous operation across diverse experimental environments at large-scale scattering facilities.

Chen, Zhantao (ORCID:0000000319543868)↗
DOE OSTI2026

Automated and High-Throughput Phase Separation Control for Supramolecular Polymer Blends Enabled by Machine Learning

Supramolecular polymer blends (SPBs) offer tunable morphologies that dictate their macroscopic properties, yet their rational design is limited by the absence of predictive structure−morphology models. Here, we introduce a data-driven highthroughput workflow that integrates modular polymer synthesis, robotic formulation, automated morphology characterization, and machine learning (ML) for accelerated SPB discovery. Using a plug-and-play synthetic strategy, 33 hydrogen-bonding endfunctional homopolymers were prepared and orthogonally combined to generate 260 SPBs in 1 day. A fully automated atomic force microscopy (AFM) pipeline enabled systematic imaging, producing 2340 morphology data sets with minimal human intervention. Domain spacings were extracted through complementary imageprocessing methods and used to train ML models. A support vector regression (SVR) model accurately predicted target phase-separation sizes (50, 100, and 150 nm), which were experimentally validated. This work demonstrates the power of coupling high-throughput experimentation with ML to accelerate morphology discovery and provides one of the first large-scale experimental data sets for supramolecular polymer systems.

ML-guided polymer design↗
DOE OSTI2026

Large-scale calculations of 𝛽-decay rates and implications for 𝑟-process nucleosynthesis

Nuclear 𝛽 decay is a key element of the astrophysical rapid neutron capture process (𝑟 process). In this work, we present state-of-the-art global 𝛽-decay calculations based on the quantified relativistic nuclear energy density functional theory and the deformed proton-neutron quasiparticle random-phase approximation. Our analysis considers contributions from allowed and first-forbidden transitions. We used two point-coupling functionals with carefully calibrated time-odd terms and isoscalar pairing strength. The new calculations display consistent results for both employed functionals, especially near the neutron drip line, suggesting slower 𝛽 decays past the 𝑁=126 neutron shell closure than in commonly used 𝛽-decay models. The new rates, along with the existing rates based on the recent nonrelativistic global calculations, are found to slow down the synthesis of heavy elements in the 𝑟 process and significantly reduce the contribution of neutron-induced fission.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Enhanced antineutrino emission from 𝛽 decay in core-collapse supernovae with self-consistent weak decay rates

Nuclear weak-interaction rates are known to exert a prominent effect in the late stages of stellar collapse. Despite their importance, most studies to date on core-collapse supernovae (CCSNe) have focused primarily on the effects of electron captures, neglecting 𝛽 decay contributions. In this work, we present the first CCSNe simulation incorporating global 𝛽 decay rates from a microscopic theory. These are enabled by a large-scale evaluation of both electron capture and 𝛽 decay rates, obtained self-consistently utilizing the relativistic energy density functional theory and finite-temperature quasiparticle random-phase approximation. Including 𝛽 decay leads to a dramatic enhancement of the pre-bounce antineutrino signal as the antineutrino emissivity increases by more than two orders of magnitude and the luminosity by a factor of 50 relative to thermal emission alone, while the average antineutrino energy increases by over 1 MeV. Furthermore, it is expected that these new rates could help us constrain the model uncertainties related to weak-interaction processes, improving the prediction of antineutrino signal during the final stages of stellar death.

Beta decay↗
DOE OSTI2026

Scalable slot-die coating of phyllosilicate membranes for selective ion separations

Abstract Two-dimensional (2D) materials have recently drawn attention as candidate materials for molecular-scale membrane separations due to their tunable nanoscale interlayer properties. Phyllosilicates, a broad class of naturally abundant 2D clay minerals, offer significant advantages over synthetic 2D materials, including low cost, stability, and environmental compatibility. While these properties make phyllosilicates attractive for industrial-scale nanofiltration applications, phyllosilicate-based membranes have, to date, only been fabricated at the lab scale via vacuum filtration. Scalable fabrication methods are essential to advance the technical maturity of phyllosilicate membranes and bring these promising materials closer to large-scale adoption. Herein, we have successfully scaled up phyllosilicate (vermiculite) membrane fabrication via slot-die coating and roll-to-roll coating of an ethylenediamine–vermiculite (EDAVM) mixture onto nylon. The coated membranes are 1–2 μm $ \unicode{x03BC} \mathrm{m} $ mu m in thickness and exhibit similar structure and performance to vacuum-filtered EDAVM membranes. The membranes also show selectivity for monovalent ions over multivalent ions in binary salt mixtures. This work represents a major step toward scaling up phyllosilicate membranes for industrial ion separation applications such as resource recovery from water.

Booth, Austin [Princeton University]↗
DOE OSTI2026

Quantifying the phase diagram and Hamiltonian of S = 1/2 kagome antiferromagnets: bridging theory and experiment

Spin-1/2 kagome antiferromagnets are leading candidates for realizing quantum spin liquid (QSL) ground states. While QSL ground states are predicted for the pure Heisenberg model, understanding the robustness of the QSL to additional interactions that may be present in real materials is a forefront question in the field. Here we employ large-scale density-matrix renormalization group simulations to investigate the effects of next-nearest neighbor exchange couplings J 2 and Dzyaloshinskii-Moriya interactions D, which are relevant to understanding the prototypical kagome materials herbertsmithite and Zn-barlowite. By utilizing clusters as large as XC12 and extrapolating the results to the thermodynamic limit, we precisely delineate the scope of the QSL phase, which remains robust across an expanded parameter range of J 2 and D. Direct comparison of the simulated static and dynamic spin structure factors with inelastic neutron scattering reveals the parameter space of the Hamiltonians for herbertsmithite and Zn-barlowite, and, importantly, provides compelling evidence that both materials exist within the QSL phase. These results establish a powerful convergence of theory and experiment in this most elusive state of matter.

Jiang, Shengtao [SLAC National Accelerator Laborat↗
DOE OSTI2026

From natural language to control signals: a conceptual framework for semantic channel finding in complex experimental infrastructure

Modern experimental platforms such as particle accelerators, fusion devices, telescopes, and industrial process control systems expose tens to hundreds of thousands of control and diagnostic channels, accumulated over decades of hardware evolution. Operators and AI systems alike depend on informal expert knowledge, inconsistent naming conventions, and scattered documentation to locate the signals required for monitoring, troubleshooting, and automated control, creating a persistent bottleneck for reliability, scalability, and emerging language-model-driven interfaces. We formalize semantic channel finding, the task of mapping natural-language intent to concrete control-system signals, as a general problem in complex experimental infrastructure, and introduce a four-paradigm conceptual framework to guide architecture selection based on facility-specific data regimes. The paradigms span (i) direct in-context lookup over small, curated channel dictionaries, (ii) constrained hierarchical navigation through structured trees, (iii) interactive agent exploration using iterative reasoning and tool-based database queries, and (iv) ontology-grounded semantic search that decouples channel meaning from facility-specific naming conventions. We demonstrate the practical feasibility of each paradigm through proof-of-concept implementations at four operational facilities spanning two orders of magnitude in scale: from compact free-electron lasers to large synchrotron light sources, operating under diverse control-system architectures ranging from clean hierarchical naming schemes to legacy environments with decades of heterogeneous conventions. Where evaluated against expert-curated operational queries, these instantiations achieve 90%–97% accuracy, validating the framework’s applicability across real-world deployment scenarios. To accelerate adoption across the broader scientific and industrial control-system community, we release open-source, plug-and-play implementations of all three interactive paradigms-direct lookup, hierarchical navigation, and middle-layer exploration-within the Osprey framework, together with tools for channel database generation, interactive testing, and minimal-configuration deployment. This work establishes semantic channel finding as a foundational capability for human-centric and agentic AI interfaces at large-scale facilities, providing both a systematic framework for architecture design and practical resources to enable adoption without building custom infrastructure from scratch.

channel finding↗
DOE OSTI2026

Accelerating Bilevel Optimization With Hierarchical Many-Threaded Parallel Differential Evolution

Bilevel optimization is encountered in many relevant real-world applications. The main feature of this type of problem is that an upper-level optimization problem is constrained by a nested lower-level optimization problem. Because of this nested structure, bilevel problems (BLPs) are usually computationally expensive to solve. Differential evolution (DE) has demonstrated promising results in solving BLPs of relatively small scales. As the problem scale increases, the decision space becomes intrinsically larger, requiring a growing number of function evaluations for the method to work properly. In this context, heavy parallelization and high-performance computing techniques are indispensable to enable the resolution of more complex and challenging optimization problems. Hence, we propose a hierarchical many-threaded parallel DE approach for BLPs, where both levels are parallelized. The computational experiments demonstrate that the parallel implementation achieved runtime speeds ranging from 44 to 2559 times faster than the sequential version on a well-known scalable SMD benchmark test problem when executed on an NVIDIA A100 GPU. The findings indicate that the algorithm’s convergence is strongly influenced by the number of both upper- and lower-level generations. Moreover, the success of experiments with large-scale problems is closely linked to the choice of small population sizes.

Dufek, Amanda S↗
DOE OSTI2026

Progressing Analysis of Variable Electric Rates (PAVER) Study

The Progressing Analysis of Variable Electric Rates (PAVER) study analyzed the impact of a range of time-varying electric rates on the performance of a regional electric grid and the resulting costs for participating and non-participating customers. This analysis leveraged and extended the work of PNNL’s Distribution System Operator with Transactive (DSO+T) study. Five different rate designs were included: a flat volumetric energy charge, a typical Time of Use (TOU) rate, a dynamic energy (DE) rate (based on wholesale locational marginal prices), a dynamic energy and capacity (DE+C) rate, and, finally, a Block and Swing (B&S) rate that billed customers based on their average load profile at constant pricing, but used the DE+C dynamic price for load deviations from their average profile. These rates were analyzed in a large-scale co-simulation of an entire regional grid with a customer population representative of the current state. A large fraction (80%) of residential and commercial customers were assumed to participate in these time-varying rates with automatically controlled HVAC, water heaters, electric vehicles, and batteries. This study assumed no industrial sector participation. The DE and DE+C rates saw system peak loads reduced by 6-7%, while the large participation in the TOU rate case saw a significant rebound effect and a resulting peak load increase of >5%. The impacts to the annual and peak system demand impacted system wholesale prices and the overall grid operating costs. This cost structure determined the revenue needed to be collected from customers by each rate design. Participating customers on the DE and DE+C rates (located in one of the modeled DSOs) saw reductions in average annual electricity bills of 11-17% with average increases in monthly bill variation of no more than 13%. At such high participation levels, TOU customers saw 10% higher average annual bills (due to system-wide rebound effects) and average increased monthly bill variation of 16%. Residential owners of large flexible loads (such as electric vehicles) saw larger bill savings (17-20%) when on a fully dynamic rate. The presence of on-site generation (such as rooftop solar) did not appear to appreciably change customer outcomes. Customers on the Block and Swing rate did see 6% lower monthly bill variation (as intended) than the flat rate case, but at the expense of appreciable bill savings, which were only 3%, comparable to the savings seen by non-participants. Given this finding we recommend that additional research be conducted into how best various bill protection mechanisms can balance minimizing customer bill variation with providing financial incentives commensurate with the flexibility customers provide. We also recommend that customer outcomes be explored across a range of regions using current actual customer and system cost data.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Final Technical Report for U.S.-Japan Hadronic Physics Exchange Program for Studies of Hadron Structure and QCD

Nuclear physics explores the fundamental properties of matter -- how protons and neutrons emerge as quantum systems of elementary particles, how they form the atomic nuclei, and how they give rise to the wide variety of phenomena and applications at biological, technical, and astronomical scales. It is a global scientific effort centered around large-scale experimental user facilities (particle accelerators and detectors), advanced theoretical methods and concepts, and computational techniques and resources. Exchange of knowledge and ideas, scientific collaboration, and workforce development on a global scale are essential for the future of the field. The nuclear physics program envisaged in the 2023 DOE/NSF NSAC Long-Range Plan and pursued at the U.S. National Labs has strong synergies with programs at other facilities worldwide and will realize significant benefits from international collaboration. Nuclear physics is also recognized for promoting international cooperation in the broadest sense through joint construction and operation of experimental equipment, personal contacts between scientists, and education and training. The U.S.-Japan Hadronic Physics Exchange Program (USJPHE) supported collaborative scientific research in hadronic physics and quantum chromodynamics. USJHPE focused on subject areas related to the programs at current and future experimental facilities in the U.S.\ and Japan and supported both experimental and theoretical studies. USJHPE particularly aimed to realize synergies between the hadronic physics programs at Jefferson Lab 12 GeV and J-PARC resulting from the complementarity of electromagnetic and hadronic probes in the multi-GeV energy range. Subject areas of common interest included the quark-gluon structure of hadrons and nuclei, meson and baryon spectroscopy, strangeness and hypernuclear physics, and other related topics. USJHPE also supported research in hadronic physics and nuclear-physics-enabled tests of fundamental symmetries related to the programs at Brookhaven National Lab, Fermilab, KEK, Spring-8, and university-based facilities in the U.S. and Japan. USJHPE especially promoted collaboration between the U.S. and Japanese nuclear physics communities in developing the physics program and instrumentation for the future Electron-Ion Collider. USJHPE was intended to provide travel grants to U.S.-based scientists (primary institutional affiliation with a U.S.\ university, national laboratory, or other research center) to visit Japanese institutions and conduct collaborative research there. The program supported senior researchers, postdoctoral fellows, and students. Continuing the setup of the preceding grant period, J-PARC served as the Japanese “hub” for U.S. physicists for short- and long-term visits, and JLab served as the corresponding U.S. “hub”. The program was officially managed through the U. of Connecticut in Storrs, CT. Support for Japanese physicists visiting the U.S. was provided through funds from Japanese funding agencies. The USJHPE program promoted the scientific exchange and the collaborative spirit in hadronic physics between the two countries.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Influence of Powder Characteristics and Processing Methods on Creep Performance of Powder Metallurgy Hot Isostatic Pressed SS316

The U.S. nuclear energy expansion goals are driving the demand for manufacturing routes that can rapidly produce large, complex, near net shape components. Powder metallurgy hot isostatic pressing (PM HIP) is an advanced manufacturing technique that can be economically scaled-up, while alleviating the supply chain challenges that forging and casting face in terms of cost and lead time constraints. This makes PM-HIP a viable technology to aid and accelerate large-scale part manufacturing for nuclear applications. However, large-scale qualification and deployment of this technology require a thorough understanding of the influence of powder feedstock quality, powder handling history, hot isostatic pressing (HIP) parameters, and subsequent heat treatment on microstructural evolution and elevated temperature mechanical performance. The present work focusses on 316 austenitic stainless steel (SS316) which is most commonly used in high temperature environments for nuclear applications Results from this study show that PM HIPed SS316 meets ASME tensile requirements at room temperature and at elevated temperature. However, creep performance of PM-HIPed SS316 remains inferior to its wrought counterpart, demonstrating that tensile performance alone is not a reliable metric for long duration high temperature integrity. Further, this report delineates powder derived microstructural features that govern creep damage, with key evidences pointing to “microstructural inheritance” from gas atomized powder feedstocks. Commercial SS316 powders of varying chemical compositions and recycling histories were studies, and the results showed large differences in elemental segregation, oxide surface layers and secondary phase distributions. Multi-scale characterization revealed segregation of chromium, molybdenum, manganese and silicon at the boundaries and the precipitation of manganese-, silicon-, and molybdenum-oxides. During HIP consolidation, these surface oxides transform into decorated prior particle boundaries (PPBs) and grain boundary inclusions that persist through conventional post-HIP solution annealing treatment. The retained oxides in post-HIP microstructures were found to influence grain growth, precipitation behavior, and ultimately creep cavitation and fracture. Such post-HIP heat treatments are therefore limited by a complex trade-off between grain growth, and oxide coarsening which aggravate creep damage by acting as nucleation sites for cavities. The objective of this work is to establish an integrated processing–structure–property framework for PM-HIP 316 stainless steel by investigating the influence of powder feedstock characteristics in pre- and post-HIP processing as well as to understand the significance of post-HIP heat treatment on microstructural evolution and creep properties. The results from this report emphasize the significance of powder feedstock integrity in improving creep performance of PM-HIPed SS316, by highlight the effect of rapid solidification, elemental segregation, oxide formation and powder recycling on microstructural defect inheritance following HIP consolidation. Rather than considering HIP processing, solution annealing, and mechanical performance independently, this report treats powder production, HIP consolidation, post-HIP thermal processing, and creep deformation as interconnected stages within a continuous metallurgical process. The resulting framework will provide a scientific basis for developing feedstock engineering strategies capable of improving long-term reliability of PM-HIP stainless steels and accelerating their qualification for advanced nuclear applications.

Ajjarapu, Pavan [Oak Ridge National Laboratory (OR↗
DOE OSTI2026

DESI DR2 Reference Mocks: Clustering results from UCHUU ELGs and QSOs

High-redshift galaxy clustering provides a powerful probe of the growth of structure, testing models of dark matter, dark energy, and galaxy formation during the epoch when the Universe was rapidly evolving. Emission line galaxies (ELGs) and quasars (QSOs) are used as tracers of dark matter by the Dark Energy Spectroscopic Instrument (DESI) to probe this redshift regime. We present results from ELG and QSO mock catalogs created from the Uchuu N-body simulation and tuned to DESI Data Release 2 (DR2) clustering. Employing a modified subhalo abundance matching (SHAM) technique, we populate Uchuu halos and subhalos with QSOs between 0.8 < z < 2.1. For ELGs, we modify this method to select satellite galaxies with low velocities relative to their associated central halos, and populate a separate set of Uchuu halos and subhalos with ELGs between 0.8 < z < 1.6. In this paper, we reproduce the redshift evolution of number density and clustering statistics across the fitted range of scales. We also measure the large-scale clustering bias of both the data and mock samples. These results improve simulated lightcone construction from cosmological models and enhance our understanding of the galaxy-halo connection.

Vaisakh, R. [Southern Methodist U.] (ORCID:0009000↗