Cross-Quality Few-Shot Transfer for Alloy Yield Strength Prediction: A New Material Science Benchmark and An Integrated Optimization Framework
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Estimating List-Mode Data Sensitivities to Nuclear Data with MCNP6
Nuclear data are a vital component of predictive simulations used in applications like experiment design, stockpile stewardship, nuclear nonproliferation/safeguards, health physics, and criticality safety. A singular simulation requires the coalescence of different areas of nuclear data such as cross sections, angular distributions, and energy distributions of emitted neutrons for different materials and energy ranges. Improving nuclear data and thus reducing the uncertainty in simulated parameters could enable smaller, better-informed safety factors and ultimately reduce operational and procedural costs. There is a constant effort to garner a better understanding of the physical quantities represented by nuclear data through experiments. Integral experiment benchmarks use simulated and measured results to validate current nuclear data values. In the past, benchmarks primarily focused on the effective multiplication factor (k eff ); however, this limited scope has caused compensating errors and areas of nuclear data that lack validation. Compensating errors are inaccuracies in nuclear data that are obfuscated by cancellation when observing integrated values such as k eff . Diverse integral benchmark experiments that look for quantities of interest other than k eff and include multiple responses minimize the possibility of compensating errors and provides validation to areas of nuclear data previously lacking experimental validation. Benchmark experiments can be optimized during the design process to be highly dependent on specific areas of nuclear data. The dependence of a response in an experiment to a specific area/type of nuclear data is defined as sensitivity. A larger sensitivity means that nuclear data uncertainties will play a larger role in the response(s) resulting in larger bias. Currently, the sensitivity capabilities of the Monte Carlo N-Particle (MCNP ®1 ) transport code are limited to responses of k eff and tallied values (e.g., flux, surface current). As a part of the EUCLID project, this work explores estimating list-mode nuclear data sensitivities that can be used to design experiments aimed to constrain and reduce compensating errors in nuclear data by focusing on responses other than k eff . Tallied values are ideal quantities that are estimated with detectors during experiments. List-mode data (a list of neutron collection times) are the direct output of detector systems in subcritical neutron noise experiments. Expanding MCNP sensitivity capabilities to include the sensitivity of responses estimated from list-mode data, such as the prompt neutron decay constant (α) and multiplicity estimates (S and D), enables more direct comparison of simulated and measured experimental quantities. Additionally, deterministic tools such as SENSMG are capable of obtaining sensitivities to a wide variety of responses; however, these tools cannot handle complex geometries due to the assumptions made in discretizing the phase-space variables of the Boltzman transport equation.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Nuclear Data Testing Of Pu{sup 239} CIELO Evaluation With NDaST
Within the Collaborative International Evaluated Library Organisation (CIELO) project a coordinated effort is being made amongst the different nuclear data evaluation projects, ENDF, JEFF, JENDL etc. to improve the cross sections of six of the most important isotopes, Pu{sup 239}, U{sup 235}, U{sup 238}, Fe{sup 56}, O{sup 16} and H{sup 1}. Recently developed, the Nuclear Data and Sensitivity Tool (NDaST) is an online tool (http://www.oecd-nea.org/ndast/) that allows nuclear data evaluators to get rapid feedback about the consequences of changes to nuclear data on integral experiment benchmarks that exist within the International Criticality Safety Benchmark Evaluation Project (ICSBEP) and International Reactor Physics Evaluation (IRPhE) Project handbooks. NDaST links the thousands of nuclear data sensitivity profiles in the Database for ICSBEP (DICE) and the IRPhE Database and Analysis Tool (IDAT), with the nuclear data viewing tool JANIS. NDaST can be used to provide timely feedback to CIELO participants. Normally an evaluator changes the ENDF/B files based on differential data, and subsequently hands the new evaluation to testers who give feedback on the performance of the new evaluation in terms of accurately predicting the integral benchmark results. This process is often slow. With accurate sensitivity coefficients it is feasible to predict the results of changes to differential data without performing the direct substitution, by simply multiplying the change in cross section with the sensitivity. Furthermore, when multiple aspects of the file are changed simultaneously, the impact of each individual change can be muddled in the substitution testing. NDaST when used in conjunction with direct perturbation testing can help provide feedback as to which cross section changes are driving changes in the benchmarks. In this paper we focus on Pu{sup 239}, and comparing the NDaST predictions of CIELO files to direct substitution. (authors)
07 ISOTOPE AND RADIATION SOURCES↗
Integral Nuclear Data and Benchmarking Needs for Fusion Energy Systems
Fusion energy systems are currently being designed and optimized using radiation transport codes. To deal with the unique environment inside a fusion-based system, many of these designs incorporate novel materials able to withstand the high radiation fields, ensure adequate cooling and thermal protection, and produce tritium. Validation plays a vital role in building trust in the predictive power of these models and computational methods. Validation of a code consists of modeling documented real-world experiments and comparing the code-predicted response to the measured response. Adequate validation requires measured responses from real-world experiments, also known as integral data, that mimic the system being designed, including materials, impinging radiation, and temperature, among other variables. The most trusted integral data are experimental responses that have been through a rigorous benchmarking process that develops a recommended computational model and evaluates all experimental uncertainties. Finally, there are a few research groups around the world that have been producing integral data for fusion applications, but a substantial investment is needed to address the unique validation needs of the fusion community.
Fusion↗
Uncertainty Quantification of a Light Water Pulsed-Neutron Die-Away Experiment to Thermal Neutron Scattering Laws
Thermal neutron scattering laws are important nuclear data for many nuclear science and engineering applications. Validation helps to ensure that a thermal neutron scattering law has a high quality and often employs critical benchmarks as integral experiments. Recently, pulsed-neutron die-away benchmarks have been used as an experiment to validate thermal neutron scattering laws. Herein, we evidence how this alternative integral experiment has a high sensitivity to these nuclear data by performing an uncertainty quantification analysis. The analysis randomly sampled the nuclear model parameters associated with hydrogen bound in light water thermal neutron scattering law and sampled other nuclear data that influenced the experiment’s integral parameter (e.g., elastic scattering, absorption in hydrogen and oxygen) from their respective covariance matrices. The thermal neutron scattering law caused an uncertainty in the integral parameter that reached 2.67%, which exceeds by an order of magnitude the uncertainties induced in commonly used thermal solution critical benchmarks. The validation performed here, although limited due to a poor description of the historical experiment, indicated that the ENDF/B-VIII.0 thermal neutron scattering law well predicted the integral parameter. These results motivate further benchmark and validation efforts using pulsed-neutron die-away experiments.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
New Cross Section Measurements and Evaluations of Zirconium Isotopes [Slides]
This presentation gives a look to new cross section measurements and evaluations of Zirconium (Zr) isotopes. This lecture includes highlights of accomplishments made in 2022. Additionally, it explains the motivation for investigation, new measurements, and re-evaluation of the Zr isotopes. This presentation concludes with a preliminary evaluation result from RPI 90 Zr evaluation and preliminary results from Eigenvalue sensitivity study on Zr sensitive critical benchmark experiments.
07 ISOTOPE AND RADIATION SOURCES↗
UMo Benchmark Experiment Data Needed to Support Computational and Nuclear Data Validation
Currently there are almost no benchmark experiment data available directly supporting neutronics modeling and simulation of ~20 wt.% 235U, UMo fuel contained within the Organisation for Co-operation and Development Nuclear Energy Agency (OECD NEA) international benchmark handbooks from the International Criticality Safety Benchmark Evaluation Project (ICSBEP) and International Reactor Physics Experiment Evaluation Project (IRPhEP). The benchmarks within these two handbooks represent the international standards used to support computational and nuclear data validation. Recent activities in nuclear data development supporting new neutronics thermal scattering cross sections for UMo fuel indicate the necessity to provide quality benchmark experiment data that can be utilized to support their validation and development. Preparation of high-integrity neutronics benchmarks from UMo experimental data will ensure proper integral testing of these cross sections, as well as serve to support computational validation of contemporary modeling and simulation software.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Invited: Bambu: an Open-Source Research Framework for the High-Level Synthesis of Complex Applications
This paper presents the open-source High-Level Synthesis research framework Bambu. The framework provides an open-source starting point to experiment with new ideas across High-Level Synthesis, high-level verification and debugging, FPGA/ASIC design, design flow space exploration, and parallel hardware accelerator design. The tool accepts as input standard C/C++ specifications and compiler intermediate representations (IRs) coming from the well-known Clang/LLVM and GCC com- pilers. The broad spectrum and flexibility of input formats allow the electronic design automation (EDA) research community to explore and integrate new transformations and optimizations. The easily extendable modular framework already includes many op- timizations and HLS benchmarks. The integration with synthesis and verification backends (commercial and open-source) allows researchers to quickly test any new finding and easily obtain performance and resource usage metrics for a given application. Different FPGA devices are supported from several different vendors: AMD/XILINX, Intel/Altera, Lattice Semiconductor, and NanoXplore. Finally, integration with the OpenRoad open-source end-to-end silicon compiler perfectly fits with the recent push towards open-source EDA.
Ferrandi, Fabrizio↗
Resonance Parameter Adjustment Based on Integral Experiments
Our project seeks to allow coupling of differential and integral data evaluation in a continuous-energy framework and to use the generalized linear least-squares (GLLS) methodology in the TSURFER module of the SCALE code package to update the parameters of a resolved resonance region evaluation. We recognize that the GLLS methodology in TSURFER is identical to the mathematical description of a Bayesian update in SAMMY, the SAMINT code was created to use the mathematical machinery of SAMMY to update resolved resonance parameters based on integral data. Traditionally, SAMMY used differential experimental data to adjust nuclear data parameters. Integral experimental data, such as in the International Criticality Safety Benchmark Experiments Project, remain a tool for validation of completed nuclear data evaluations. SAMINT extracts information from integral benchmarks to aid the nuclear data evaluation process. Later, integral data can be used to resolve any remaining ambiguity between differential data sets, highlight troublesome energy regions, determine key nuclear data parameters for integral benchmark calculations, and improve the nuclear data covariance matrix evaluation. Moreover, SAMINT is not intended to bias nuclear data toward specific integral experiments but should be used to supplement the evaluation of differential experimental data. Using GLLS ensures proper weight is given to the differential data.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Improving the Performance of NEML2 with Modern Graph Compilation Backends
NEML2 vectorizes constitutive-model evaluation for large-scale multiphysics simulation, using PyTorch as its tensor backend so that a batch of material-point updates runs on CPU or GPU through a single implementation. In the two prior reports in this series it was a C++-native library, deployed through TorchScript tracing and just-in-time (JIT) compilation; it has since been rewritten from the ground up into a Python-native library deployed through Ahead-of-Time Inductor (AOTInductor), a modern PyTorch graph-compilation backend. The rewrite is driven by a persistent tension, not a language preference: NEML2 composes constitutive models at runtime from a registry of small, independently-authored pieces, and that flexibility is difficult to reconcile with the compile-time knowledge an efficient GPU kernel needs. This report documents the rewrite and the investment that accompanied it: the AOTInductor export pipeline that turns a Python-authored model into a portable, Python-free compiled artifact loadable from pure C++; the eager and compiled runtimes and the new implicit solver layer built on them; a head-to-head benchmark of legacy JIT against AOTInductor; the physics-model catalog and its worked examples; the developer tooling; and the corresponding overhaul of MOOSE’s NEML2 integration that lets MOOSE consume it. A central objective is to examine whether modern PyTorch graph-compilation backends are effective for MOOSE GPU integration. The benchmark answers directly: AOTInductor outperforms legacy JIT on every GPU scenario measured, by 1.0–4.5×. Modern graph-compilation backends are effective for MOOSE GPU integration, and AOTInductor specifically – not compilation in the abstract – is why.
Hu, Gary (Tianchen) [Argonne National Laboratory (↗
Optical nanofiber testbeds for benchmarking membrane-waveguide photonic integrated circuit platforms toward on-chip quantum inertial sensing
Recent advances in cold atom interferometry with optical and magnetic atom guides have set the stage for quantum inertial sensors capable of operating in dynamic environments. In this work, we present three key innovations—evanescent-field (EF) atom guides, optical nanofiber testbeds, and membrane-waveguide photonic integrated circuit (PIC) platforms—to advance EF-guided atom interferometry. First, we demonstrate EF atom guides on optical nanofiber testbeds, which serve as performance benchmarks for our membrane-waveguide PIC platforms. Second, we achieve low-power ( ~ 5 mW) guiding of freely moving, laser-cooled 133 Cs atoms in two-color, traveling-wave EF optical dipole traps at the novel, heat-efficient magic wavelengths of 793 and 937 nm (i.e., “793/937-nm EF atom guides”). Concurrently, we design and fabricate membrane-waveguide PIC platforms for these EF atom guides; in our prior work, we showed that these structures safely accommodate 4–6 times the required optical trap power under vacuum and enable dense cold atom generation via magneto-optical trapping in the vicinity of the optical wavguide for efficient loading. Third, we verify preserved atomic coherence via microwave fields and EF-coupled Doppler-free Raman beams; to our knowledge, this is the first report of coherence fringes driven by co-propagating EF-coupled Raman beams with only 150 nW of total optical power. By providing a direct comparison between optical nanofiber testbeds and membrane-waveguide PIC platforms, our results lay critical groundwork for the on-chip realization of EF-guided atom interferometry and the development of fully integrated, compact, lightweight, and low-power quantum accelerometers and gyroscopes.
Orozco, Adrian [Sandia National Laboratories (SNL-↗
Development of a Research Reactor Protocol for Neutron Multiplication Measurements
Establishing a protocol for measurements on research reactors is the next step in advanced subcritical neutron multiplication inference measurements. Such measurements can help identify deficiencies and quantify uncertainties in nuclear data and validate predictive Monte Carlo radiation transport simulation capabilities related to subcritical neutron multiplication inference techniques. This work expands on previous benchmark-quality efforts performed at the National Criticality Experiments Research Center (NCERC) located in Nevada, including subcritical experiments with a 4.5-kg {alpha}-phase plutonium sphere surrounded by copper, tungsten and nickel. Evaluations of the nickel and tungsten measurements have both been accepted into the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook. The nickel benchmark was the first ICSBEP-accepted evaluation of measurements using the Feynman Variance-to-Mean method based on the Hage-Cifarelli formulism, and was the culmination of many years of subcritical experiment research. To help establish a protocol for research reactor-based subcritical measurements, a series of Critical and Subcritical {Phi}-Power Experiments at Rensselaer (CaSPER) have been designed. This neutron multiplication measurement series will be executed at the Rensselaer Polytechnic Institute Walthousen Reactor Critical Facility (RPI-RCF) in upstate New York. The RPI reactor facility can provide benchmark-quality integral experimental configurations where different reactivity states can be achieved by varying the control rod height/water height in the reactor. The diversity of achievable configurations are also unique in contrast to previous subcritical benchmark measurements in that they are spatially complex, involve different materials (fuel, moderator), and are characterized by system-specific neutron cross section sensitivities (various energy ranges and neutron reaction contributions). It is worth noting that previous proof-of-concept subcritical measurements have been performed on thermal systems at NCERC, at a variety of reactivity states by separation distance, yet it was not possible to know the reactivity of various configurations accurately for benchmarking (there are no control rod worth curves as there are no control rods). (authors)
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
ORIGEN Integration into MPACT
This report documents the work performed to meet the objectives of the RTM milestone on ORIGEN Integration into MPACT. The main objective of the milestone was to demonstrate the capability to perform depletion problems with MPACT where the point depletion calculation, which solves the nuclide transmutation equation with ORIGEN. While MPACT already has a depletion capability based on ORIGEN, it is desired to transition to using ORIGEN because ORIGEN has undergone far more validation and software quality assurance than the implementation in MPACT. The integration was completed by compiling and linking the two codes together at build-time. Within the TriBITS build structure used for VERA, the MPACT and SCALE code repositories are already present. An optional dependency was created between MPACT to SCALE. The API available within ORIGEN was modified slightly to be able to be used by MPACT and two new software classes were implemented in MPACT to call the necessary ORIGEN subroutines. To test the ORIGEN integration two benchmark problems were analyzed. The first being the spent fuel isotopics from the Takahama-3 PWR reactor. This involved comparisons to experimentally measured nuclide concentrations for select actinides and fission products, and comparisons to other numerical results. The benchmark defines a 1-D problem limiting most of the effects due to the accuracy of a multi-dimensional transport solver, and thus differences can be assumed to be more likely caused by the point depletion solver methodology and associated nuclear data. The other benchmark analyzed two problems from the reactor physics benchmarks for LWR next generation fuel. The two configurations simulated were a PWR UO 2 pin cell and PWR UO 2 assembly. For all the benchmark problems the results were generally in good agreement with the other numerical results which were computed by several codes, including MPACT with its original depletion implementation. Thus it is concluded that ORIGEN was successfully integrated into MPACT and is capable of providing reasonable results. An additional cursory analysis of the performance of the ORIGEN point depletion compared to native MPACT point depletion solver was also performed. In this analysis it was observed that ORIGEN, on average, needs about 0.28 to 0.30 seconds to perform the point depletion calculation. This is greater than the target performance of 0.1 seconds which means future work to investigate smaller depletion chains will be needed. The MPACT internal depletion solver completes the point depletion calculation in 0.02 seconds, so it may be used for routine analysis until the use of a simplified burnup chain and performance improvements are implemented. Other future work should focus on implementing the post-correction fix-up and quadratic depletion and computation of decay heat and gamma sources.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
The Risk Assessment Information System Compendium of Ecological Screening Benchmarks for Chemicals and Radionuclides (2025) (Volume I – Text, Radionuclides for all Media, Chemicals for Air and Biota)
The Risk Assessment Information System Ecological Benchmark Tool compiles screening level benchmark values for assessing potential ecological risks posed by chemical and radionuclide contaminants. Drawing from a wide array of federal, state, and international sources, it consolidates benchmarks across environmental media, including soil, water, sediment, air, and biota, and presents values for a broad range of receptors and exposure pathways. The tool includes both effect-based and no-effect thresholds, with media- and organism-specific guidance derived from peer-reviewed literature and regulatory frameworks. By integrating diverse benchmark types into a unified reference, the tool supports consistent, scientifically grounded evaluations in ecological risk assessments and environmental management practices.
54 ENVIRONMENTAL SCIENCES↗
ENDF/B-VIII.0: the 8. major release of the ENDF/B library containing CIELO-project evaluations, new standards and thermal scattering data - 25580
The ENDF/B-VIII.0 evaluated nuclear reaction data library was released on February 2, 2018. ENDF/B-VIII.0 incorporates many improvements including the new IAEA standards, a nearly completely new thermal neutron scattering sublibrary and the CIELO project evaluations for neutron reactions on {sup 16}O, {sup 56}Fe, {sup 235}U, {sup 238}U and {sup 239}Pu. These evaluations are a direct result of improvements in theory and simulation and benefit from recent experimental data obtained in the U.S. and Europe. Notable other advances include updated evaluated data for light nuclei, structural materials, actinides and charged-particle reactions, and specific updates to fission energy release, prompt fission neutron and γ-ray spectra. ENDF/B-VIII.0 has undergone substantial testing including a wide range of criticality, reaction rate, and shielding benchmarks. Our integral validation demonstrates that performance of the library is a noticeable improvement over the previous ENDF/B-VII.1 library. (authors)
61 RADIATION PROTECTION AND DOSIMETRY↗
Mic-hackathon 2024: hackathon on machine learning for electron and scanning probe microscopy
Microscopy is one of the primary sources of information on materials structure and functionality at the nanometer and atomic scales. The data generated through microscopy is often contained in well-structured datasets, enriched with extensive metadata and sample histories, although not always with the same level of detail or storage format. The broad incorporation of data management plans by major funding agencies ensures the preservation and accessibility of this data. However, deriving insights from these rich datasets remains challenging due to the lack of established code ecosystems, standardized benchmarks, and integration strategies. Correspondingly, the efficiency of data usage is very low, and time expenditures at the analysis stage are enormous. In addition to post-acquisition data analysis, the emergence of application programming interfaces by major microscope manufacturers now creates opportunities for real-time ML-based data analytics to enable automated decision making, and particularly ML-agent controlled real-time microscope operation. Despite these opportunities, there is a significant gap in integrating the ML community with the broader microscopy community, limiting the value that these methods bring to physics and materials discovery and materials optimization. Hackathons address these challenges by fostering collaboration between ML experts and microscopy professionals, encouraging the development of innovative solutions that leverage ML for microscopy and preparing the workforce of the future both for microscopy-intensive domains areas, instrument manufacturers, and ML scientists interested in real world applications for fundamental research, materials optimization, and manufacturing. The hackathon generated benchmark datasets and digital twins of microscopes that further contribute to the development of the field and establish data analysis ecosystems. All the codes can be found at GitHub(https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1) and Zenodo (https://zenodo.org/records/15579940).
97 MATHEMATICS AND COMPUTING↗
Revolutionizing thermal Management in Next-Generation AI data centers: Challenges and breakthrough innovations
Data centers (DCs) serve as critical infrastructure for powering the growth and evolution of AI. Next-generation AI DCs present unique challenges in thermal management driven by unprecedented computational demands. This paper provides a comprehensive summary of key stakeholder perspectives on technology gaps, infrastructure requirements, test bed needs, emerging opportunities, and preliminary solutions related to thermal management for AI DCs. It establishes six strategic pillars of thermal management for next generation AI DC: reliability, deployability, efficiency, resilience, measurability, and valorization. The discussion spans a range of critical topics, including advanced cooling technologies, thermal strategies for emerging modular and edge DCs, system-level optimization and control frameworks, infrastructure planning and grid integration designs, benchmarking approaches, and pathways for waste heat recovery and reuse. The proposed research, development, and demonstration efforts are aimed at accelerating the deployment of AI DCs while ensuring energy efficiency, reliability, safety, and regulatory compliance.
Wang, Pengtao [ORNL] (ORCID:0000000214713429)↗