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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Automated scanning probe microscopy of combinatorial ferroelectric libraries: Gaussian-process-guided exploration and noise-aware experiment planning

Combinatorial materials libraries provide an efficient route for mapping composition–property relationships, but their broader impact depends on rapid, quantitative, and functionally relevant characterization. Scanning Probe Microscopy (SPM), including piezoresponse force microscopy (PFM), offers significant potential for quantitative, functionally relevant combi-library readouts. Here, we implement a fully automated SPM workflow for ferroelectric combinatorial libraries and benchmark Gaussian-process-based Bayesian optimization strategies for autonomous experiment planning. The workflow integrates automated probe motion, contact optimization, imaging, and dual amplitude resonance tracking-PFM spectroscopy, and uses scalarized spectroscopic observables to guide subsequent measurements. Stage motion, probe engagement, in-contact tuning, imaging, spectroscopy, and the choice of the next measurement location all proceed without human input. We demonstrate the approach on Sm-doped BiFeO 3 and Zn x Mg 1−x O libraries. By comparing vanilla Bayesian optimization with a measured-noise variant, we show that explicit treatment of local reproducibility can improve modeling of composition-dependent response when the measured variance is physically meaningful, but can also reduce robustness when variability is dominated by outliers or topographic artifacts. Furthermore, these results establish automated SPM as a bridge between combinatorial synthesis and quantitative functional characterization.

Liu, Yu [University of Tennessee, Knoxville, TN (U↗
DOE OSTI2026

The Role of Nuclear Data Sensitivities in Prompt α-Eigenvalue Predictions of Delayed Critical Benchmarks

Alpha (α) eigenvalues, which describe the logarithmic time derivative of the neutron population in a multiplying system, are integral to time-dependent behavior and diagnostic applications. However, uncertainties in the evaluated nuclear data can significantly impact the accuracy of transport simulations for such quantities. This work explores the use of machine learning models to predict two key outputs, α-eigenvalues and keff bias, using input features derived from α-eigenvalue sensitivities to nuclear data. The criticality safety benchmark models used in this study come from the International Handbook of Evaluated Criticality Safety Benchmark Experiments. Three models, random forest, XGBoost, and NGBoost, are trained on both energy-resolved and energy-summed α sensitivities. For the α-eigenvalue bias prediction, NGBoost achieved the highest R 2 (0.9476) using energy-resolved features, while XGBoost performed best using summed sensitivities. In contrast, when predicting the keff bias, all the models showed moderate predictive capability (best R 2 ≈ 0.72), as the mapping from the static α-sensitivities to the static keff bias was less direct. SHAP (SHapley Additive exPlanations) analysis was used to interpret the model predictions. Across both prediction tasks, the features associated with neutron capture [H-1 (n, γ)], uranium scattering reactions (such as 235 U elastic/inelastic), and actinide capture/fission reactions (such as 239 Pu and 234 U) were consistently identified as the most impactful. This highlights the key role of specific nuclear reactions and energy ranges in shaping both time-dependent and steady-state criticality behavior. These results demonstrated that α-sensitivities, despite being computed for time-dependent metrics, can provide valuable insights for predicting both α-eigenvalues and the keff bias. Moreover, machine learning models offer a promising pathway for uncovering important nuclear data dependencies and guiding future data evaluation efforts.

Nuclear data↗
DOE OSTI2026

Systematic Construction of Time-Dependent Hamiltonians for Microwave-Driven Josephson Circuits

Time-dependent electromagnetic drives are fundamental for controlling complex quantum systems, including superconducting Josephson circuits. In these devices, accurate time-dependent Hamiltonian models are imperative for predicting their dynamics and designing high-fidelity quantum operations. Existing numerical methods, such as black-box quantization (BBQ) and energy-participation ratio (EPR), excel at modeling the static Hamiltonians of Josephson circuits. However, these techniques do not fully capture the behavior of driven circuits stimulated by external microwave drives, nor do they include a generalized approach to account for the inevitable noise and dissipation that enter through microwave ports. Here, we introduce numerical techniques that leverage classical microwave simulations, efficiently executable in finite-element solvers, to obtain the time-dependent Hamiltonian of microwave-driven superconducting circuits with arbitrary geometries under charge, flux, or mixed electromagnetic modulation. Importantly, our techniques do not rely on a lumped-element description of the superconducting circuit, in contrast to previous approaches to tackling this problem. We demonstrate the versatility of our approach by characterizing the driven properties of realistic circuit devices in complex electromagnetic environments, including coherent dynamics due to charge and flux modulation, as well as drive-induced relaxation and dephasing. Our techniques offer a powerful toolbox for optimizing circuit designs and advancing practical applications in superconducting quantum computing.

Lu, Yao [Yale U.; Yale U. (main); Fermilab] (ORCID↗
DOE OSTI2026

Evidence of Z Z γ Production and Observation of 4 ℓ γ in Proton-Proton Collisions at s = 13 TeV

Evidence of the production of two Z bosons and a photon in proton-proton collisions is reported for the first time in CMS. The analysis uses data collected by the CMS experiment between 2016 and 2018 at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 138 fb - 1 . Evidence for the process p p → Z Z γ → 4 ℓ γ ( ℓ = e , μ ), with an observed (expected) significance of 3.7 (3.1) standard deviations in a fiducial region defined by p T γ > 20 GeV , | η γ | < 2.4 , Δ R ( ℓ , γ ) > 0.5 , m Z between 60 and 120 GeV, and the invariant mass of either of the two Z bosons combined with the photon ( m Z γ ) larger than 100 GeV, is reported. The measured (predicted) fiducial cross section is 60 - 22 + 27 ab ( 47.56 ± 0.04 ab ). Additionally, the inclusive production of p p → 4 ℓ γ is studied by removing the m Z γ requirement to include final state radiation where one Z boson decays to 2 ℓ γ , yielding an observed (expected) significance of 5.0 (4.2) standard deviations and a measured (predicted) fiducial cross section of 156 - 35 + 39 ab ( 99.97 ± 0.09 ab ).

Hayrapetyan, Aram [Yerevan Phys. Inst.]↗
DOE OSTI2026

Monte Carlo Event Generation with Continuous Normalizing Flows

We apply continuous normalizing flows trained with the flow matching method to the problem of phase-space sampling in Monte Carlo event generation for high-energy collider physics. Focusing on lepton-pair and top-quark pair production with multiple jets, the two computationally most expensive processes at the Large Hadron Collider, we train helicity-conditioned continuous normalizing flows to remap the random numbers used in matrix element evaluation. Compared to standard methods, we achieve unweighting efficiency improvements by factors of up to 184 and 25 for the two processes at their respective highest jet number, at the cost of an increased evaluation time. When combining the advantages of continuous normalizing flows with the fast evaluation times of coupling-layer-based flows, using the RegFlow approach, we find parton-level unweighted event generation walltime gains of about a factor of 10 at the highest jet numbers. These substantial gains highlight the promise of samplers based on machine learning for next-generation collider experiments.

Bothmann, Enrico [CERN; Gottingen U.] (ORCID:00000↗
DOE OSTI2026

Optical cycling on thorium monoxide for an improved test of fundamental symmetries

Optical cycling refers to repeated excitation and spontaneous emission on an electronic transition in an atom or molecule. Optical cycling in molecules can enable a wide range of quantum control and readout techniques, but unfortunately it has only been demonstrated on a small class of alkalilike or alkaline-earth-like molecules. Thorium monoxide (ThO), a molecule used in one of the most precise permanent electron electric dipole moment (eEDM) searches (ACME†) [Andreev et al., Nature (London) 562, 355 (2018)], does not fall into this category. In this work, we demonstrate the first optical cycling on this nonconventional class over a range of experimental parameter space, including laser intensity, polarization switching rate, and interaction time. We show that both the 𝐽 = 1 and 𝐽 = 2 rotational levels of ThO molecule are capable of cycling an average of 11(2) photons with a single laser, at scattering rates of 1.9⁢(6) × 10 6 and 2.3⁢(7) × 10 6 s −1 , respectively, before population is lost to other vibronic levels. We outline a scheme to apply this demonstrated optical cycling in an ACME-style eEDM measurement, improving the detection efficiency by over fourfold compared to noncycling fluorescence detection. This would lead to over a twofold enhancement in the statistical sensitivity of the eEDM search. This optical-cycling scheme can be further extended to scatter ∼100 photons, which would enable a wider range of quantum control and sensing using ThO molecules.

Atomic & molecular processes in external fields↗
DOE OSTI2026

Improving Process Level Understanding of Boundary Layer Winds over the Northeast U.S. Shelf: The Third Wind Forecast Improvement Project (WFIP3)

The third Wind Forecast Improvement Project (WFIP3), a U.S. Department of Energy and National Oceanic and Atmospheric Administration sponsored investigation, sought to improve understanding of the physical phenomena in the atmosphere and ocean that dictate the structure and variability of wind and thermodynamic fields within the marine atmospheric boundary layer. WFIP3 focused on mesoscale and submesoscale flows -- including sea breezes, low-level jets, low-level clouds, and coastal storms -- and the ability of advanced numerical model parameterizations to represent them within fully coupled oceanic and atmospheric modeling systems and foundational weather forecast models. WFIP3 conducted a comprehensive 18-month observational study over the Northeast U.S. outer continental shelf, a high-use coastal zone, using a 3D multiscale sensor array to highly resolve the temporal, vertical, and horizontal structure of the coupled atmospheric and oceanic boundary layers. Multiple land-based study sites adjacent to the coastal ocean observed surface meteorology and vertical profiles of atmospheric properties via passive infrared and microwave radiometers, active lidars and radars, and radiosondes. At sea, an array of surface flux buoys and two vertical profiling lidar buoys observed both atmospheric and oceanic properties, augmented by land-based oceanographic radar systems and routine ship-based surveys. Intensive observations of the marine atmospheric boundary layer over the ocean was done from an air-sea interaction flux tower and extended deployments of a large autonomous barge platform. Numerous critical forecasting phenomena were observed that are being evaluated within regional coupled and uncoupled modeling systems, including the National Oceanic and Atmospheric Administration's foundational High-Resolution Rapid Refresh forecast model.

Kirincich, Anthony↗
DOE OSTI2026

Neutron scattering for NiPS3

Inelastic magnetic spectra for NiPS3 as published in Phys. Rev. B 108, 104402, 2023; https://doi.org/10.1103/PhysRevB.108.104402 . Analysis scripts and SpinW code also included.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Scalable High-Throughput Open-Air Spray-Plasma Manufacturing of Solid-State Lithium Batteries

This final technical report presents a comprehensive analysis of a novel plasma-based in-line manufacturing process for large-area, LLZO-separator-based, solid-state lithium-ion batteries, demonstrating both technical feasibility and economic advantages over conventional vacuum deposition methods. The technical validation shows that spray-deposition with plasma curing achieves comparable electrode and separator quality to vacuum techniques while enabling continuous processing of components and industrially relevant film areas. Critical material interfaces maintain low porosity and high ionic conductivities, which confirm the process's ability to overcome the primary limitation of conventional methods - the trade-off between deposition quality and economically-viable production scale.

25 ENERGY STORAGE↗
DOE OSTI2026

Steady Spills, Stronger Signals: Machine Learning for Slow Spill Analysis

Particle accelerator experiments rely on stable, consistent proton beams to maximize scientific discovery. This presentation introduces beam spills, duty factor, and beam stability using a meteor shower analogy before exploring how feedback control and machine learning, including recurrent neural networks (RNNs), can analyze spill data, identify patterns, and predict beam behavior. Together, these approaches support beam optimization and improve our understanding of accelerator performance for experiments such as Mu2e.

Prescott, Matthew J. [Fermilab]↗
DOE OSTI2026

The Case for a Muon Collider

In the search for future high-energy collider technologies, the muon collider has emerged as an extremely promising long-term option. The muon’s large mass relative to the electron allows it to reach high mass energies with a small footprint. This talk will first provide an overview of the scientific potential of a future muon collider, followed by a discussion of the key technical challenges that must be overcome, given the muon’s short lifetime and the many acceleration stages in a collider. This talk concludes with encouragement for audiences to join the ongoing international research and development efforts for a muon collider.

Keeler, Dominic [Purdue U., West Lafayette] (ORCID↗
DOE OSTI2026

The Heartbeat of PIP-II

Presenting a bird eye's view of the reference line project in the radio frequency department for the IGNITE OFF competition.

Subedi, Harsheet [Unlisted, US; Fermilab] (ORCID:0↗
DOE OSTI2026

Building the Eyes of Discovery

My presentation focuses on the development and enhancement of sensor modules that help particle detectors “see” particles invisible to the human eye, specifically the Compact Muon Shield detector, which is part of the High-Luminosity Large Hadron Collider. As a CCI intern at Fermilab’s Silicon Detector Facility, I worked on testing and visual inspection of different module parts before assembly and on testing the modules after assembly. My talk highlights how small sensors carry the capability to collect tiny particle signals within the detector and the reliability of control checks pre-assembly for scientists to produce reliable data for future discoveries.

Siddiqui, Hooriya [Fermilab] (ORCID:00090001510973↗
DOE OSTI2026

DUNE: 100 Billion Reasons

20-slide Ignite Off! Challenge: I am presenting a 5-minute overview of the DUNE project along with my work in data validation of the TMS. I begin with some unique facts regarding neutrinos, then I move into a brief explanation of what DUNE is, and finally close off with a mention of my contributions to the experiment.

Vershaw, Andre [Unlisted, US, IL] (ORCID:000900084↗