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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Causes of and Solutions to Wind Speed Bias in NREL's 2020 Offshore Wind Resource Assessment for the California Pacific Outer Continental Shelf

This report provides the results of a detailed analysis into the causes of high wind speed bias in the 20-year wind resource data set for offshore California the National Renewable Energy Laboratory (NREL) released in 2020, herein called CA20. The data set was developed using the state-of-the-art Weather Research and Forecasting (WRF) model. Notably, no floating lidars were available at the time in offshore California to validate offshore hub-height wind speeds. In late 2020, the Pacific Northwest National Laboratory (PNNL) deployed two floating lidars in the California outer continental shelf (OCS), near the Bureau of Ocean Energy Management (BOEM) call areas of Humboldt and Morro Bay. Using these observations through 2021, NREL found considerable bias in modeled hub-height winds at both locations: up to +2 m/s at Humboldt over a 6-month period, and up to +1 m/s at Morro Bay over a one-year period. Upon the discovery of this bias, the Department of Energy (DOE) and BOEM funded NREL and PNNL to investigate the causes of, impacts of, and solutions to the bias in the CA20 data set. This report summarizes the findings of this research. We first investigated whether different WRF model setups could lead to reduced bias. We found that the choice of planetary boundary layer (PBL) scheme - which controls the vertical turbulent mixing of momentum, heat, and moisture in the lowermost part of the atmosphere - greatly affected hub-height wind speeds in the region. Specifically, switching from the Mellor-Yamada-Nakanishi-Niino (MYNN) scheme used in CA20 (and widely used across a range of operational and research weather models) to the less common Yonsei University (YSU) scheme nearly eliminated the bias at both the Humboldt and Morro Bay lidar locations. The large discrepancy between the MYNN- and YSU-modeled hub-height winds pointed towards the role of atmospheric stability. In general, PBL schemes agree well in conditions of high turbulence and mixing, normally referred to as "unstable" conditions. By contrast, PBL schemes start to diverge in "stable" conditions, where turbulence is low and thermal stratification (i.e., higher temperature air sitting on top of colder air) greatly suppresses vertical mixing. Under such conditions, winds aloft can decouple from surface effects and greatly accelerate, causing high wind speeds at hub-height and frequent low-level jets (LLJs). We determined that these stable conditions are in fact dominant in offshore California. The region is characterized by moderate-to-extreme stable stratification with a LLJ on average around 200 meters above sea-level. To our knowledge, no wind energy area globally has as strongly stable stratification as offshore California. Under these extreme conditions, we determined that the MYNN scheme models higher stability than YSU, resulting in less vertical turbulent mixing than YSU, allowing for the acceleration of hub-height winds, more intense LLJs, and higher-amplitude inertial oscillations. Using surface observations, we found that MYNN overestimates near-surface stability, whereas YSU tends to model stability better. We then considered several short-term case studies to assess additional meteorological drivers of the bias at Humboldt. We found that during synoptic scale northerly flows driven by the North Pacific High and inland thermal low, a coastal warm bias in the MYNN case studies contributes to the modeled wind speed bias by altering the boundary layer thermodynamics via a thermal wind mechanism. Given the strong performance of the YSU-based runs in offshore California, NREL has produced and published an updated version of the CA20 data set with YSU as the PBL scheme. This updated data set is now part of NREL's 2023 National Offshore Wind (NOW-23) data set, which covers all the U.S. offshore waters. The development and final validation of the NOW-23 data set in offshore California is documented in this report.

17 WIND ENERGY↗
DOE OSTI2025

Ensuring continued operation of INSPIRE as a cornerstone of the HEP information infrastructure

The INSPIRE platform — the most widely-used discovery service specifically tailored to the needs of researchers in High Energy Physics (HEP) — has become a central component of the information infrastructure for the discipline. Despite this, INSPIRE's continued sustainability is frequently endangered by resource constraints, recently made more acute by the loss of support from historical funders changing their research priorities. If the European particle physics community wishes to ensure INSPIRE's long-term sustainability, the community should secure international support and ensure appropriate funding.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

High entropy alloys as catalysts: A focused review

A brief literature review of recent experimental and computational efforts on the use of high entropy alloys (HEAs) as catalysts is presented, while also sharing some perspectives and future insights. To fully broach the vast compositional possibilities of HEA materials, integrating computational modeling with high-throughput experimental synthesis and validation is necessary to accelerate their design and development. Once identified, specific HEAs can be a class of materials for the next generation of catalysts when addressing global challenges related to energy independence, commodity chemical production, environmental remediation, abating emissions, and modernized domestic supply chain resilience.

42 - ENGINEERING↗
DOE OSTI2023

Establishing a versatile toolkit of flux enhanced strains and cell extracts for pathway prototyping

Building and optimizing biosynthetic pathways in engineered cells holds promise to address societal needs in energy, materials, and medicine, but it is often time-consuming. Cell-free synthetic biology has emerged as a powerful tool to accelerate design-build-test-learn cycles for pathway engineering with increased tolerance to toxic compounds. However, most cell-free pathway prototyping to date has been performed in extracts from wildtype cells which often do not have sufficient flux towards the pathways of interest, which can be enhanced by engineering. Here, in this study, to address this gap, we create a set of engineered Escherichia coli and Saccharomyces cerevisiae strains rewired via CRISPR-dCas9 to achieve high-flux toward key metabolic precursors; namely, acetyl-CoA, shikimate, triose-phosphate, oxaloacetate, α-ketoglutarate, and glucose-6-phosphate. Cell-free extracts generated from these strains are used for targeted enzyme screening in vitro. As model systems, we assess in vivo and in vitro production of triacetic acid lactone from acetyl-CoA and muconic acid from the shikimate pathway. The need for these platforms is exemplified by the fact that muconic acid cannot be detected in wildtype extracts provided with the same biosynthetic enzymes. We also perform metabolomic comparison to understand biochemical differences between the cellular and cell-free muconic acid synthesis systems (E. coli and S. cerevisiae cells and cell extracts with and without metabolic rewiring). While any given pathway has different interfaces with metabolism, we anticipate that this set of pre-optimized, flux enhanced cell extracts will enable prototyping efforts for new biosynthetic pathways and the discovery of biochemical functions of enzymes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

First Demonstration of a Liquid Argon Time Projection Chamber Based Search for Intranuclear Neutron-Antineutron Transitions and Annihilation in $^{40}$Ar using the MicroBooNE Detector

We present a novel methodology to search for intranuclear neutron-antineutron transition ($n\rightarrow\bar{n}$) followed by $\bar{n}$-nucleon annihilation within an $^{40}$Ar nucleus, using the MicroBooNE liquid argon time projection chamber (LArTPC) detector. A discovery of $n\rightarrow\bar{n}$ transition or a new best limit on the lifetime of this process would either constitute physics beyond the Standard Model or greatly constrain theories of baryogenesis, respectively. The approach presented in this paper makes use of deep learning methods to select $n\rightarrow\bar{n}$ events based on their unique features and differentiate them from cosmogenic backgrounds. The achieved signal and background efficiencies are (70.22$\pm$6.04)\% and (0.0020$\pm$0.0003)\%, respectively. A demonstration of a search is performed with a data set corresponding to an exposure of $3.32 \times10^{26}\,$neutron-years, and where the background rate is constrained through direct measurement, assuming the presence of a negligible signal. With this approach, no excess of events over the background prediction is observed, setting a demonstrative lower bound on the $n\rightarrow\bar{n}$ lifetime in $^{40}$Ar of $\tau_{\textrm{m}} \gtrsim 1.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Ultimate Colliders

Our understanding of the Universe critically depends on the fundamental knowledge of particles and fields, which represents a central endeavor of modern high-energy physics. Energy frontier particle colliders - arguably, among the largest, most complex and advanced scientific instruments of modern times - for many decades have been at the forefront of scientific discoveries in high-energy physics. Due to technology advances and beam physics breakthroughs, the colliding beam facilities have progressed immensely and now operate at energies and luminosities many orders of magnitude greater than the pioneering instruments of the early 1960s. While the Large Hadron Collider and the Super-KEKB factory represent the frontier hadron and lepton colliders of today, respectively, future colliders are an essential component of a strategic vision for particle physics. Conceptual studies and technical developments for several exciting near- and medium-term future collider options are underway internationally. Analysis of numerous proposals and studies for far-future colliders indicate the limits of the collider beam technology due to machine size, cost, and power consumption, and call for a paradigm shift of the particle physics research at ultra-high energy but low luminosity colliders approaching or exceeding 1 PeV center-of-mass energy scale.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Superconductivity and normal-state transport in compressively strained La 2 PrNi 2 O 7 thin films

The discovery of superconductivity under high pressure in Ruddlesden-Popper phases of bulk nickelates has sparked great interest in stabilizing ambient pressure superconductivity in thin-film form using epitaxial strain. Recently, signs of superconductivity have been observed in compressively strained bilayer nickelate thin films with an onset temperature exceeding 40 K, albeit with broad and two-step-like transitions. Here, in this study, we report intrinsic superconductivity and normal-state transport properties in compressively strained La 2 PrNi 2 O 7 thin films, achieved through a combination of isovalent Pr substitution, growth optimization, and precision ozone annealing. The superconducting onset occurs above 48 K, with zero resistance reached above 30 K, and the critical current density at 1.4 K is 100-fold larger than previous reports. The normal-state resistivity exhibits quadratic temperature dependence indicative of Fermi liquid behaviour, and other phenomenological similarities to transport in overdoped cuprates suggest parallels in their emergent properties.

Liu, Yidi [SLAC National Accelerator Laboratory (S↗
DOE OSTI2025

Counterpart identification and classification for eRASS1 and characterisation of the active galactic nuclei content

Context. Accurately accounting for the Active Galactic Nucleus (AGN) phase in galaxy evolution requires a large, clean AGN sample. This is now possible with SRG/eROSITA, which completed its first all-sky X-ray survey (eRASS1) on June 12, 2020. The public Data Release 1 (DR1, Jan 31, 2024) includes 930,203 sources from the western Galactic hemisphere. Aims. The data enable the selection of a large AGN sample and the discovery of rare sources. However, scientific return depends on accurate characterisation of the X-ray emitters, requiring high-quality multi-wavelength data. This paper presents the identification and classification of optical and infrared counterparts to eRASS1 sources. Methods. Counterparts to eRASS1 X-ray point sources were identified using Gaia DR3, CatWISE2020, and Legacy Survey DR10 (LS10) with the Bayesian NWAY algorithm and trained priors. Sources were classified as Galactic or extragalactic via a machine-learning model combining optical/IR and X-ray properties, trained on a reference sample. For extragalactic LS10 sources, photometric redshifts were computed using CIRCLEZ. Results. Within the LS10 footprint, all 656,614 eROSITA/DR1 sources have at least one possible optical counterpart; ∼570 000 are extragalactic and likely AGN. Half are new detections compared to AllWISE, Gaia, and Quaia AGN catalogues. Gaia and CatWISE2020 counterparts are less reliable, due to the survey’s shallowness and the limited amount of features available to assess the probability of being an X-ray emitter. In the Galactic plane, where the overdensity of stellar sources also increases the chance of associations, using conservative reliability cuts, we identified approximately 18 000 Gaia and 55 000 CatWISE2020 extragalactic sources. Conclusions. We have released three high-quality counterpart catalogues – plus the training and validation sets – as a benchmark for the field. These datasets have many applications, but in particular, they empower researchers to build AGN samples tailored for completeness and purity, accelerating the hunt for the Universe’s most energetic engines.

X-rays: general↗
DOE OSTI2026

Crystal generation using the fully differentiable pipeline and latent space optimization

We present a materials generation framework that couples a symmetry-conditioned variational autoencoder with a differentiable SO(3) power spectrum objective to steer candidates toward a specified local environment under the crystallographic constraints. In particular, we implement a fully differentiable pipeline that performs batch-wise optimization on both direct and latent crystallographic representations. Using the GPU acceleration, the implementation achieves about fivefold speed compared to our previous CPU workflow, while yielding comparable outcomes. In addition, we introduce the optimization strategy that alternatively performs optimization on the direct and latent crystal representations. This dual-level relaxation approach can effectively escape local minima defined by different objective gradients, thus increasing the success rate of generating complex structures satisfying the target local environments. This framework can be extended to systems consisting of multi-components and multi-environments, providing a scalable route to generate material structures with the target local environment.

conditional VAE↗
DOE OSTI2026

Artificial Intelligence for Event Reconstruction and Higgs Physics at CMS and Future Colliders

This dissertation charts a trajectory in which advances in artificial intelligence (AI) play a central role in pushing the high-energy physics frontier, complementing progress driven by higher collision energies and larger colliders. The discovery potential of the LHC and future colliders relies on accurate reconstruction of increasingly complex particle collision events. In the CMS experiment, this task is performed by the particle-flow (PF) algorithm. This dissertation presents the first implementation of a machine-learning-based particle-flow (MLPF) reconstruction in the CMS detector based on transformer architectures. In simulated top quark--antiquark pair (ttbar) events under LHC Run~3 (2023--2024) conditions, MLPF improves jet energy resolution by 10--20\% compared to standard PF for jets with transverse momentum between 30--100\GeV. Runtime performance is evaluated using simulated multijet events, with a median inference time of 20\unit{ms} per event on an NVIDIA L4 GPU, compa red to approximately 110\unit{ms} for standard PF. The MLPF algorithm is also validated on Run~3 collision data, representing the first data-validated ML-based reconstruction pipeline at any LHC experiment. We then extend MLPF toward future electron--positron colliders and introduce the first full-simulation cross-detector transfer learning workflow for PF reconstruction. The model is pre-trained on simulated events from the Compact Linear Collider detector (CLICdet) and fine-tuned on the CLIC-like detector (CLD) proposed for the Future Circular Collider (FCC). This approach achieves up to a 40\% improvement in jet energy resolution over rule-based reconstruction while reducing the required training dataset size by an order of magnitude, demonstrating the potential of AI to accelerate detector development and optimization. This dissertation also demonstrates how modern AI techniques enhance the sensitivity of LHC physics analyses. A CMS search for highly Lorentz-boosted Higgs bosons decaying to \textrm{W} boson pairs is presented, focusing on the single-lepton final state. A dedicated fine-tuning strategy for \ParT yields an approximately 70\% increase in expected sensitivity relative to the baseline model. The analysis uses proton--proton collision data at a center-of-mass energy of \ensuremath{\sqrt{s}=13\TeV} collected by CMS between 2016 and 2018, corresponding to an integrated luminosity of 138\ensuremath{\ \mathrm{fb}^{-1}}. The expected significance of the search is $1.86\sigma$, with an observed signal strength of $-0.19^{+0.48}_{-0.46}$. Finally, explainable AI techniques are applied to the MLPF and \ParticleNet algorithms using layerwise relevance propagation, showing that both models base their predictions on physically meaningful features consistent with our physics intuition. Together, these results demonstrate how advanced AI methods can enhance reconstruction, analysis sensitivity, and interpretability, shaping the next era of experimental parti cle physics.

Mokhtar, Farouk [UC, San Diego]↗
DOE OSTI2025

Description of the Electronic Structure of Oxyhemoglobin Using Fe L-Edge X-ray Absorption Spectroscopy

The electronic structure of oxyhemoglobin has been controversial since the discovery of the compound’s diamagnetism in 1936. Here, this study uses partial fluorescence yield Fe L-edge X-ray absorption spectroscopy (XAS) in the 3s→2p fluorescence on oxyhemoglobin solutions, measured using a transition-edge sensor detector, to obtain a quantitative experimental description of the electronic structure of the O 2 -bound iron site. The spectrum is very different from typical low-spin Fe II and Fe III heme spectra, and multiplet simulations indicate a mixed ground configuration with ∼57% low-spin Fe III and ∼43% low-spin Fe II character. This is also very different from the Fe II character found for the picket-fence porphyrin model complex. The oxyhemoglobin L-edge XAS data further show that the O 2 ligand engages in a weak σ- but strong π-bond with the iron ion, leading to the overall strong Fe–O 2 bond required for O 2 transport.

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

Genesis Data Card Schema, Template and Supporting Tools

Genesis Data Cards provide a standardized template and schema for documenting scientific datasets in support of discovery, access, interoperability, reusability, governed use, and AI usability. This release of the Genesis Data Card repository includes a versioned Markdown template, a LinkML schema with generated Pydantic and JSON artifacts, schema documentation, and example completed data cards. Validation tooling is provided to ensure that completed data cards conform to the schema prior to submission. Accompanying documentation for the structured metadata is provided as a Field Reference Guide. The schema and accompanying template provided in this repository address the call for actionable context that enables humans and AI systems to find, access, interpret, cite, and reuse data, and, when appropriate, integrate it into AI and machine learning workflows. The data card is intended to serve as a common metadata artifact intended to support standardized, cross-program dataset documentation across Department of Energy (DOE)-aligned efforts, including but not limited to Genesis Mission-related implementations, the Office of Science, National Nuclear Security Administration (NNSA), and Advanced Simulation and Computing (ASC) data governance and stewardship initiatives.

data card↗
DOE OSTI2022

A Muon Collider Facility for Physics Discovery

Muon colliders provide a unique route to deliver high energy collisions that enable discovery searches and precision measurements to extend our understanding of the fundamental laws of physics. The muon collider design aims to deliver physics reach at the highest energies with costs, power consumption and on a time scale that may prove favorable relative to other proposed facilities. In this context, a new international collaboration has formed to further extend the design concepts and performance studies of such a machine. This effort is focused on delivering the elements of a $\sim$10 TeV center of mass (CM) energy design to explore the physics energy frontier. The path to such a machine may pass through lower energy options. Currently a 3 TeV CM stage is considered. Other energy stages could also be explored, e.g. an s-channel Higgs Factory operating at 125 GeV CM. We describe the status of the R&D and design effort towards such a machine and lay out a plan to bring these concepts to maturity as a tool for the high energy physics community.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Ultraheavy diquark decaying into vectorlike quarks at the LHC

We explore the discovery potential of ultraheavy (7–8.5 TeV) diquark scalars (𝑆 𝑢⁢𝑢 ) produced in the collision of two up quarks at the LHC. Assuming that the diquark scalar decays into two vectorlike quarks of mass around 2 TeV, each of them decaying into a 𝑊 + boson and a 𝑏 quark, we focus on the fully hadronic final state. We present a signal-from-background separation study based on a discriminator built with machine learning techniques. For this six-jet final state and a luminosity of 3000 fb −1 , we estimate that a diquark scalar of mass near 8 TeV may be discovered or ruled out even when its coupling to up quarks is as low as 0.2.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Guest-Induced Large and Tunable Negative Thermal Expansion in Soft Microporous Carbon

Materials that exhibit negative thermal expansion (NTE) are of fundamental interest due to their rarity and counterintuitive behavior. While research in this area has been directed toward the discovery of materials that display NTE and explaining its origin, there has been less attention to describing the complexities of a secondary phase or other guests that could influence the magnitude and mechanism of NTE. We report herein that zeolite-templated carbon (ZTC), a soft carbonaceous framework solid with ordered microporosity, exhibits a large and widely tunable thermal expansion in the presence of adsorbed guests. For ZTC in the presence of CO 2 at 1 bar, the largest coefficient of isotropic NTE ever observed (−8.4 × 10 –4 K –1 ) is measured between 200 and 220 K. These results comprise a tunable mechanism of thermal expansion based on the interaction between two independent, positively expanding phases that together give rise to an anomalous guest-induced NTE under certain conditions.

03 NATURAL GAS↗
DOE OSTI2025

Semi-supervised permutation invariant particle-level anomaly detection

The development of analysis methods to distinguish potential beyond the Standard Model phenomena in a model-agnostic way can significantly enhance the discovery reach in collider experiments. However, the typical machine learning (ML) algorithms employed for this task require fixed length and ordered inputs that break the natural permutation invariance in collision events. To address this, a semi-supervised anomaly detection tool is presented that takes a variable number of particle-level inputs and leverages a signal model to encode this information into a permutation invariant, event-level representation via supervised training with a Particle Flow Network (PFN). Data events are then encoded into this representation and given as input to an autoencoder for unsupervised ANomaly deTEction on particLe flOw latent sPacE (ANTELOPE), classifying anomalous events based on a low-level and permutation invariant input modeling. Performance of the ANTELOPE architecture is evaluated on simulated samples of hadronic processes in a high energy collider experiment, showing good capability to distinguish disparate models of new physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Toward Trustworthy Autonomous Science: A Two-Year Community Roadmap

One year ago, the AISLE roadmap argued that autonomous laboratories operated as isolated islands and proposed a grassroots network organized around five critical dimensions. The field has since moved faster than that roadmap anticipated: multi-agent systems have produced experimentally validated hypotheses, self-driving laboratories have grown more interoperable and orchestrated, reasoning-trained and domain foundation models have raised the capability ceiling, and the Genesis Mission has placed autonomous experimentation at the center of U.S. federal science strategy, with industry emerging as a primary actor. Progress has met a sobering counter-current, including a corrected flagship discovery result, benchmarks showing that agents which rival experts on closed-ended questions still complete only a fraction of open-ended research, and fabricated citations surfacing at leading venues. We read this as the defining tension of the field: producing a candidate discovery is no longer the hard part, but verifying it is, and this asymmetry now limits autonomous science more than raw model capability. Accordingly, we update the roadmap around seven dimensions, revisiting the original five and elevating two former cross-cutting concerns, trust, verification, and reproducibility, and safety, security, and governance, to first-class status. We assess the original milestones (M1 through M14) as achieved, partially achieved, reframed, or open, add four new milestones (M15 through M18) for the elevated dimensions, and scope the path forward to a two-year horizon, with the first year concentrating on interfaces, protocol adoption, and the scaffolding of verification, and the second targeting federation, zero-trust coordination, and governance. Throughout, we position the grassroots network as the interoperability fabric that lets national programs, international initiatives, and commercial platforms connect rather than re-silo.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Optimal celestial bodies for dark matter detection

A wide variety of celestial bodies have been considered as dark matter detectors. Which stands the best chance of delivering the discovery of dark matter? Which is the most powerful dark matter detector? We investigate a range of objects, including the Sun, Earth, Jupiter, Brown Dwarfs, White Dwarfs, Neutron Stars, Stellar populations, and Exoplanets. We quantify how different objects are optimal dark matter detectors in different regimes by deconstructing some of the in-built assumptions in these search sensitivities, including observation potential and particle model assumptions. We find new constraints and future sensitivities across a range of dark matter annihilation final states. We quantify mediator properties leading to detectable celestial-body energy injection or Standard Model fluxes, and show how different objects can be expected to deliver corroborating signals. We discuss different search strategies, their opportunities and limitations, and the interplay of regimes where different celestial objects are optimal dark matter detectors. Deconstructing the assumptions of these searches leads us to point out a new search using the Galactic center stellar population that can provide greater sensitivity to the dark matter-nucleon scattering cross section than the Sun, despite being significantly further away in our Galaxy.

79 ASTRONOMY AND ASTROPHYSICS↗