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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

2022 AI Testbed Expeditions Report

By exploiting the coherent properties of a light source, coherent diffraction imaging (CDI) is able to obtain the sample image at a nanoscale resolution using the measured diffraction pattern. Bragg Coherent Diffraction Imaging (BCDI) has become valuable for recovering the displacement and strain field of crystals, providing a valuable tool in material science and solid-state physics. X-ray ptychography is another emerging CDI technique that can produce a high-resolution image of the extended sample and has become popular in many research areas (e.g., materials science, biology, electronics, and optics characterization). CDI including BCDI and ptychography has become an established technique in Synchrotron Facilities including the Advanced Photon Source (APS) and will greatly benefit from the 100x coherent flux increase of the upcoming APS Upgrade (APSU). The current image formation process in CDI employs iterative phase retrieval algorithms, which is a time-consuming and computationally expensive process. Especially after APSU, the traditional iterative methods will not be able to match the experimental data acquisition speed. We employ deep learning (DL) approach to replace the iterative approaches, therefore allowing hundreds of times faster recovery of the object. We developed AutoPhaseNN, a DL-based approach which learns to solve the inverse problem without labeled data. Taking 3D BCDI as a representative technique, AutoPhaseNN has been demonstrated to be one hundred times faster than traditional iterative phase retrieval methods while providing comparable image quality. The current network is trained with 64 x 64 x 64 data size, to achieve higher resolution imaging, we will need to scale the network to input and train/infer 3D arrays of size 256 x 256 x 256 (today) and of size 2560x2560x2560 (APSU). However, the scalability of the network is restricted due to the memory-intensive training process. To perform the training for a 256 x 256 x 256 data size, the required memory exceeds the capacity of the current machine. In this project, we explore using Sambanova system to train the network for the direct data inversion for CDI.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Current Status and Future Directions of All-Solid-State Batteries with Lithium Metal Anodes, Sulfide Electrolytes, and Layered Ternary Oxide Cathodes

All-solid-state batteries (ASSBs) offer great promise as a next-generation energy storage technology with higher energy density, wider operating temperature range, and improved safety for electric vehicles. ASSBs employing lithium metal anodes (Li), sulfide-based solid-state electrolytes (SSE), and Ni-rich layered transition metal oxide cathodes (LiMO 2 , M = Ni, Mn, Co, Al) are particularly promising due to its superior electrochemical performance compared to other solid-electrolyte systems. However, the battery cycle life at high cathode mass loading and high current is still limited because the failure mechanism is not fully understood. Lithium dendrite growth at the anode or inside a solid electrolyte still represents as a serious risk of cell failure. Interfacial resistance increases attributed to electrolyte decomposition and interfacial void formation at both cathode–electrolyte and anode–electrolyte interfaces lead to gradual capacity fading. In this Review, we present the fundamental challenges and recent scientific understandings of each component in ASSBs. The novel diagnostic tools for these components, especially the interfaces buried under the surface that are often hard for characterization are mainly examined. Lastly, we offer a perspective for future research directions. We hope this Review will provide a timely snapshot of state-of-the-art research progress in ASSBs to accelerate the development of ASSBs.

25 ENERGY STORAGE↗
DOE OSTI2025

NEPATEC2.0: NEPA Text Corpus v2.0

The National Environmental Policy Act of 1969, as amended (NEPA), is a major environmental law in the United States, requiring Federal agencies to consider and document potential environmental impacts before deciding on a proposed action. Modernization of NEPA and permitting processes faces significant challenges due to the lack of standardized formats and interoperable systems for organizing and sharing NEPA-related information across agencies. Much of the information gathered during NEPA reviews is written into documents such as categorical exclusions, environmental assessments, and environmental impact statements, then filed in predominately independent agency file stores that may or may not be publicly accessible. The application of metadata and data standards, such as those recommended by the Council on Environmental Quality (CEQ), to NEPA documents offers a shared vocabulary and structure for key entities like projects, processes, and documents that can streamline information exchange and enhance collaboration across systems. In this work, we publicly release NEPATEC2.0, an expanded corpus of NEPA documents with associated metadata. NEPATEC2.0 encompasses approximately 120,000 documents from 60,000 projects prepared by more than 60 different agencies. Modeled to align with CEQ metadata standards, NEPATEC2.0 promotes consistency in environmental reviews and supports the ongoing effort to modernize permitting technologies by facilitating more transparent, efficient, and data-driven decision-making. Importantly, NEPATEC2.0 demonstrates the possibilities and limitations of large language model-based prompting to extract information from NEPA documents at scale.

environmental review↗
DOE OSTI2024

Differential cross-sections for events with missing transverse momentum and jets measured with the ATLAS detector in 13 TeV proton-proton collisions

Measurements of inclusive, differential cross-sections for the production of events with missing transverse momentum in association with jets in proton-proton collisions at √s = 13 TeV are presented. The measurements are made with the ATLAS detector using an integrated luminosity of 140 fb –1 and include measurements of dijet distributions in a region in which vector-boson fusion processes are enhanced. They are unfolded to correct for detector resolution and efficiency within the fiducial acceptance, and are designed to allow robust comparisons with a wide range of theoretical predictions. A measurement of differential cross sections for the Z → νν process is made. The measurements are generally well-described by Standard Model predictions except for the dijet invariant mass distribution. Auxiliary measurements of the hadronic system recoiling against isolated leptons, and photons, are also made in the same phase space. Ratios between the measured distributions are then derived, to take advantage of cancellations in modelling effects and some of the major systematic uncertainties. These measurements are sensitive to new phenomena, and provide a mechanism to easily set constraints on phenomenological models. To illustrate the robustness of the approach, these ratios are compared with two common Dark Matter models, where the constraints derived from the measurement are comparable to those set by dedicated detector-level searches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Measurements of jet observables sensitive to $\textit{b}$-quark fragmentation in $t\bar{t}$ events at the LHC with the ATLAS detector

Several observables sensitive to the fragmentation of $\textit{b}$ quarks into $\textit{b}$ hadrons are measured using 36 fb -1 of $\sqrt{s}$ = 13 TeV proton-proton collision data collected with the ATLAS detector at the LHC. Jets containing b hadrons are obtained from a sample of dileptonic $t\bar{t}$ events, and the associated set of charged-particle tracks is separated into those from the primary $\textit{pp}$ interaction vertex and those from the displaced b-decay secondary vertex. This division is used to construct observables that characterize the longitudinal and transverse momentum distributions of the $\textit{b}$ hadron within the jet. The measurements have been corrected for detector effects and provide a test of heavy-quark-fragmentation modeling at the LHC in a system where the top-quark decay products are color connected to the proton beam remnants. The unfolded distributions are compared with the predictions of several modern Monte Carlo parton-shower generators and generator tunes, and a wide range of agreement with the data is observed, with $\textit{p}$ values varying from 5 × 10 -4 to 0.98. These measurements complement similar measurements from $e^+e^-$ collider experiments in which the $\textit{b}$ quarks originate from a color singlet $Z/γ^{\ast}$.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Search for heavy, long-lived, charged particles with large ionisation energy loss in $pp$ collisions at $ \sqrt{s} $ = 13 TeV using the ATLAS experiment and the full Run 2 dataset

This paper presents a search for hypothetical massive, charged, long-lived particles with the ATLAS detector at the LHC using an integrated luminosity of 139 fb –1 of proton–proton collisions at √s =13 TeV. These particles are expected to move significantly slower than the speed of light and should be identifiable by their high transverse momenta and anomalously large specific ionisation losses, dE/dx. Trajectories reconstructed solely by the inner tracking system and a dE/dx measurement in the pixel detector layers provide sensitivity to particles with lifetimes down to O(1) ns with a mass, measured using the Bethe–Bloch relation, ranging from 100 GeV to 3 TeV. Interpretations for pair-production of R-hadrons, charginos and staus in scenarios of supersymmetry compatible with these particles being long-lived are presented, with mass limits extending considerably beyond those from previous searches in broad ranges of lifetime.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Data-driven design of electrolyte additives supporting high-performance 5 V LiNi 0.5 Mn 1.5 O 4 positive electrodes

LiNi 0.5 Mn 1.5 O 4 (LNMO) is a high-capacity spinel-structured material with an average lithiation/de-lithiation potential at ca. 4.6–4.7 V vs Li + /Li, far exceeding the stability limits of electrolytes. An efficient way to enable LNMO in lithium-ion batteries is to reformulate an electrolyte composition that stabilizes both graphitic (Gr) negative electrode with solid-electrolyte-interphase and LNMO with cathode-electrolyte-interphase. In this study, we select and test a diverse collection of 28 single and dual additives for the Gr||LNMO battery system. Subsequently, we train machine learning models on this dataset and employ the trained models to suggest 6 binary compositions out of 125, based on predicted final area-specific-impedance, impedance rise, and final specific-capacity. Such machine learning-generated new additives outperform the initial dataset. This finding not only underscores the efficacy of machine learning in identifying materials in a highly complicated application space but also showcases an accelerated material discovery workflow that directly integrates data-driven methods with battery testing experiments.

batteries↗
DOE OSTI2024

Inclusive and differential cross-section measurements of $ t\overline{t}Z$ production in pp collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector, including EFT and spin-correlation interpretations

Measurements of both the inclusive and differential production cross sections of a top-quark-top-antiquark pair in association with a Z boson ($t\overline{t}Z$) are presented. Final states with two, three or four isolated leptons (electrons or muons) are targeted. The measurements use the data recorded by the ATLAS detector in pp collisions at $\sqrt{s}$ = 13 TeV at the Large Hadron Collider during the years 2015–2018, corresponding to an integrated luminosity of 140 fb -1 . The inclusive cross section is measured to be ${\sigma}_{t\overline{t}Z}$ = 0.86 ± 0.04 (stat.) ± 0.04 (syst.) pb and found to be in agreement with the most advanced Standard Model predictions. The differential measurements are presented as a function of a number of observables that probe the kinematics of the $t\overline{t}Z$ system. Both the absolute and normalised differential cross-section measurements are performed at particle level and parton level for specific fiducial volumes, and are compared with NLO+NNLL theoretical predictions. The results are interpreted in the framework of Standard Model effective field theory and used to set limits on a large number of dimension-6 operators involving the top quark. The first measurement of spin correlations in $t\overline{t}Z$ events is presented: the results are in agreement with the Standard Model expectations, and the null hypothesis of no spin correlations is disfavoured with a significance of 1.8 standard deviations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Amplitude analysis of ψ3686→γKS0KS0

Using (2712 ± 14) × 106ψ(3686) events collected with the BESIII detector, we perform the first amplitude analysis of the radiative decay ψ3686→γKS0KS0$$ \psi (3686)\to \gamma {K}_S^0{K}_S^0 $$ within the mass region MKS0KS0<2.8$$ {M}_{K_S^0{K}_S^0}<2.8 $$ GeV/c2. Employing a one-channel K-matrix approach for the description of the dynamics of the KS0KS0$$ {K}_S^0{K}_S^0 $$ system, the data sample is well described with four poles for the f0-wave and three poles for the f2-wave. The determined pole positions are consistent with those of well-established resonance states. The observed f0 and f2 states are found to be in agreement with those produced in radiative J/ψ decays. The production behaviors of f0 and f2 poles in ψ3686→γKS0KS0$$ \psi (3686)\to \gamma {K}_S^0{K}_S^0 $$ are qualified with their residues and the converted branching fractions. By comparing with J/ψ→γKS0KS0$$ J/\psi \to \gamma {K}_S^0{K}_S^0 $$ decay, the ratios Bψ3686→γf0,2BJ/ψ→γf0,2$$ \frac{\mathcal{B}\left(\psi (3686)\to \gamma {f}_{0,2}\right)}{\mathcal{B}\left(J/\psi \to \gamma {f}_{0,2}\right)} $$ are determined, which provides crucial experimental inputs on the internal structure of the f0,2 states, especially their potential mixing with glueball components.

Ablikim, M↗
DOE OSTI2026

Measurement of the top-quark mass using decays with a J/ψ meson at s=13 TeV with the ATLAS detector

The top-quark mass is measured using top-quark decays producing an isolated lepton and J/ψ meson reconstructed in its μ+μ− decay mode. The data sample was recorded with the ATLAS detector in proton-proton collisions at a centre-of-mass energy of s=13$$ \sqrt{s}=13 $$ TeV during Run 2 of the Large Hadron Collider, corresponding to an integrated luminosity of 140 fb−1. The measurement is based on the invariant mass m(ℓμ+μ−) of the system made of the isolated lepton ℓ from the W boson decay and the non-isolated μ+μ− pair from a J/ψ decay of a b-hadron, exploiting its sensitivity to the top-quark mass. An unbinned maximum-likelihood fit to the m(ℓμ+μ−) distribution is performed to extract the top-quark mass. The top-quark mass is measured to be mtop = 172.17 ± 0.80 (stat) ± 0.81 (syst) ± 1.07 (recoil) GeV, with a total uncertainty of 1.56 GeV. The third uncertainty arises from changing the dipole parton shower gluon-recoil scheme used in top-quark decays.

Aad, G↗
DOE OSTI2024

The ATLAS experiment at the CERN Large Hadron Collider: a description of the detector configuration for Run 3

The ATLAS detector is installed in its experimental cavern at Point 1 of the CERN Large Hadron Collider. During Run 2 of the LHC, a luminosity of ℒ = 2 × 10 34 cm -2 s -1 was routinely achieved at the start of fills, twice the design luminosity. For Run 3, accelerator improvements, notably luminosity levelling, allow sustained running at an instantaneous luminosity of ℒ = 2 × 10 34 cm -2 s -1 , with an average of up to 60 interactions per bunch crossing. The ATLAS detector has been upgraded to recover Run 1 single-lepton trigger thresholds while operating comfortably under Run 3 sustained pileup conditions. A fourth pixel layer 3.3 cm from the beam axis was added before Run 2 to improve vertex reconstruction and b-tagging performance. New Liquid Argon Calorimeter digital trigger electronics, with corresponding upgrades to the Trigger and Data Acquisition system, take advantage of a factor of 10 finer granularity to improve triggering on electrons, photons, taus, and hadronic signatures through increased pileup rejection. The inner muon endcap wheels were replaced by New Small Wheels with Micromegas and small-strip Thin Gap Chamber detectors, providing both precision tracking and Level-1 Muon trigger functionality. Trigger coverage of the inner barrel muon layer near one endcap region was augmented with modules integrating new thin-gap resistive plate chambers and smaller-diameter drift-tube chambers. Tile Calorimeter scintillation counters were added to improve electron energy resolution and background rejection. Upgrades to Minimum Bias Trigger Scintillators and Forward Detectors improve luminosity monitoring and enable total proton-proton cross section, diffractive physics, and heavy ion measurements. These upgrades are all compatible with operation in the much harsher environment anticipated after the High-Luminosity upgrade of the LHC and are the first steps towards preparing ATLAS for the High-Luminosity upgrade of the LHC. This paper describes the Run 3 configuration of the ATLAS detector.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Pushing the frontiers in climate modelling and analysis with machine learning

Climate modelling and analysis are facing new demands to enhance projections and climate information. Here, in this study, we argue that now is the time to push the frontiers of machine learning beyond state-of-the-art approaches, not only by developing machine-learning-based Earth system models with greater fidelity, but also by providing new capabilities through emulators for extreme event projections with large ensembles, enhanced detection and attribution methods for extreme events, and advanced climate model analysis and benchmarking. Utilizing this potential requires key machine learning challenges to be addressed, in particular generalization, uncertainty quantification, explainable artificial intelligence and causality. This interdisciplinary effort requires bringing together machine learning and climate scientists, while also leveraging the private sector, to accelerate progress towards actionable climate science.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

GridCoPilot for Thermal Events: An LLM-Based Platform for Power Grid Reliability Analysis

Large Language Models show promise for translating natural language into database queries, but deploying such systems in safety-critical domains requires high reliability. We present an application of GridCoPilot to thermal event analysis (heatwaves and coldwaves) that affect power grid reliability. Our approach uses a LangChain SQL Agent to translate natural language queries into auditable SQL statements, with deterministic visualization routines that parse the structured query results. We introduce structural framing as a design principle, we integrate a NERC-region-level event library with county-level meteorology and decompose the combined data into three relational tables (event metadata, county-level event details, and a county-to-NERC subregion mapping), using prompt-guided joins to direct the model toward correct multi-table queries. For two core analytical patterns (identifying worst events by region and by region-year), the system achieved 100% SQL accuracy across all 16 NERC subregions and both event types (64 queries total). These results validate the approach for target use cases, though performance on diverse natural language formulations requires further investigation. We discuss design trade-offs, failure modes including JSON output truncation, and pathways for extending this approach to other hazard domains.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Challenges and Opportunities for Rechargeable Aqueous Sn Metal Batteries

Rechargeable aqueous batteries based on metallic anodes hold tremendous potential of high energy density enabled by the combination of relatively low working potential and large capacity while retaining the intrinsic safety nature and economical value of aqueous systems; However, the realization of these promised advantages relies on the identification of an ideal metal anode chemistry with all these merits. In this review, the emerging Sn metal anode chemistry is examined as such an anode candidate in both acidic and alkaline media, where the inertness of Sn toward hydrogen evolution, flat low voltage profile, and low polarization make it a unique metal anode for aqueous batteries. From a panoramic viewpoint, the key challenges and detrimental issues of Sn metal batteries are discussed, including dead Sn formation, self‐discharge, and electrolyte degradation, as well as strategies for mitigating these issues by constructing robust Sn anodes. New design approaches for more durable and reliable Sn metal batteries are also discussed, with the aim of fully realizing the potential of Sn anode chemistry.

Sn metal↗
DOE OSTI2026

Ultra-faint Milky Way Satellites Discovered in Carina, Phoenix, and Telescopium with DELVE Data Release 3

We report the discovery of three Milky Way satellite candidates: Carina IV, Phoenix III, and DELVE 7, in the third data release of the DECam Local Volume Exploration survey (DELVE). The candidate systems were identified by cross-matching results from two independent search algorithms. All three are extremely faint systems composed of old, metal-poor stellar populations (τ ≳ 10 Gyr, [Fe/H] ≲−1.4). Carina IV (M V = −2.8; r 1/2 = 40 pc) and Phoenix III (M V = −1.2; r 1/2 = 19 pc) have half-light radii that are consistent with the known population of dwarf galaxies, while DELVE 7 (M V = 1.2; r 1/2 = 2 pc) is very compact and seems more likely to be a star cluster, though its nature remains ambiguous without spectroscopic follow-up. The Gaia proper motions of stars in Carina IV ($M_{\star} = 2250^{+1180}_{-830} M_⊙$) indicate that it is unlikely to be associated with the LMC, while DECam CaHK photometry confirms that its member stars are metal poor. Phoenix III ($M_{\star} = 520^{+660}_{-290} M_⊙$) is the faintest known satellite in the extreme outer stellar halo (D GC > 100 kpc), while DELVE 7 ($M_{\star} = 60^{+120}_{-40} M_⊙$) is the faintest known satellite with D GC > 20 kpc.

Tan, Chin Yi [Univ. of Chicago, IL (United States)↗
DOE OSTI2021

Observation of photon-induced W+W- production in pp collisions at s = 13 TeV using the ATLAS detector

This letter reports the observation of photon-induced production of W -boson pairs, γγ → W W . The analysis uses 139 fb -1 of LHC proton–proton collision data taken at s = 13 TeV TeV recorded by the ATLAS experiment during the years 2015–2018. The measurement is performed selecting one electron and one muon, corresponding to the decay of the diboson system as W W → e ± νμ ∓ ν final state. The background-only hypothesis is rejected with a significance of well above 5 standard deviations consistent with the expectation from Monte Carlo simulation. A cross section for the γγ → W W process of 3.13 ± 0.31(stat.)±0.28(syst.) fb is measured in a fiducial volume close to the acceptance of the detector, by requiring an electron and a muon of opposite signs with large dilepton transverse momentum and exactly zero additional charged particles. This is found to be in agreement with the Standard Model prediction.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Recent Developments in DFTB+, a Software Package for Efficient Atomistic Quantum Mechanical Simulations

DFTB+ is a flexible, open-source software package developed by its community, designed for fast and efficient atomistic quantum mechanical simulations. It employs various methods that approximate density functional theory (DFT), such as density functional-based tight binding (DFTB) and the extended tight binding (xTB) approach allowing simulations of large systems over extended time scales with reasonable accuracy, while being significantly faster than traditional ab initio methods. In recent years, several new extensions of the DFTB method have been developed and implemented in the DFTB+ program package in order to improve the accuracy and generality of the available simulation results. In this paper, we review those enhancements, show several use case examples and discuss the strengths and limitations of its features.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Connected and Learning Based Optimal Freight Management for Efficiency

The management of the future heterogenous fleet is a complex decision-making problem. The heterogenous fleet is emerging as decarbonization technologies are deployed by fleets toward lowering the freight operation emissions in Medium and Heavy-duty vehicles. Traditionally, in fleets characterized by a homogeneous Diesel Internal Combustion Engine (ICE) powertrain, the process of fleet planning and operational optimization unfolds sequentially without the necessity to account for powertrain and vehicle-specific characteristics during dispatch decisions. Fleets with trucks less than 5 years old tend to maintain stable vehicle efficiency with minimal operational reliability risks for fleet managers. However, the landscape changes with the incorporation of emerging powertrain technologies, which lack extensive operational data and service experiences. This includes technologies like hybrid, Electric, Fuel Cell, or alternative fuel ICE. Operational decisions for fleets featuring heterogeneous powertrain technologies and facing limited access to alternative fueling and charging stations become intricate, requiring careful consideration and optimization at each dispatch. The difference in efficiency characteristics of emerging technologies, their range limitations, and the restricted availability of charging/alternative fueling infrastructure, coupled with sensitivity to driving conditions (e.g., EV range reduction in low temperatures) and their impact on component aging (such as batteries), become pivotal factors influencing the reliable and efficient freight transportation. To make the path toward low emission freight transportation efficient and reliable, an AI-assisted fleet management software is developed in this project to help fleet managers in optimizing both adoption of emerging powertrain decarbonization, connected and automated technologies and also operating the fleet after such technologies are deployed as schematically. Freight transportation requirements are different depending on the cargos to be shipped, customer requirements and regions of operations. This further highlights the need for software and digital solutions to tailor deployment and operation of emerging powertrain, connectivity, and automation technologies toward the specific fleet operation requirements. The fleet management optimizer was also integrated with a model of the fleet to simulate the operation of the fleet over 1 year of the baseline fleet operation (250,000+ shipments) indicating the significance of day-to-day variations on emissions and energy consumption of a freight transportation fleet. The results demonstrate ≥20% improvement in freight efficiency in terms of WTW CO2 per ton-mile of cargo shipments while all fleet operation constraints are enforced, and the cost (CapEx and OpEx) is minimized.

33 ADVANCED PROPULSION SYSTEMS↗