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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 OSTI2026

A New Window into the Baryon Cycle at Cosmic Noon with Line Intensity Mapping: Forecasts for auto- and cross-correlations in [CII]-158$μ$m, HI 21 cm, CO$_{J+1\rightarrow J}$, and H$α$ galaxies

Across the peak of cosmic star formation at $z\sim1-2$, inflow, processing, and feedback drive rapid changes in the spatial distribution and chemical composition of baryons in galaxies and surrounding reservoirs; this baryon cycle can be tomographically mapped by line intensity mapping (LIM) of atomic hydrogen, ionized carbon, and carbon monoxide. We present a simulation-based forecasting framework for detecting auto- and cross-power spectra between spectroscopic surveys of four such tracers at $z\sim0.5-1.7$ mapping the same deep field - TIM, EoRSpec/FYST, MeerKAT, & Euclid. We forward-model 3-D distributions for these tracers from magnetohydrodynamic simulations, directly capturing the two-halo, one-halo, and shot statistics without relying on analytical decompositions. We further detail a signal-to-noise formalism, tailored to LIM surveys with highly anisotropic geometries and Fourier-space coverage. We demonstrate that galaxy cross-correlations will be the dominant discovery channel for current-generation surveys. These instruments will detect the auto-spectra for CO and HI 21 cm and the CO $\times$ 21 cm cross-spectrum at modest S/N $\sim 1-10$, while placing upper limits on the [CII]-158$μ$m signals. [CII], CO, and HI LIM will be $\sim3-30\times$ ($0.5-1.5$ dex) more sensitive to cross-correlation with the Euclid survey, however, than their respective auto-correlations, constraining all three models of line emission at high significance (S/N $\sim 10-40$) within this decade. Finally, we formulate a staged instrumental trajectory with planned or reasonable improvements, including the as-proposed SKA-Mid. We forecast advancing the per-$k$-mode sensitivities of each auto-, galaxy-line, and line-line spectrum by several orders of magnitude, enabling new percent- and sub-percent level constraints on cosmology and the redshift evolution of star formation and the baryon cycle.

Agrawal, Shubh [Pennsylvania U., Dept. Math.]↗
DOE OSTI2025

Cooperative Merging via Online Speed Replanning: A Model-Free Approach With Vehicle-to-Vehicle Communication Packet Drop Compensation

On-ramp merging is a critical bottleneck in freeway traffic flow, contributing to congestion, accidents, and excessive fuel consumption. Although traditional ramp metering provides macroscopic control, it lacks the granularity for optimizing an individual vehicle’s trajectory. Cooperative merging, enabled by connected and automated vehicles, can potentially enhance traffic efficiency, safety, and fuel economy. However, existing research often neglects the influence of heterogeneous vehicle dynamics, unreliable vehicle-to-vehicle (V2V) communication, and real-time implementation challenges. Here, this paper introduces novel model-free online speed planners for cooperative on-ramp merging. The planners address these limitations by being agnostic to vehicle dynamics, effectively compensating for V2V communication packet drops and incurring only a light computational burden. Comprehensive evaluation, conducted on a real-time traffic-vehicle-communication co-simulation platform integrating high-fidelity vehicle dynamics, a traffic simulator, and recorded V2V communication footprints, demonstrates the effectiveness of the proposed speed planners. Simulation results reveal that the proposed method yields accurate tracking of desired speed and inter-vehicle distance, maintaining low fuel consumption even under high packet drop ratios, and demonstrating real-time implementation efficiency.

Wang, Zejiang [Univ. of Texas at Dallas, Richardso↗
DOE OSTI2025

Learning plasma dynamics and robust rampdown trajectories with predict-first experiments at TCV

The rampdown phase of a tokamak pulse is difficult to simulate and often exacerbates multiple plasma instabilities. To reduce the risk of disrupting operations, we leverage advances in Scientific Machine Learning (SciML) to combine physics with data-driven models, developing a neural state-space model (NSSM) that predicts plasma dynamics during Tokamak à Configuration Variable (TCV) rampdowns. The NSSM efficiently learns dynamics from a modest dataset of 311 pulses with only five pulses in a reactor-relevant high-performance regime. The NSSM is parallelized across uncertainties, and reinforcement learning (RL) is applied to design trajectories that avoid instability limits. High-performance experiments at TCV show statistically significant improvements in relevant metrics. A predict-first experiment, increasing plasma current by 20% from baseline, demonstrates the NSSM’s ability to make small extrapolations. The developed approach paves the way for designing tokamak controls with robustness to considerable uncertainty and demonstrates the relevance of SciML for fusion experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Entanglement engineering of optomechanical systems by reinforcement learning

Entanglement is fundamental to quantum information science and technology, yet controlling and manipulating entanglement—so-called entanglement engineering—for arbitrary quantum systems remains a formidable challenge. There are two difficulties: the fragility of quantum entanglement and its experimental characterization. We develop a model-free deep reinforcement-learning (RL) approach to entanglement engineering, in which feedback control together with weak continuous measurement and partial state observation is exploited to generate and maintain desired entanglement. We employ quantum optomechanical systems with linear or nonlinear photon–phonon interactions to demonstrate the workings of our machine-learning-based entanglement engineering protocol. In particular, the RL agent sequentially interacts with one or multiple parallel quantum optomechanical environments, collects trajectories, and updates the policy to maximize the accumulated reward to create and stabilize quantum entanglement over an arbitrary amount of time. The machine-learning-based model-free control principle is applicable to the entanglement engineering of experimental quantum systems in general.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Hydrated Metal and Metal-Nitrate Complexes in Water: Full Lanthanide(III) Series plus Miscellaneous Metal Ions

This is a dataset of hydrated metal complexes and metal–nitrate hydrated complexes intended for public use, reproducibility, and downstream structural analysis. A key feature is coverage across the full lanthanide(III) series (La–Lu), enabling systematic comparisons of coordination motifs and bonding trends across the entire lanthanide sequence. In addition to the lanthanides, the dataset also includes other metal ions such as UO2(VI), Fe(II), and Fe(III). The dataset provides optimized geometries for hydrated and nitrate-containing hydrated complexes, together with representative ab initio molecular dynamics (AIMD) trajectories saved in standard XYZ formats. The accompanying NWChem input decks enable reproduction of the reported calculations and provide a starting point for extending the simulations to related coordination environments. Computationally, DFT calculations employ the B3LYP functional with DFT-D3BJ dispersion corrections and a COSMO continuum solvent model (dielectric constant 78.4) to represent solvation beyond the explicitly treated first hydration shell. AIMD simulations are performed with the NWChem qmd module at 298 K, integrating nuclear motion with the velocity-Verlet algorithm and controlling temperature using a Nosé–Hoover thermostat. Trajectories are approximately 4.8 ps in length and are used primarily to assess short-time stability of candidate coordination motifs, including (for lanthanides) differences between 8- versus 9-water coordination and comparisons between nitrate-bound and nitrate-free hydrated complexes.

Dinpajooh, Mohammadhasan [Pacific Northwest Nation↗
DOE OSTI2025

Electrification pathways for U.S. passenger vehicles

As electric vehicle (EV) adoption continues to accelerate, we explore the implications of different adoption trajectories that achieve a full transition to EVs by 2050 for U.S. light-duty passenger vehicles (LDVs). Using a vetted transportation system model, we find that achieving 100% EV sales by 2040 would decrease tailpipe greenhouse gases (GHGs) by 90% between 2022 and 2050, leaving about 45 million gasoline vehicles on the road. Achieving 100% sales by 2035, tailpipe emissions decrease 93%, with about 28 million gasoline vehicles on the road in 2050 (9% of stock). Slower EV adoption, reaching 100% sales by 2045, would result in 69 million gasoline vehicles on the road in 2050. Fully electrifying passenger vehicles by 2050 would require a full transition to EVs sales in the 2030s coupled with either changes to mobility, or an accelerated stock turnover in the 2040s with additional 19–30% annual LDV sales.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Inferring demographic and selective histories from population genomic data using a 2-step approach in species with coding-sparse genomes: an application to human data

Abstract The demographic history of a population, and the distribution of fitness effects (DFE) of newly arising mutations in functional genomic regions, are fundamental factors dictating both genetic variation and evolutionary trajectories. Although both demographic and DFE inference has been performed extensively in humans, these approaches have generally either been limited to simple demographic models involving a single population, or, where a complex population history has been inferred, without accounting for the potentially confounding effects of selection at linked sites. Taking advantage of the coding-sparse nature of the genome, we propose a 2-step approach in which coalescent simulations are first used to infer a complex multi-population demographic model, utilizing large non-functional regions that are likely free from the effects of background selection. We then use forward-in-time simulations to perform DFE inference in functional regions, conditional on the complex demography inferred and utilizing expected background selection effects in the estimation procedure. Throughout, recombination and mutation rate maps were used to account for the underlying empirical rate heterogeneity across the human genome. Importantly, within this framework it is possible to utilize and fit multiple aspects of the data, and this inference scheme represents a generalized approach for such large-scale inference in species with coding-sparse genomes.

Soni, Vivak (ORCID:0000000294969562)↗
DOE OSTI2025

Oakridge PPU Magnets: Results and Measurements

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) is being upgraded from 1.0 GeV to 1.3 GeV (or 1.4 to 2.8 MW). Several water-cooled magnets have been upgraded to transport 30% higher beam energy. Fermilab contributed the magnet design for the new chicane magnets and injection/extraction septum. Designing the magnets was a challenging task because the new magnets required good combined integrated field quality and needed to occupy the old magnets space but with about 20% greater integrated magnetic field. Additional strong requirements applied to the magnets fringe field so as not to disturb the circulating beam. After fabrication of the magnets, an extensive measurement campaign was developed and performed at Fermilab’s Magnet Test Facility. The measurements needed to assess magnet performance and provide comparison to design calculations. These included verification of field strength and harmonics along an 8 m length and 200 mm good field diameter for the chicane dipoles, end-field Hall probe mapping of these magnets, and measurements along two differently curved trajectories within the ∼3 m septum gradient magnet. Details of the measurements and systems are presented along with results and comparison to field models.

DiMarco, J. [Fermilab] (ORCID:0000000228400036)↗
DOE OSTI2026

A Real2Sim Digital Twin Pipeline for Photorealistic Robot Simulation: Evaluating VLA Policy Deployment on a Bimanual Mobile Robot

Digital twins that are automatically constructed from robot sensor data offer a promising pathway for scalable Real2Sim and Sim2Real transfer. However, it remains an open question whether photorealistic reconstruction alone is sufficient to support reliable deployment of vision-language-action (VLA) policies. We present a generative-AI-assisted Real2Sim pipeline that generates simulation-ready digital twins from real-world RGB observations with minimal manual intervention. The pipeline uses prompted segmentation to isolate scene components and a generative 3D model to directly produce simulation assets, eliminating the need for traditional multi-view reconstruction or manual 3D modeling.\r\nTo evaluate simulation fidelity, we deploy and compare policies from two VLA models in both the real robot and the reconstructed\r\nsimulation under identical tasks and initial conditions. We compare joint-level action trajectories and analyze how divergence evolves over time in closed-loop execution. Although the reconstructed environments are visually accurate, we observe increasing trajectory divergence during closedloop operation. These results indicate that photorealistic reconstruction alone is insufficient to preserve closed-loop control behavior\r\nin VLA policies, particularly in contact-rich manipulation settings where small perceptual errors compound over time.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

GRUMDN: A Multi-Task Model for Predicting Human Patterns-of-Life from Stay Transition Data

Understanding human patterns-of-life (PoL) is essential towards ensuring safe and secure indoor facility environment as well as outdoor urban environment. Prediction of human movement in between places of interest is vital in understanding human PoL. Movement between spaces maybe represented and detected in one of the two forms: 1) trajectories: locations measured at regular time intervals by mobile sensors, bluetooth or GPS sensors; or 2) stay transitions: semantic PoI (points of interest) and stay duration data measurable by eventbased sensors that collect data when a check-in or check-out event is detected. Stay transition data provides a more compressed data format compared to trajectories data, especially in situations with longer stay durations, while preserving the information necessary for PoL analysis. Now as introduced briefly in the paper, our deployed end application (Digital Twin of a facility with non-player characters, besides the interactive user in virtual reality) needed a well-performing and validated AI/ML model for simulating high quality stay transitions behavior. In this study we thus primarily present our findings with developing and validating that model, which is a multi-task neural network for stay transition prediction. The neural network consists of two heads, for corresponding two tasks of stay category prediction and stay duration prediction. We evaluated gated recurrent units and multi-layer perceptrons of varying network sizes for stay category prediction; while mixture density networks, noisy generator-only networks, and generative adversarial networks of varying network sizes for stay duration prediction. We have then evaluated four multi-task models, constructed by combining these specialized models, on their ability to predict stay transition data. We tested our models on datasets from two different cases: 1) a simulation-generated dataset of indoor movement within the HFIR (high flux isotope reactor) nuclear reactor facility at Oak Ridge National Laboratory (ORNL); and 2) the GeoLife human mobility dataset of outdoor urban movement available in literature. Our results indicate that GRUMDN, which combines gated recurrent units (GRU) for stay category prediction task, and mixture density networks (MDN) for stay duration prediction task, did overall outperform other multitask models and the current state-of-the-art.

Gunaratne, Chathika [ORNL] (ORCID:0000000225088745↗
DOE OSTI2024

The Impact of the 8–10 March 2012 Geomagnetic Storm on Inner Zone Protons as Measured by Van Allen Probes

The Relativistic Electron Proton Telescope (REPT) instrument on the Van Allen Probes observed a double-peaked inner zone proton population throughout the 7 year lifetime of the mission. M. Hudson et al. (2023) showed that a strong SEP event accompanied by a CME-shock in early March 2012 provided the Solar Energetic Proton (SEP) source for the higher L trapped proton population, which then diffused radially inward to be observed by REPT at L = 2. The study followed trajectories of SEP protons launched isotropically from a sphere at 7 Re for 2.5 hr in fields calculated by the LFM-RCM global MHD model, which includes electric fields needed to model the transport and trapping of the protons by the shock, and then a radial diffusion simulation was run for 2 years using the result from the test-particle simulation as the initial condition. The simulation result was compared with REPT measurement in November 2013 and showed reasonable agreement. However, the simulation overestimated the Phase Space Density by a factor of four due to lack of field line curvature scattering during the storm in the model. In this study, a test-particle simulation is performed for 2 days following the injection and trapping of protons in March 2012 using TS05 fields to simulate the field line curvature scattering of the trapped SEP due to the buildup of the ring current during the geomagnetic storm. The resulting sample distribution was then weighted using the flux at the end of the two-hour MHD-test particle simulation. A radial diffusion simulation is then run using the initial profile that included the loss effect, with improved comparison with REPT measurements after 2 years.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

A windowed mean trajectory approximation for condensed phase dynamics

We propose a trajectory-based quasi-classical method for approximating dynamics in condensed phase systems. Building upon the previously developed optimized mean trajectory approximation that has been used to compute linear and nonlinear spectra, we borrow some ideas from filtering trajectory methods to obtain a novel semiclassical method for the dynamical propagation of density matrices. This new approximation is tested rigorously against standard multistate electronic models, spin-boson models, and models of the Fenna–Matthews–Olson complex. For dissipative systems, the current method is significantly better or as good as many other semiclassical methods available, especially at low temperatures and for off-diagonal density matrix elements, whereas for scattering models, the current method bears similar limitations as mean-field propagation schemes. All results are tested against the numerically exact hierarchical equations of motion method. In conclusion, the new method shows excellent agreement across various parameter regimes with numerically exact results, highlighting the robustness and accuracy of our approach.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Nonresonant central exclusive production of charged-hadron pairs in proton-proton collisions at s = 13 TeV

The central exclusive production of charged-hadron pairs in p p collisions at a center-of-mass energy of 13 TeV is examined, based on data collected in a special high- β * run of the LHC. The nonresonant continuum processes are studied with the invariant mass of the centrally produced two-pion system in the resonance-free region, m π + π − < 0.7 or m π + π − > 1.8 GeV . Differential cross sections as functions of the azimuthal angle between the surviving protons, squared exchanged four-momenta, and m π + π − are measured in a wide region of scattered proton transverse momenta, between 0.2 and 0.8 GeV, and for pion rapidities | y | < 2 . A rich structure of interactions related to double-pomeron exchange is observed. A parabolic minimum in the distribution of the two-proton azimuthal angle is observed for the first time. It can be interpreted as an effect of additional pomeron exchanges between the protons from the interference between the bare and the rescattered amplitudes. After model tuning, various physical quantities are determined that are related to the pomeron cross section, proton-pomeron and meson-pomeron form factors, pomeron trajectory and intercept, and coefficients of diffractive eigenstates of the proton. © 2024 CERN, for the CMS and TOTEMs Collaboration 2024 CERN

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Toward a unified description of hadron scattering at all energies

The construction of general amplitudes satisfying symmetries and S -matrix constraints has been the primary tool in studying the spectrum of hadrons for over half a century. In this work, we present a new parametrization, which can fulfill many expectations of S -matrix and Regge theory and connects the essential physics of hadron scattering in the resonance region and in asymptotic limits. In this construction, dynamical information is entirely contained in Regge trajectories that generalize resonance poles in the complex energy plane to moving poles in the angular momentum plane. We highlight the salient features of the model, compare with existing literature on dispersive and dual amplitudes, and benchmark the formalism with an initial numerical application to the ρ and σ / f 0 ( 500 ) mesons in π π scattering. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems

Multiscale systems are ubiquitous in science and technology, but are notoriously challenging to simulate as short spatiotemporal scales must be appropriately linked to emergent bulk physics. When expensive high-dimensional dynamical systems are coarse-grained into low-dimensional models, the entropic loss of information leads to emergent physics which are dissipative, history-dependent, and stochastic. To machine learn coarse-grained dynamics from time-series observations of particle trajectories, we propose a framework using the metriplectic bracket formalism that preserves these properties by construction; most notably, the framework guarantees discrete notions of the first and second laws of thermodynamics, conservation of momentum, and a discrete fluctuation-dissipation balance crucial for capturing non-equilibrium statistics. We introduce the mathematical framework abstractly before specializing to a particle discretization. As labels are generally unavailable for entropic state variables, we introduce a novel self-supervised learning strategy to identify emergent structural variables. We validate the method on benchmark systems and demonstrate its utility on two challenging examples: (1) coarse-graining star polymers at challenging levels of coarse-graining while preserving non-equilibrium statistics, and (2) learning models from high-speed video of colloidal suspensions that capture coupling between local rearrangement events and emergent stochastic dynamics. We provide open-source implementations in both PyTorch and LAMMPS, enabling large-scale inference and extensibility to diverse particle-based systems.

Computational Engineering, Finance, and Science (c↗
DOE OSTI2025

Polariton spectra under the collective coupling regime. I. Efficient simulation of linear spectra and quantum dynamics

We outline two general theoretical techniques to simulate polariton quantum dynamics and optical spectra under the collective coupling regimes described by a Holstein–Tavis–Cummings (HTC) model Hamiltonian. The first one takes advantage of sparsity of the HTC Hamiltonian, which allows one to reduce the cost of acting polariton Hamiltonian onto a state vector to the linear order of the number of states, instead of the quadratic order. The second one is applying the well-known Chebyshev series expansion approach for quantum dynamics propagation and to simulate the polariton dynamics in the HTC system; this approach allows us to use a much larger time step for propagation and only requires a few recursive operations of the polariton Hamiltonian acting on state vectors. These two theoretical approaches are general and can be applied to any trajectory-based non-adiabatic quantum dynamics methods. We apply these two techniques with our previously developed Lindblad-partially linearized density matrix approach to simulate the linear absorption spectra of the HTC model system, with both inhomogeneous site energy disorders and dipolar orientational disorders. Our numerical results agree well with the previous analytic and numerical work.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Quantum simulations of nuclear resonances with variational methods

Background: The many-body nature of nuclear physics problems poses significant computational challenges. These challenges become even more pronounced when studying the resonance states of nuclear systems, which are governed by the non-Hermitian Hamiltonian. Quantum computing, particularly for quantum many-body systems, offers a promising alternative, especially within the constraints of current noisy intermediate-scale quantum (NISQ) devices. Purpose: This work aims to simulate nuclear resonances using quantum algorithms by developing a variational framework compatible with non-Hermitian Hamiltonians and implementing it fully on a quantum simulator. Methods: We employ the complex scaling technique to extract resonance positions classically and adapt it for quantum simulations using a two-step algorithm. First, we transform the non-Hermitian Hamiltonian into a Hermitian form by using the energy variance as a cost function within a variational framework. Second, we perform 𝜃-trajectory calculations to determine optimal resonance positions in the complex energy plane. To address resource constraints on NISQ devices, we utilize Gray code (GC) encoding to reduce qubit requirements. Results: We first validate our approach using a schematic potential model that mimics a nuclear potential, successfully reproducing known resonance energies with high fidelity. We then extend the method to a more realistic 𝛼−𝛼 nuclear potential and compute the 𝐷- and 𝐺-wave resonance energies with a basis size of 𝑁=16, using only four qubits. The quantum simulation results closely match the classical values, demonstrating the feasibility of our approach. Conclusions: This study demonstrates, for the first time, that the complete 𝜃-trajectory method can be implemented on a quantum computer without relying on any classical input beyond the Hamiltonian. The results establish a scalable and efficient quantum framework for simulating resonance phenomena in nuclear systems. This work represents a significant step toward quantum simulations of open quantum systems and lays the foundation for future investigations into resonance structures in nuclear, atomic, and molecular physics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

The derivation of CRSS in pure Ti and Ti-Al alloys

The work focuses on the determination of the critical resolved shear stress (CRSS) in titanium (Ti) and titanium-aluminum (Ti-Al) alloys, influenced by an array of factors such as non-symmetric fault energies and minimum energy paths, dislocation core-widths, short-range order (SRO) effects which alter the local atomic environment, and tension-compression (T-C) asymmetry affected by intermittent slip motion. To address these multifaceted complexities, an advanced theory has been developed, offering an in-depth understanding of the mechanisms underlying slip behavior. The active slip systems in these materials are basal, prismatic, and pyramidal planes, with the latter involving both $\langle$a$\rangle$ and $\langle$c + a$\rangle$ dislocations. Each slip system is characterized by distinct Wigner-Seitz cell configurations for misfit energy calculations, varying partial dislocation separation distances, and unique dislocation trajectories—all critical to precise CRSS calculations. The theoretical CRSS results were validated against a comprehensive range of experimental data, demonstrating a strong agreement and underscoring the model's efficacy.

Critical stress↗