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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

From Electronic Structure to Ion Transport: Photoelectron Spectroscopy and Molecular Dynamics Simulations Reveal the Role of Anions in Lithium Battery Electrolytes

Electrolyte anions are pivotal for lithium battery performance, yet their fundamental electronic structural properties are not well understood. In this work, we employ a combination of negative-ion photoelectron spectroscopy (NIPES), ab initio calculations, and molecular dynamics (MD) simulations to investigate the electronic structures of three representative electrolyte anions. This multiscale approach enables us to elucidate how their intrinsic electronic properties govern anion–solvent interactions in gas-phase clusters, as well as lithium-ion (Li + ) solvation structures and ion transport behavior in the condensed phase. NIPES reveals that difluoro(oxalato)borate (DFOB – ), bis(fluorosulfonyl)imide (FSI – ), and bis(oxalato)borate (BOB – ) all exhibit high electron binding energies, with vertical/adiabatic detachment energies increasing from DFOB – (6.09/5.70 eV) to FSI – (6.80/6.10 eV) to BOB – (6.82/6.40 eV), correlating with enhanced oxidation stability. Ab initio calculations reveal that DFOB – /FSI – –solvent complexes bind Li + ∼ 10 kcal/mol stronger than BOB – series, aligning with the strength of a Li + –anion model. DFOB – exhibits pronounced charge localization on both oxygen and fluorine atoms, enabling their involvement in Li + coordination. In contrast, fluorine atoms in FSI – are largely electron-depleted and remain excluded from direct Li + binding. MD simulations further demonstrate that LiDFOB and LiFSI systems exhibit Li + diffusion coefficients three and five times higher than those of LiBOB across four common solvents. Notably, LiFSI salt in acetonitrile (AN) exhibits the fastest Li + diffusion among 12 electrolyte systems, highlighting the synergistic effect of FSI – and AN in promoting ion mobility. In conclusion, these findings provide a molecular-level understanding of the critical roles of anion and its microsolvation in optimizing Li + diffusion dynamics, once again emphasizing the positioning of FSI – and DFOB – as prime candidates for next-generation electrolytes.

25 ENERGY STORAGE↗
DOE OSTI2025

Surrogate Modeling of Landau Damping with Deep Operator Networks

Kinetic simulations excel at capturing microscale plasma physics phenomena with high accuracy, but their computational demands make them impractical for modeling large-scale space and astrophysical systems. In this context, we build a surrogate model, using Deep Operator Networks (DeepONets), based upon the Vlasov–Poisson simulation data to model the dynamical evolution of plasmas, focusing on the Landau damping process—a fundamental kinetic phenomenon in space and astrophysical plasmas. The trained DeepONets are able to capture the evolution of electric field energy in both linear and nonlinear regimes under various conditions. Extensive validation highlights DeepONets’ robust performance in reproducing complex plasma behaviors with high accuracy, paving the way for large-scale modeling of space and astrophysical plasmas.

plasma astrophysics↗
DOE OSTI2024

Spatially and Temporally Detailed Water and Carbon Footprints of U.S. Electricity Generation and Use

Electricity generation in the United States entails significant water usage and greenhouse gas emissions. However, accurately estimating these impacts is complex due to the intricate nature of the electric grid and the dynamic electricity mix. Existing methods to estimate the environmental consequences of electricity use often generalize across large regions, neglecting spatial and temporal variations in water usage and emissions. Consequently, electric grid dynamics, such as temporal fluctuations in renewable energy resources, are often overlooked in efforts to mitigate environmental impacts. The U.S. Department of Energy (DOE) has initiated the development of resilient energyshed management systems, requiring detailed information on the local electricity mix and its environmental impacts. This study supports DOE's goal by incorporating geographic and temporal variations in the electricity mix of the local electric grid to better understand the environmental impacts of electricity end users. We offer hourly estimates of the U.S. electricity mix, detailing fuel types, water withdrawal intensity, and water consumption intensity for each grid balancing authority through our publicly accessible tool, the Water Integrated Mapping of Power and Carbon Tracker (Water IMPACT). While our primary focus is on evaluating water intensity factors, our dataset and programming scripts for historical and real-time analysis also include evaluations of carbon dioxide (equivalence) intensity within the same modeling framework. This integrated approach offers a comprehensive understanding of the environmental footprint associated with electricity generation and use, enabling informed decision-making to effectively reduce Scope 2 water usage and emissions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Ultrafast renormalization of the magnetic continuum in the proximal Kitaev quantum spin liquid H 3 LiIr 2 O 6 by tr-RIXS [Invited]

We present the first circularly polarized non-resonant pump time-resolved resonant inelastic X-ray scattering (tr-RIXS) experiment in H 3 LiIr 2 O 6 , an iridium-based Kitaev system. Our calculations and experimental results are consistent with the modification of the low-energy magnetic excitations in H 3 LiIr 2 O 6 only during illumination by the laser pulse. We discuss these results in a cooperative framework between the Floquet engineering of the exchange interactions and the dynamic renormalization of electron-electron correlations. However, the penetration length mismatch between the X-ray probe and laser pump and the intrinsic complexity of Kitaev magnets prevents us from unequivocally extracting towards which ground state H 3 LiIr 2 O 6 is driven. We outline possible solutions to these challenges for light-driven stabilization and observation of the Kitaev quantum spin liquid limit by RIXS.

Kim, Jungho [Argonne National Laboratory (ANL), Ar↗
DOE OSTI2026

Semi-Analytical Hierarchical Bayesian Inference of Nonlinear Model Structure in Stochastic Dynamics: Applied to Compartmental Models of Infectious Diseases

A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented. This framework enables the concurrent estimation of system states, time-varying parameters, time-invariant parameters, and the optimal sparsity structure of the model parameters. Because differential equation-based models are often simplified mechanistic or phenomenological representations, robust inference from noisy measurement data requires explicit treatment of model error and uncertainty. Model error and time-varying parameters can be represented as random processes, enabling inference while making minimal assumptions about the underlying sources of discrepancy and variability. Adopting stochastic differential equation representations affords the model significant flexibility, but can also render it susceptible to overfitting during statistical inversion, where the inferred model may track noise rather than the underlying signal. To alleviate the effects of overfitting and to enable the discovery of the optimal sparse representation of the time-invariant parameters, a Bayesian sparse learning algorithm is embedded within the framework. This sparse learning framework adopts an approximate hierarchical Bayesian setting defined by a series of semi-analytical expressions. The model structure inference framework is validated using a stochastic compartmental model for tracking and forecasting active cases of an infectious disease. Compartmental models describe population-level infectious disease dynamics through interactions among population fractions grouped by disease state. Mathematically, such models consist of a system of coupled ordinary differential equations. This example adopts an expressive compartmental model that includes multiple possible interactions between disease states, motivated by early uncertainty surrounding COVID-19 reinfection dynamics and their implications for long-term epidemic forecasting. The sparse learning exercise permits the inference of a priori unknown epidemiological dynamics from simulated public health data, discovering the nested compartmental model that optimizes the trade-off between average data-fit and model complexity. It is shown that inducing sparsity among the model parameters eliminates redundant interactions between compartments, equivalently revealing the optimal coupling structure between differential equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Evaluation of a high-resolution regional climate simulation for surface and hub-height wind climatology over North America

Assessing the availability of key wind resources requires augmenting observations to support the implementation of wind energy infrastructure. However, observations are limited, necessitating the development of high-resolution, long-term gridded datasets. This study presents a robust, dynamically downscaled climatological dataset, offering 20 years of hourly wind data at a 4 km spatial resolution across North America, and evaluates its performance against observations, including meteorological towers and automated surface-observing system (ASOS) stations, as well as coarse-resolution reanalysis data (the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5)). Results demonstrate that the downscaled high-resolution wind data outperform ERA5 in regions of complex terrain and coastal areas, with improved overlap coefficients for wind data distributions and reduced root mean square errors (RMSEs) for hub-height and near-surface diurnal wind patterns. The downscaled simulation also captures the synoptic drivers of seasonal wind direction patterns reasonably well, indicated by high wind rose similarity indices. This study also provides an analysis of interannual variability, utilizing the dataset's full 20-year period, and model uncertainty, generated by varying model initial conditions and physics parameterizations across 1-year ensemble members, which are key considerations for wind resource assessment in wind farm development.

17 WIND ENERGY↗
DOE OSTI2025

Nature-GL: A Revolutionary Learning Paradigm Unleashing Nature’s Power in Real-World Spatial-Temporal Graph Learning

Spatial-Temporal Graph Learning (ST-GL) is a prominent research area due to its unique capability to effectively learn real-world graphs. Applications of ST-GL pose stringent and various demands on not only real-time inference with low energy cost and high ac- curacy but also fast training. Unfortunately, as Moore’s Law approaches its limits and ST-GL model complexity drastically grows, the gap between digital hardware’s computational power and ST- GL application demands is widening. In response, this paper introduces Nature-GL, a nature-powered graph learning paradigm that exploits the principle of entropy increase to advance graph learning. In particular, Nature-GL transforms both the training and inference of real-valued ST-GL into electron-speed natural anneal- ing processes of a parameterized dynamical system that represents the target graphs. Experimental results across four real-world ap- plications with six datasets demonstrate that Nature-GL achieves orders-of-magnitude speedups in both training and inference, delivering higher accuracy compared to Graph Neural Networks.

Liu, Chuan [University of Rochester]↗
DOE OSTI2024

Physicochemical control of solvation and molecular assembly of charged amphiphilic oligomers at air-aqueous interfaces

Hypothesis : Understanding the rules that control the assembly of nanostructured soft materials at interfaces is central to many applications. We hypothesize that electrolytes can be used to alter the hydration shell of amphiphilic oligomers at the air-aqueous interface of Langmuir films, thereby providing a means to control the formation of emergent nanostructures. Experiments : Three representative salts – (NaF, NaCl, NaSCN) were studied for mediating the self-assembly of oligodimethylsiloxane methylimidazolium (ODMS-MIM + ) amphiphiles in Langmuir films. The effects of the different salts on the nanostructure assembly of these films were probed using vibrational sum frequency generation (SFG) spectroscopy and Langmuir trough techniques. Experimental data were supported by atomistic molecular dynamic simulations. Findings : Langmuir trough surface pressure – area isotherms suggested a surprising effect on oligomer assembly, whereby the presence of anions affects the stability of the interfacial layer irrespective of their surface propensities. In contrast, SFG results implied a strong anion effect that parallels the surface activity of anions. These seemingly contradictory trends are explained by anion driven tail dehydration resulting in increasingly heterogeneous systems with entangled ODMS tails and appreciable anion penetration into the complex interfacial layer comprised of headgroups, tails, and interfacial water molecules. These findings provide physical and chemical insight for tuning a wide range of interfacial assemblies.

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

Machine Learning-Accelerated First-Principles Molecular Dynamics Explains Anomalous Lattice Thermal Expansion in BaZr 0.78 Y 0.22 O 3-δ

Fuel cells are a vital clean energy technology that converts chemical energy directly into electricity with high efficiency, making them a cornerstone of a sustainable energy future. Herein we investigate the thermal and chemical lattice expansion behavior of hydrated BaZr 0.78 Y 0.22 O 3-δ using machine learning-accelerated ab initio molecular dynamics simulations. Here, our results reproduce the experimentally observed non-monotonic and anomalous temperature dependence of lattice expansion, which we attribute to the competing effects of thermal expansion and dehydration—two mechanisms that influence the lattice expansion in opposite directions. The importance of this work lies in its detailed demonstration of how advanced computational techniques can accurately capture complex environmental effects, providing a valuable framework for modeling similar phenomena in a variety of material systems and applications.

Proton conducting fuel cell↗
DOE OSTI2025

Solvation directed morphological control in metal oxide nanostructures

The development of structural hierarchy on various length scales during the crystallization process is ubiquitous in biological systems and minerals and is common in synthetic nanomaterials. The driving forces for the formation of complex architectures range from local interfacial interactions, that modify interfacial speciation, local supersaturation, and nucleation barriers, to macroscopic interparticle forces. Although it is enticing to interpret the formation of hierarchical architectures as the assembly of independently nucleated building blocks, crystallization pathways often follow monomer-by-monomer addition with structural complexity arising from interfacial chemical coupling and strongly correlated fluctuation dynamics in the electric double layers. Here, we show that the development of structural hierarchy through heterogeneous nucleation is driven by dipolar and solvation forces. Specifically, coupled simulations and experimental studies revealed that dipole build-up along the slow growth direction can trigger twinning and the development of branched architectures. Enthalpic solvation interactions were shown to either enhance or reduce the dipole moment of the nanoparticles and, thereby, control crystal morphology and architecture. The systematic studies of chemical coupling between different solvents and undercoordinated surface atoms of the growing nanocrystals revealed the mechanism of dimensionality control and the development of structural hierarchy without ligands or structure-directing agents.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

Data and scripts associated with a manuscript modeling microbial regulation of priming effects

This data package is associated with the publication “Modeling Microbial Regulatory Feedback in Organic Matter Decomposition Identifies Copiotrophic Traits as Key Drivers of Positive Priming” published as a preprint on BioRXiv by Ahamed et al. (2026); https://doi.org/10.1101/2024.08.11.607483. The package contains MATLAB scripts and saved simulation outputs used to implement a cybernetic model of microbial regulation during complex organic matter (OM) decomposition governing priming effects. It includes models of (i) single microbial functional groups (copiotrophic or oligotrophic degraders) and (ii) binary consortia composed of degraders and non-degraders with contrasting or common growth traits. Simulation results were generated using Monte Carlo analyses, with randomized key model parameters across a range of environmental mixing fractions of complex and labile OM. The dataset was created to provide a transparent and reusable computational framework for systematically exploring how microbial growth traits, metabolic regulation, and community composition influence OM decomposition dynamics and priming effects. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes the variable definitions. This package includes: (1) annotated MATLAB code implementing the system of ordinary differential equations and cybernetic control laws; (2) saved output files containing data (e.g., biomass, substrates, enzyme levels, priming metrics); and (3) scripts for processing saved outputs and regenerating figures. Specifically, the data package contains three main MATLAB scripts: runPrimingModel.m, runPlotData.m, and runPlotSuppFigS1.m, along with this readme and supporting documentation. Users should begin with runPrimingModel.m, which contains the annotated code implementing the system of ordinary differential equations and cybernetic control laws. This script runs the Monte Carlo simulations of microbial OM decomposition and allows users to modify microbial trait definitions, adjust parameter distributions, or define new community configurations. Simulation outputs are automatically saved as .mat files in the folder named SavedData, which stores all pre-generated results included in this package. The second script, runPlotData.m, reads files from the SavedData folder and processes them to regenerate the figures presented in the manuscript. The third script, runPlotSuppFigS1.m, specifically generates Figure S1 in the Supplementary Material of the manuscript. The package also includes the aforementioned files in non-proprietary .txt format. If users intend to use them, they should first save the files in their respective .m or .mat formats prior to execution in MATLAB.

Biomass concentration↗
DOE OSTI2026

Engineering Structural Transitions in a Multilevel Molecular Switch via Intermolecular Coupling

Controlling molecular conformations with atomic precision is essential for advancing molecular functional electronics, as well as our understanding of molecular dynamics. While switching between bistable molecular conformers is common in nature, creating systems with multiple, addressable states remains synthetically challenging. Here, we demonstrate a bottom-up strategy in which intermolecular interactions give rise to multilevel functionality within a simple two-molecule assembly. Using low-temperature scanning tunneling microscopy, we show that a pyrrolidine dimer on Cu(100) exhibits six distinct adsorption conformations, exceeding the four expected from two independent bistable units. This unusual complexity arises from the interplay between intermolecular van der Waals attraction and steric repulsion, which reshapes the potential energy landscape and changes a single high-energy transition into a sequential two-step pathway. Each step is driven by low-energy inelastic electron excitations, achieving a switching efficiency an order of magnitude higher than that of the monomer. Here, by tuning the bias voltage and tip–molecule distance, we achieve deterministic control over multiple stable states, establishing a general design principle for on-demand engineering of collective molecular behavior and energy-efficient multilevel molecular devices.

Molecular interactions↗
DOE OSTI2025

Two-dimensional ring polymer molecular dynamics determination of the MnO + + H 2 /D 2 reaction rates on a Δ-machine learned potential energy surface

In this work, we investigate the impact of nuclear quantum effects in the kinetics of the MnO + + H 2 reaction, a prototypical system for gas-phase H 2 activation by transition metal oxide ions. The DFT based potential energy surfaces (PESs) for the lowest-lying quintet and septet spin states reported in our previous work are improved by 2953 newly calculated CCSD(T)/AVDZ points using a delta-machine learning (Δ-ML) method. To examine nuclear quantum effects, the rate coefficients are computed using ring-polymer molecular dynamics (RPMD). Due to the mechanistic complexity of the reaction, two reaction coordinates are necessary to map out the free-energy surface and an extended RPMD rate theory is developed. The calculated RPMD rate coefficients on the new PES are in better agreement with experimental data. The calculated kinetic isotope effects (KIEs) range from 1.6 to 1.8, also in good agreement with the experimental values. Additionally, the converged RPMD rate coefficients are 2.3 to 3.1 times higher than their classical counterparts across the studied temperature range, suggesting the presence of moderate nuclear quantum effects in this reaction.

Liu, Yang [University of New Mexico, Albuquerque, ↗
DOE OSTI2024

Comparison of Full-Field and Integrated CFD Convergence Based on Richardson Extrapolation

This work investigated the usefulness of Richardson extrapolation--based discretization error estimates across all points in a solution field to produce a spatial convergence field for a computational fluid dynamics (CFD) simulation. The presented work used previously developed methods for Richardson extrapolation to compute the convergence orders of a CFD simulation at all points of the base (coarsest) mesh solution. Three test cases of increasing complexity were considered: Poiseuille flow, incompressible flow around a sharp corner, and transonic flow over an RAE 2822 airfoil. These test cases highlighted the potential of the proposed method to identify error sources and their relation to the model system-response-quantity convergence orders. However, these test cases also revealed the immaturity of the proposed method stemming from the unreliability of computing observed convergence orders at single points. Nonetheless, the test cases highlighted that the observed convergence orders allow for a more accurate diagnosis of constructive and destructive error transport than mesh pair error estimates. In the long run, the proposed method can be a tool for developing efficient and advanced error management strategies like adaptive mesh refinement.

Weinmeister, Justin↗
DOE OSTI2024

Accretion onto a Supermassive Black Hole Binary before Merger

While supermassive binary black holes (SMBBHs) inspiral toward merger they may also accrete matter from a surrounding disk. To study the dynamics of this system requires simultaneously describing the evolving spacetime and the magnetized plasma. We present the first relativistic calculation simulating two equal-mass, nonspinning black holes as they inspiral from a 20 M (G = c = 1) initial separation almost to merger. Our results imply important observational consequences: for instance, the accretion rate $\dot{M}$ onto the black holes first decreases and then plateaus, dropping by only a factor of ~3 despite the rapid inspiral. An estimated bolometric light curve follows the same profile, suggesting some merging SMBBHs may be significantly luminous past the predicted circumbinary disk decoupling. The minidisks are nonstandard: Reynolds, not Maxwell, stresses dominate, and they oscillate between two states. In one part of the cycle, "sloshing" streams transfer mass between minidisks, carrying kinetic energy at a rate sometimes as high as the peak minidisk bolometric luminosity. We also discover that episodic accretion drives time-varying minidisk tilts. These complex dynamics all contribute to unique cyclical behavior in the light curves of late-time inspiraling SMBBHs. The poloidal magnetic flux on the black holes is roughly constant at a dimensionless level $\phi$ ~ 2–3, but doubles just before merger; for significant black hole spin, this flux predicts powerful jets with variability driven by binary dynamics, another potentially unique electromagnetic signature. This simulation is the first to employ our multipatch infrastructure PATCHWORK MHD, decreasing the computational expense to ~3% of conventional single-grid methods' cost.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Influence of Grafting Density and the Ionic Environment on the Structure of Zwitterionic Brushes

Zwitterionic polymer brushes possess a high potential for applications as surface coatings, e.g., in antifouling applications. Their complex association behavior due to the coexistence of oppositely charged groups on the same monomeric unit allows for a broad variation of polarity, hydrophilicity, and the antipolyelectrolyte effect with the variation of the surrounding environment. In this study, planar polysulfobetaine brushes were investigated with neutron reflectometry (NR) and neutron spin–echo spectroscopy under grazing incidence (GINSES) to explore the brush structure and its inner dynamics perpendicular to the substrate. In particular, the effects of the substrate–initiator system and ionic strength on the structure were investigated with neutron reflectometry and a swelling by a factor of 3–5 was found in the presence of aqueous NaCl and MgCl 2 solutions compared to the dry state. During the data analysis, the applicability of a model-free evaluation was also demonstrated for the investigated polysulfobetaine brushes. Furthermore, it was found with GINSES that in contrast to neutral brushes, these polysulfobetaine brushes do not show the typical concentration fluctuations in the nanosecond time range when partially swollen with salt-free water.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Pressure-induced structural phase transitions in CrSBr

There is growing interest in combining chemical complexity with external stimuli like pressure, field, and light for property control in van der Waals solids. This is because extreme conditions trigger the development of new states of matter and functionality. In this work, we bring together synchrotron-based infrared absorption, Raman scattering, and diamond anvil cell techniques with first-principles calculations of the lattice dynamics and energy landscape to reveal the series of structural phase transitions in CrSBr. By tracking how the phonons change under pressure, we uncover a remarkable chain of complex symmetry modifications, interlayer interactions, and chemical reactions. A group-subgroup analysis suggests that CrSBr undergoes an orthorhombic Pmmn → monoclinic P2/m transition at 7.6 GPa, and based upon a comparison with model oxychlorides like FeOCl and CrOCl, we propose that changes in the pendant halide groups drive the system to a P2 1 /m-like space group above 15.3 GPa. Compression above 20.2 GPa is irreversible, resulting in the formation of an entirely new compound that is metastable for months. This work opens the door to the use of pressure and possibly strain to control the properties of CrSBr.

Microscopy↗
DOE OSTI2025

Weighted active space protocol for multireference machine-learned potentials

Multireference methods such as multiconfiguration pair-density functional theory accurately capture electronic correlation in systems with strong multiconfigurational character, but their cost precludes direct use in molecular dynamics. Combining these methods with machine-learned interatomic potentials (MLPs) can extend their reach. However, the sensitivity of multireference calculations to the choice of the active space complicates the consistent evaluation of energies and gradients across structurally diverse nuclear configurations. To overcome this limitation, we introduce the weighted active space protocol (WASP), a systematic approach to assign a consistent active space for a given system across uncorrelated configurations. By integrating WASP with MLPs and enhanced sampling techniques, we propose a data-efficient active learning cycle that enables the training of an MLP on multireference data. We demonstrated the approach on the TiC + -catalyzed C–H activation of methane, a reaction that poses challenges for Kohn–Sham density functional theory due to its significant multireference character. This framework enables accurate and efficient modeling of catalytic dynamics, establishing a paradigm for simulating complex reactive processes beyond the limits of conventional electronic-structure methods.

enhanced sampling↗