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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Experimental investigation into coal wettability changes caused by reactions with scCO 2 -H 2 O

Geological CO 2 sequestration (GCS) can help mitigate global warming and enhance methane recovery from coal beds. However, few studies have linked the effects of CO 2 to surface chemistry changes controlling wetting behavior in deep coal beds. Contact angles (CAs) of CO 2 /N 2 -high volatile bituminous coal-water systems were measured under different temperatures and pressures. The surface chemistry and physical structure of coals were characterized to investigate changes in physicochemical properties and their relations with wettability after reactions. For N 2 treatment, the time-dependence of static and dynamic CAs were insignificant, ranging within 4°. For gaseous CO 2 treatment, the static CAs and the average advancing angles increased slightly. With supercritical (sc) CO 2 , both the static and dynamic CAs increased significantly, and θ adv changed to intermediate-wet (92°). Reactions with minerals exposed to scCO 2 resulted in greater surface roughness and heterogeneity, greater contact angle hysteresis and more surface sites occupied by scCO 2 rather than H 2 O. Increases in hydrophobic functional groups and decreases in hydrophilicity were shown by FTIR spectra, reflecting the shedding of polar oxygen-containing functional groups, reduction of hydrogen bonds, and increasing percentage of hydrocarbons. XRD patterns obtained following scCO 2 -treatment showed that crystallite growth and molecular polymerization were higher toward graphite-like. The calculated structural parameters of functional groups and crystallites both showed elevated coal rank. Changes in crystallite structure, notably higher carbon content and decreased negative surface charge, are unfavorable for water-wetting. Finally, this study contributes to understanding surface chemistry changes responsible for decreased wettability during CO 2 -enhanced coal bed methane recovery and GCS in coal reservoirs.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

AI-NERD: Elucidation of relaxation dynamics beyond equilibrium through AI-informed X-ray photon correlation spectroscopy

Abstract Understanding and interpreting dynamics of functional materials in situ is a grand challenge in physics and materials science due to the difficulty of experimentally probing materials at varied length and time scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited for characterizing materials dynamics over wide-ranging time scales. However, spatial and temporal heterogeneity in material behavior can make interpretation of experimental XPCS data difficult. In this work, we have developed an unsupervised deep learning (DL) framework for automated classification of relaxation dynamics from experimental data without requiring any prior physical knowledge of the system. We demonstrate how this method can be used to accelerate exploration of large datasets to identify samples of interest, and we apply this approach to directly correlate microscopic dynamics with macroscopic properties of a model system. Importantly, this DL framework is material and process agnostic, marking a concrete step towards autonomous materials discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A foundation model for atomistic materials chemistry

Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model-and its qualitative and at times quantitative accuracy-on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or "foundation" model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields.

Batatia, Ilyes↗
DOE OSTI2024

Modeling graphene sheet growth and dynamical matrix calculations using molecular dynamics

Molecular dynamics (MD) has been an incredibly useful tool to model physical processes that were synthesized experimentally but not fully understood. MD, through the use of semi-empirical inter-atomic potentials, has allowed understanding of different physical processes in materials science. Yet as well as providing useful insights into materials science, molecular dynamics has a wider range of usability. In this report, I will be detailing how MD can be used to study graphene formation from a carbon liquid which requires high temperatures and pressures. Beyond this, I will describe the usefulness of MD for understanding the physics for phonon transport quantum sensors. To do this, MD was employed to determine the dynamical matrix by treating atoms as coupled oscillators. An accurate understanding of the dynamical matrix of a system is required to calculate the non-equilibrium Green’s function used to describe the phonon transport within phonon wave-guides. I found that, across multiple pressures and temperatures, randomly placed carbon atoms will show evidence of pent-first formation with semi-empirical models. Density functional theory (DFT), on the other hand, was too computationally expensive to use for full scale MD simulations, but we have the possibility of training a machine learned interatomic potential to approximate DFT for carbon in the environments being studied for pent-first graphene sheet formation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Phase II Final Technical Report: Dynamic Gamma-ray Imaging for In Vivo Tracking of Microelement Transport Across Plant-Microbial Systems

The Department of Energy Office of Biological and Environmental Research (DOE BER) Mesoscale to Molecules Bioimaging Technology Program aims to develop new imaging and measurement technology to enable in situ and dynamic imaging across a range of spatial and temporal scales. Various imaging modalities are required to span the complete spatiotemporal landscape for bioenergy and environmental bioimaging needs. The high-resolution gamma-ray spectroscopy, imaging, and sensitivity of new high-purity germanium (HPGe) instruments provide a unique opportunity to complement and enhance these research goals. The HPGe-based Gamma-ray Imager for Plant Research (GIPR) developed here provides non-invasive, in vivo measurements to dynamically track the uptake and distribution of multiple gamma-emitting radioactive elements simultaneously as they move from the soil microbiome into living plants. The hand portable GIPR utilizes commercially available radioisotopes to provide spatial and temporal imaging of plant microelement exchange for the broader scientific community.

Kiser, Matthew↗
DOE OSTI2024

Cosmological probes of Dark Radiation from Neutrino Mixing

Abstract Models of stepped dark radiation have recently been found to have an important impact on the anisotropies of the cosmic microwave background, aiding in easing the Hubble tension. In this work, we study models with a sector of dark radiation with a step in its abundance, which thermalizes after big bang nucleosynthesis by mixing with the standard model neutrinos. For this, we extend an earlier work which has focused on the background evolution only until the dark sector thermalizes by deriving the full background and perturbation equations of the model and implementing them in an Einstein-Boltzmann solving code. We expound on the behavior of this model, discussing the wide range of parameters that result in interesting and viable cosmologies that dynamically generate dark radiation during a range of epochs. We find that for the strongly self-coupled regime, there is no large cosmological impact for a tight prior on the mass, whereas larger mass ranges allow a smooth interpolation between a behavior close to the ΛCDM cosmological standard model and close to an additional component of strongly self-interacting dark radiation. In the weakly self-coupled regime we find that we can accommodate a parameter space relevant for the neutrino anomalies as well as one relevant to easing the Hubble tension.

Astronomy & Astrophysics↗
DOE OSTI2026

Synaptic Functionality and Neuromorphic Information Processing in Membrane Ion Channel Junctions

The human brain performs complex memory and computational tasks with high energy efficiency by regulating ion transport through membrane channels. These signaling mechanisms have been inspiring the development of nanofluidic memristors that emulate synaptic behavior. Here, in this study, we describe a membrane ion channel synapse (MICS), constructed from aqueous droplets linked by gramicidin A channels, that achieves neuromorphic functionality. MICS exhibits memristive ion transport with hysteretic current–voltage behavior arising from voltage-dependent channel formation and ion transport dynamics. MICS emulates a range of synaptic behaviors including associative learning. We further demonstrate its application in reservoir computing by performing handwritten digit classification and tic-tac-toe game and explore the system parameters that improve the computational performance. This droplet-based biomimetic synapse offers a potentially scalable and energy-efficient platform for next-generation neuromorphic computing systems.

Droplet interface bilayer↗
DOE OSTI2026

Neutron Scattering in Sodium-Ion Battery Research: Progress and Prospects

Sodium-ion batteries have attracted renewed interest in recent years and are widely studied as a complementary power source to Li-ion batteries for large-scale stationary energy storage and small electric vehicles. Compared with Li-ion batteries, Na-ion batteries offer several advantages, including elemental abundance, lower cost, improved safety, and better low-temperature performance. An in-depth understanding of Na-ion batteries across multiple length and time scales has further accelerated the rapid development of this technology. Among the various advanced characterization techniques used to study Na-ion batteries, neutron scattering has gained significant traction in recent years. It has become a powerful and versatile tool for probing structure, morphology, and sodium-ion dynamics over a wide range of length and time scales. In this article, we will briefly review the development of neutron scattering technology and highlight recent advances in applying neutron-based techniques—including neutron diffraction, total scattering, small-angle scattering, quasi-elastic/inelastic scattering, and neutron imaging—to Na-ion battery materials. We also provide perspectives on future technique developments, particularly in the realm of in situ and operando neutron scattering characterization, and discuss how these approaches could further enhance our understanding of Na-ion battery systems.

Liu, Jue [ORNL] (ORCID:000000024453910X)↗
DOE OSTI2026

Dynamics of McMillan mappings III. Symmetric map with mixed nonlinearity

This article extends the study of the dynamical properties of the symmetric McMillan map, emphasizing its utility in understanding and modeling complex nonlinear systems. Although the map features six parameters, we demonstrate that only two are irreducible: the linearized rotation number at the fixed point and a nonlinear parameter representing the ratio of terms in the biquadratic invariant. Through a detailed analysis, we classify regimes of stable motion, provide exact solutions to the mapping equations, and derive a canonical set of action-angle variables, offering analytical expressions for the rotation number and nonlinear tune shift. We further establish connections between general standard-form mappings and the symmetric McMillan map, using the area-preserving Hénon map and accelerator lattices with thin sextupole magnet as representative case studies. Our results show that, despite being a second-order approximation, the symmetric McMillan map provides a highly accurate depiction of dynamics across a wide range of system parameters, demonstrating its practical relevance in both theoretical and applied contexts.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Cosmic Overture Echoed in a Stellar Final Act: Implications of Nanohertz Gravitational Waves for Core Collapse Supernova Neutrinos

The growing evidence for nanohertz gravitational waves, from NANOGrav and other observations, may be pointing to a cosmological first-order phase transition at temperatures of 𝒪⁢(10−100)⁢MeV. Such an interpretation requires beyond the Standard Model dynamics in this energy range. If so, it may well be the case that galactic core-collapse supernova explosions would produce the key components related to the first-order phase transition, leaving detectable imprints on the spectrum of neutrinos emitted in the initial few seconds of the collapse. This might provide further evidence in support of the early universe interpretation of the nanohertz gravitational wave signal. The scenario proposed here is also suggestive of a low-mass seesaw mechanism to explain neutrino masses. We outline the prospects for future observations of Galactic supernovae to uncover the signals of this scenario, with further confirmation from future pulsar timing array measurements of primordial nanohertz gravitational waves.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

phosaa14SB and phosaa19SB: Updated Amber Force Field Parameters for Phosphorylated Amino Acids

Phosphorylated amino acids are involved in many cell regulatory networks; proteins containing these post-translational modifications are widely studied both experimentally and computationally. Simulations are used to investigate a wide range of structural and dynamic properties of biomolecules, such as ligand binding, enzyme-reaction mechanisms, and protein folding. However, the development of force field parameters for the simulation of proteins containing phosphorylated amino acids using the Amber program has not kept pace with the development of parameters for standard amino acids, and it is challenging to model these modified amino acids with accuracy comparable to proteins containing only standard amino acids. In particular, the popular ff14SB and ff19SB models do not contain parameters for phosphorylated amino acids. Here, the dihedral parameters for the side chains of the most common phosphorylated amino acids are trained against reference data from QM calculations adopting the ff14SB approach, followed by validation against experimental data. Finally, library files and corresponding parameter files are provided, with versions that are compatible with both ff14SB and ff19SB.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Long-lived zone-boundary magnons in an antiferromagnet

Antiferromagnetic (AFM) insulators exhibit many desirable features for spintronic applications such as fast dynamics in the THz range and robustness to fluctuating external fields. However, large damping typically associated with THz magnons presents a serious challenge for THz magnonic applications. Here, we report long-lived short-wavelength zone boundary magnons in the honeycomb AFM insulator CoTiO 3 , recently found to host topological magnons. We find that its zone-boundary THz magnons exhibit longer lifetimes than its zone-center magnons. This unusual momentum-dependent long magnon lifetime originates from several factors including the antiferromagnetic order, exchange anisotropy, a finite magnon gap, and magnon band dispersion. Our work suggests that magnon-magnon interaction may not be detrimental to magnon lifetimes and should be included in future searches for topological magnons.

42 ENGINEERING↗
DOE OSTI2024

Dynamic mode decomposition of nonequilibrium electron-phonon dynamics: accelerating the first-principles real-time Boltzmann equation

Abstract Nonequilibrium dynamics governed by electron–phonon ( e -ph) interactions plays a key role in electronic devices and spectroscopies and is central to understanding electronic excitations in materials. The real-time Boltzmann transport equation (rt-BTE) with collision processes computed from first principles can describe the coupled dynamics of electrons and atomic vibrations (phonons). Yet, a bottleneck of these simulations is the calculation of e –ph scattering integrals on dense momentum grids at each time step. Here we show a data-driven approach based on dynamic mode decomposition (DMD) that can accelerate the time propagation of the rt-BTE and identify dominant electronic processes. We apply this approach to two case studies, high-field charge transport and ultrafast excited electron relaxation. In both cases, simulating only a short time window of ~10% of the dynamics suffices to predict the dynamics from initial excitation to steady state using DMD extrapolation. Analysis of the momentum-space modes extracted from DMD sheds light on the microscopic mechanisms governing electron relaxation to a steady state or equilibrium. The combination of accuracy and efficiency makes our DMD-based method a valuable tool for investigating ultrafast dynamics in a wide range of materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Physics-tailored machine learning reveals unexpected physics in dusty plasmas

Dusty plasma is a mixture of ions, electrons, and macroscopic charged particles that is commonly found in space and planetary environments. The particles interact through Coulomb forces mediated by the surrounding plasma, and as a result, the effective forces between particles can be nonconservative and nonreciprocal. Machine learning (ML) models are a promising route to learn these complex forces, yet their structure should match the underlying physical constraints to provide useful insight. Here, we demonstrate and experimentally validate an ML approach that incorporates physical intuition to infer force laws in a laboratory dusty plasma. Trained on 3D particle trajectories, the model accounts for inherent symmetries, nonidentical particles, and learns the effective nonreciprocal forces between particles with exquisite accuracy (R 2 > 0.99). We validate the model by inferring particle masses in two independent yet consistent ways. The model’s accuracy enables precise measurements of particle charge and screening length, identifying large deviations from common theoretical assumptions. Our ability to identify unknown physics from experimental data demonstrates how ML-powered approaches can guide new routes of scientific discovery in many-body systems. Furthermore, we anticipate our ML approach to be a starting point for inferring laws from dynamics in a wide range of many-body systems, from colloids to living organisms.

Science & Technology - Other Topics↗
DOE OSTI2024

Building Krylov complexity from circuit complexity

Krylov complexity has emerged as a probe of operator growth in a wide range of nonequilibrium quantum dynamics. However, a fundamental issue remains in such studies: the definition of the distance between basis states in Krylov space is ambiguous. Here we show that Krylov complexity can be rigorously established from circuit complexity when dynamical symmetries exist. Whereas circuit complexity characterizes the geodesic distance in a multidimensional operator space, Krylov complexity measures the height of the final operator in a particular direction. The geometric representation of circuit complexity thus unambiguously designates the distance between basis states in Krylov space. This geometric approach also applies to time-dependent Liouvillian superoperators, where a single Krylov complexity is no longer sufficient. Multiple Krylov complexity may be exploited jointly to fully describe operator dynamics. Published by the American Physical Society 2024

Lv, Chenwei (ORCID:0000000250952582)↗
DOE OSTI2025

Modeling 2p-2h Interactions in Neutrino-Nucleus Scattering: A Comparative Overview of the Valencia and SuSAv2-MEC Models

2p-2h interactions are crucial for describing ν-A scattering, especially in the "dip region" between the Quasi-Elastic (QE) peak and Δ-resonance production. They involve the ejection of two nucleons, leading to two holes in the nuclear ground state. The underlying nuclear dynamics involve short-range correlations (SRC), long-range correlations (Random Phase Approximation, RPA), and the interplay of one- and two-body currents. Several models attempt to describe 2p-2h interactions, including Microscopic Models (e.g., Valencia), Scaling-Based Models (e.g., SuSAv2-MEC), and Transport Models (e.g., GiBUU). Despite progress, discrepancies exist between models and with data. I Will give an overview of 2p2h from the perspective of two Models Valencia and SuSAv2-MEC.

Hassinin, Karim [Houston U.]↗
DOE OSTI2025

MEC modeling

2p-2h interactions are crucial for describing ν-Ar scattering, especially in the "dip region" between the Quasi-Elastic (QE) peak and Δ-resonance production. They involve the ejection of two nucleons, leading to two holes in the nuclear ground state. The underlying nuclear dynamics involve short-range correlations (SRC), long-range correlations (Random Phase Approximation, RPA), and the interplay of one- and two-body currents. Several models attempt to describe 2p-2h interactions like Valencia and SuSAv2-MEC. Despite progress, discrepancies exist between models and with data. I Will give an overview of 2p2h from the perspective of two Models Valencia and SuSAv2-MEC. I will introduce my current work to apply reweighting from SuSAv2 to Valencia on argon which will help improving the systematic uncertainty on MEC for SBN and DUNE.

Hassinin, Karim [Houston U.] (ORCID:00090009778353↗
DOE OSTI2024

Microstructure effects on high velocity microparticle impacts of copper

Constitutive models can fail to predict high-rate deformation behavior due to their inability to account for microstructural effects. In part, this is because of a dearth of experimental benchmarking data in the high strain-rate, low pressure regime, since many high-rate experiments also probe a region of strong shockwaves, at which point microstructure effects no longer play a primary role. This work uses laser-induced particle impact testing to quantitatively study high velocity impacts of small, rigid alumina microspheres on flat copper substrates with varying amounts of initial cold work in the weak shock regime, but at very high strain rates up to ~10 7 s –1 . Through paired experiments and numerical simulations, this work shows that the initial microstructure condition can have significant influence on dynamical mechanical properties in this range. Specifically, prior work hardening of the copper substrate leads to increased rebounding of the microparticles (i.e., less plastic dissipation in the impact) as well as smaller craters. Each of these experimental measurables can be converted into a strength measure, i.e., the dynamic yield strength or dynamic hardness, respectively, neither of which is well predicted consistently by existing constitutive laws. The general trend of hardening can be captured by such models by incorporating an existing “pre-strain,” suggesting that future calibration of the materials parameters may yield a good fit over a broader range of conditions. Our results emphasize the importance of reporting the microstructural condition in dynamic studies, as well as the necessity of accounting for these factors when formulating and optimizing constitutive models.

36 MATERIALS SCIENCE↗