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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Resummation of QED radiative corrections in a strong constant crossed field

By considering radiative corrections of up to 3rd-loop order, Ritus and Narozhny conjectured that the proper expansion parameter for QED in a strong constant crossed field is $g=\alpha\chi^{2/3}$, where the dynamical quantum parameter $\chi=e\sqrt{-(Fp)^2}/m^3$ combines the particle momentum $p$ with the external field strength tensor $F$. Here we present and discuss the first non-perturbative result in this context, the resummed bubble-type polarization corrections to the electron self-energy in a constant crossed field. Our analysis confirms the relevance of the scaling parameter $g$ to the enhancement of bubble-type radiative corrections. This parameter actually represents the characteristic value of the ratio of the 1-loop polarization bubble to the photon virtuality. After an all-order resummation we identify and discuss two contributions to the self-energy with different formation regions and asymptotic behavior for $g\gg1$. Whereas the breakdown of perturbation theory occurs already for $g\gtrsim1$, the leading-order result remains dominant until the asymptotic regime $g\gg 1$ is reached. However, the latter is specific to processes like elastic scattering or photon emission and does not have to remain true for general higher-order QED processes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Mesoscale Effects of Composition and Calendering in Lithium-Ion Battery Composite Electrodes

Macrohomogeneous battery models are widely used to predict battery performance, necessarily relying on effective electrode properties, such as specific surface area, tortuosity, and electrical conductivity. While these properties are typically estimated using ideal effective medium theories, in practice they exhibit highly non-ideal behaviors arising from their complex mesostructures. In this paper, we computationally reconstruct electrodes from X-ray computed tomography of 16 nickel–manganese–cobalt-oxide electrodes, manufactured using various material recipes and calendering pressures. Due to imaging limitations, a synthetic conductive binder domain (CBD) consisting of binder and conductive carbon is added to the reconstructions using a binder bridge algorithm. Reconstructed particle surface areas are significantly smaller than standard approximations predicted, as the majority of the particle surface area is covered by CBD, affecting electrochemical reaction availability. Finite element effective property simulations are performed on 320 large electrode subdomains to analyze trends and heterogeneity across the electrodes. Significant anisotropy of up to 27% in tortuosity and 47% in effective conductivity is observed. Electrical conductivity increases up to 7.5× with particle lithiation. We compare the results to traditional Bruggeman approximations and offer improved alternatives for use in cell-scale modeling, with Bruggeman exponents ranging from 1.62 to 1.72 rather than the theoretical value of 1.5. We also conclude that the CBD phase alone, rather than the entire solid phase, should be used to estimate effective electronic conductivity. This study provides insight into mesoscale transport phenomena and results in improved effective property approximations founded on realistic, image-based morphologies.

25 ENERGY STORAGE↗
DOE OSTI2025

Regge limit of one-loop string amplitudes

We study the high-energy limit of 2 → 2 one-loop string amplitudes at fixed momentum transfer. For the closed string, the high-energy behavior of the amplitudes can be determined from Regge theory just like in field theory, as was first discussed by Amati, Ciafaloni and Veneziano. However, field theory intuition partially breaks down for the open-string amplitude, where amplitudes can exhibit surprising asymptotics in the high-energy limit depending on the topology of the diagram. We call this phenomenon Regge attenuation. We extract Regge limits by a combination of unitarity cuts and saddle-point analysis. We show that the leading contribution of the planar open-string amplitude is sufficiently simple that we can extract it at any loop order. This allows us to resum the genus expansion in a certain limit and demonstrate that the leading Regge trajectory remains linear in that limit.

Banerjee, Pinaki [ICTP South American Institute fo↗
DOE OSTI2023

A Unification Framework for Euclidean and Hyperbolic Graph Neural Networks

Hyperbolic neural networks have recently gained significant attention due to their promising results on several graph problems including node classification and link prediction. The primary reason for this success is the effectiveness of hyperbolic space in capturing the inherent hierarchy of graph datasets. However, they are limited in terms of generalization, scalability, and have inferior performance when applied to non-hierarchical datasets. In this paper, we take a completely different perspective for modeling hyperbolic networks and answer the following question: is an Euclidean model able to approximate a function or behavior in the hyperbolic space? Extending the universal approximation theory developed for Euclidean models, We draw an analogy from the hyperbolic components to the Euclidean counterparts and conclude that, in order to capture hierarchical features, it is possible to generalize hyperbolic models to be a special case of Euclidean models with the proposed Pseudo-Poincaré technique. We applied our non-linear hyperbolic normalization to the current state-of-the-art homogeneous and multi-relational graph networks and demonstrate significant improvements in performance compared to both Euclidean and hyperbolic counterparts. The primary impact of this work lies in its ability to capture hierarchical features in the Euclidean space, and thus, can replace hyperbolic networks without any loss in performance metrics while simultaneously leveraging the power of Euclidean networks such as interpretability and efficient execution of various model components.

Khatir, Mehrdad↗
DOE OSTI2021

The Heating and Pulsations of V386 Serpentis after Its 2019 Dwarf Nova Outburst

Following the pulsation spectrum of a white dwarf through the heating and cooling involved in a dwarf nova outburst cycle provides a unique view of the changes to convective driving that take place on timescales of months versus millennia for nonaccreting white dwarfs. In 2019 January the dwarf nova V386 Ser (one of a small number containing an accreting, pulsating white dwarf) underwent a large-amplitude outburst. Hubble Space Telescope ultraviolet spectra were obtained 7 and 13 months after outburst along with optical ground-based photometry during this interval and high-speed photometry at 5.5 and 17 months after outburst. The resulting spectral and pulsational analysis shows a cooling of the white dwarf from 21,020 to 18,750 K (with a gravity $\mathrm{log}(g)=8.1$) between the two UV observations, along with the presence of strong pulsations evident in both UV and optical at a much shorter period after outburst than at quiescence. The pulsation periods consistently lengthened during the year following outburst, in agreement with pulsation theory. However, it remains to be seen if the behavior at longer times past outburst will mimic the unusual nonmonotonic cooling and long periods evident in the similar system GW Lib.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

On the Stochastic Stability of Deep Markov Models

Deep Markov models (DMM) are generative models which are scalable and expressive generalization of Markov models for representation, learning, and inference problems. However, the fundamental stochastic stability guarantees of such models have not been thoroughly investigated. In this paper, we present a novel stability analysis method and provide sufficient conditions of DMM's stochastic stability. The proposed stability analysis is based on the contraction of probabilistic maps modeled by deep neural networks. We make connections between the spectral properties of neural network's weights and different types of used activation function on the stability and overall dynamic behavior of DMMs with Gaussian distributions. Based on the theory, we propose a few practical methods for designing constrained DMMs with guaranteed stability. We empirically substantiate our theoretical results via intuitive numerical experiments using the proposed stability constraints.

Drgona, Jan↗
DOE OSTI2020

Wettability of ultra-small pores of carbon electrodes by size-asymmetric ionic fluids

Recently, we studied the phase behavior of ionic fluids under confinement using the classical density functional theory within the framework of the restricted primitive model. Additionally, the theoretical results indicate that narrowing the pore size may lead to a drastic reduction in the electric double layer capacitance, while increasing the surface electrical potential would improve the ionic accessibility of micropores. In this work, we extend the theoretical investigation to systems containing size-asymmetric electrolytes that may exhibit a vapor-liquid like phase transition in the bulk phase. The effects of pore size and surface electric potential on the phase diagram and microscopic structures of the confined electrolytes were studied over a broad range of parameters. We found that decreasing the pore size or increasing the surface potential could destabilize the liquid phase in micropores, and capillary evaporation could occur regardless of the size asymmetry between cations and anions. Compared to that in a symmetric ionic system, the vapor-liquid phase separation is more likely to take place as the size asymmetry becomes more pronounced. The phase transition would alter the “accessibility” of ions to micropores and lead to coexisting micropores with different surface charge densities as identified by Monte Carlo simulation.

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

Operator origin of anomalous dimensions in de Sitter space

The late-time limit of the power spectrum for heavy (principal series) fields in de Sitter (dS) space yields a series of polynomial terms with complex scaling dimensions. Such scaling behavior is expected to result from an associated operator with a complex dimension. In a free theory, these complex dimensions are known to match the constraints imposed by unitarity on the space of states. Yet, perturbative corrections to the scaling behavior of operators are naively inconsistent with unitary evolution of the quantum fields in dS space. This paper demonstrates how to compute one-loop corrections to the scaling dimensions that appear in the two-point function from the field theory description in terms of local operators. We first show how to evaluate these anomalous dimensions using Mellin space, which has the feature that it naturally accommodates a scaleless regulator. We then explore the consequences for the soft de Sitter effective theory (SdSET) description that emerges in the long wavelength limit. Carefully matching between the UV and SdSET descriptions requires the introduction of novel nondynamical “operators” in the effective theory. This is not only necessary to reproduce results extracted from the Källén-Lehmann representation (that use the space of unitary states directly), but it is also required by general arguments that invoke positivity. Published by the American Physical Society 2025

Cohen, Timothy↗
DOE OSTI2020

Development of neural network force fields for corrosion studies

To fully understand the chemistry and physics of corrosion, novel methods of simulation must be developed. One approach is designing machine learning (ML) algorithms integrated with density functional theory to develop adaptive force fields to gain insight into corrosion behavior namely at the surface of metal oxides. Current methods of modeling corrosion are slow due to the computational cost of resolving both reaction mechanics and mass transport processes. Machine learning methods can be implemented to obtain structure-activity relationships at both the molecular and bulk scale while still retaining the accuracy of density functional theory (DFT) and significantly decreasing the time needed for simulations of complex chemical processes in the various environments of corrosion. Multiscale models are needed for corrosion studies to fully understand its processes not only at the atomic length scale (chemical bonding, energies, and forces), but also at the nano and meso length scales (solid-state physics and material science processes). Current methods of study include DFT, molecular dynamics, and Monte Carlo. The limitation of DFT is that only a small number of atoms or molecules can be simulated at that level of theory. Density functional theory is used to study the electronic structure of atoms and molecules, and calculate the force component of each atom. However, these calculations are limited to about 1000 atoms. Custom periodic boundary conditions (PBC) can be used to describe the various environments and defects that affect the atomic forces to produce a large data set from which a training set can be derived. Machine learning can be utilized to overcome the barrier of modeling macroscopic and multi-scale processes from ab initio calculations through the development of adaptive force fields. Local environments determine the atomic forces of a given system, therefore adaptive force fields must be created to produce reliable quantum mechanical calculations. This can be achieved by developing a learning algorithm that uses the mapped atomic forces or fingerprint as an input to produce energies and magnetic moments as output. A systematic approach was used to begin to build a data set in order to accurately describe the atomic forces in various environments. In Figure 4 below, a simple PBC cell of Fe{sub 2}O{sub 3} was first optimized. A surface optimization was performed next, followed by a hydroxylated surface optimization. Once this calculation has converged, the adsorption of halide species to the hydroxylated surface will be investigated. TensorFlow is an open source platform for machine learning developed by Google. Using a high level application program interface (API) such as Keras allows for building and training ML models easily in a number of different environments and languages. For this project, a neural network was developed within Anaconda in Python. Future Work: Further development of reference data set; Refining neural network and learning algorithm; Fingerprinting atomic environment to enable mapping of atomic force components; Choosing appropriate training set from reference data; Learning from training set and enabling non-linear mapping of training set fingerprints and the atomic forces; Estimation of uncertainty to identify ranges of outside applicability; Testing and analysis of molecular dynamic simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Quantum embedding for material chemistry based on domain separation and open subsystems

Our perspective considers two theories we recently proposed to perform quantum embedding calculations for chemical systems: domain-separated density functional theory (DSDFT) and locally coupled open subsystems (LCOS). The development includes both the fundamentals of each theory as well as potential applications, some technical aspects, and related challenges. DS-DFT is suited to study intramolecular effects, where one can apply a high level of theory (based on DFT or wave function theory) to a region of interest inside a molecule or solid, and lower level theory elsewhere, with smooth switching between the regions. LCOS, in contrast, is a fragment-based embedding which offers computational advantages to study intermolecular behavior such as electron hopping, spin-environment interaction, and charge transfer excitations. Yet, both theories can exchange roles when appropriate. In addition, these theories allow for control of computational scaling of their algorithms. We investigate paths to determine the charge-transfer operator used in LCOS, and suggest an auxiliary energy minimization that can provide a practical estimate to this operator. We also briefly discuss how to implement density fitting techniques in domain separation, and how domain separation can be used for pure wavefunction-based embedding.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Interlayer spacing in pillared and grafted MCM-22 type silicas: density functional theory analysis versus experiment

Pillaring of synthetic layered crystalline silicates and aluminosilicates provides a strategy to enhance their adsorption and separation performance, and can facilitate the understanding of such behavior in more complex natural clays. In this study, we perform the first-principles density functional theory calculations for the pillaring of the pure silica polymorph of an MCM-22 type molecular sieve. Starting with a precursor material MCM-22P with fully hydroxylated layers, a pillaring agent, (EtO) 3 SiR, can react with hydroxyl groups (–OH) on adjacent internal surfaces, 2(–OH) + (EtO) 3 SiR + H 2 O → (–O) 2 SiOHR + 3EtOH, to form a pillar bridging these surfaces, or with a single hydroxyl, –OH + (EtO) 3 SiR + 2H 2 O → (–O)Si(OH) 2 R + 3EtOH, grafting to one surface. For computational efficiency, we replace the experimental organic ligand, R, by a methyl group. We find that the interlayer spacing in MCM-22 is reduced by 2.66 Å relative to weakly bound layers in the precursor MCM-22P. Including (–O) 2 SiR bridges for 50% (100%) of the hydroxyl sites in MCM-22P increases the interlayer spacing relative to MCM-22 by 2.52 Å (2.46 Å). For comparison, we also analyze the system where all –OH groups in MCM-22P are replaced by non-bridging grafted (–O)Si(OH) 2 R which results in a smaller interlayer spacing expansion of 2.17 Å relative to MCM-22. Our results for the interlayer spacing in the pillared materials are compatible with experimental observations for a similar MCM-22 type material with low Al content (Si : Al = 51 : 1) of an expansion relative to MCM-22 of roughly 2.8 Å and 2.5 Å from our x-ray diffraction and scanning transmission electron microscopy analyses, respectively. The latter analysis reveals significant variation in individual layer spacings.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Low-field magnetic anomalies in single crystals of the A-type square-lattice antiferromagnet EuGa 4

The body-centered-tetragonal antiferromagnet EuGa 4 was recently identified as a Weyl nodal-line semimetal that exhibits the topological Hall effect below its reported antiferromagnetic (AFM) ordering temperature T N = 15 – 16.5 K which we find to be T N = 16.4 ( 2 ) K. The Eu +2 ions are located at the corners and body centers of the unit cells. EuGa 4 exhibits A-type antiferromagnetic order below T N , where the Eu 2+ spin-7/2 moments are ferromagnetically aligned in the ab plane with the Eu moments in adjacent Eu planes along the c axis aligned antiferromagnetically. Low-field magnetization versus field M(H ab ) data at T = 2 K with the field aligned in the ab plane are reported that exhibit anomalous positive curvature up to a critical field H c1 at which a second-order transition occurs with H c1 ≈ 0.85 kOe for H∥ [1,1,0] and ≈ 4.8 kOe for H∥[1,0,0]. For larger fields, the linear behavior M ab = χ(T N ) H a b is followed until the critical field H c is reached at which all moments become aligned with the applied field. A theory is formulated for T = 0 K that fits the observed M(H ab ) behavior at T = 2 K well, where domains of A -type AFM order with fourfold rotational symmetry occur in the AFM state in zero field. The moments in the four domains reorient to become almost perpendicular to H ab at H c1 , followed by increasing canting of all moments toward the field with increasing field up to H c which is reported to be 71 kOe. A first-order transition in M(H ab ) at H ab = H c1 is predicted by the theory for T = 0 K when H ab is at a small angle from the [1,0,0] direction.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Quantum dynamics of cosmological particle production: interacting quantum field theories with matrix product states

Understanding real-time dynamics of interacting quantum fields in curved space-time remains a major theoretical challenge. We employ tensor network methods to study such dynamics using interacting scalar and gauge theories in 1+1 spacetime dimensions, subject to a quench modeling a homogeneously expanding gravitational background. The models considered are the scalar λϕ 4 theory and the Schwinger model, i.e. a Dirac fermion coupled to a U(1) gauge field which is equivalent via bosonization to a scalar field with a cosine self-interaction. In the free scalar limit, both theories reproduce known analytical results, providing a nontrivial numerical validation of bosonization in curved spacetime for the Schwinger model. Our central finding is that self-interactions lead to a suppression of gravitational particle production compared to the free-field case, as evidenced by two-point functions and the spectra of produced particles. We further examine the behavior of entanglement generation and find that interactions suppress entanglement growth in the λϕ 4 theory, while in the Schwinger model, the interplay between suppressed particle production and enhanced inter-particle correlations leads to more complex entanglement behavior. Our results pave the way for further explorations of nonperturbative quantum real-time dynamics of interacting scalar and gauge theories in arbitrary gravitational backgrounds.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Generalizable machine learning potentials for quantum-accurate predictions of non-equilibrium behavior in 2D materials

Machine learning interatomic potentials (ML-IAPs) are emerging as transformative tools in materials modeling, promising quantum-level accuracy at a fraction of the computational cost. However, their ability to generalize beyond equilibrium configurations and to reliably capture defect- and temperature-driven behavior remains underexplored. Here, we develop and benchmark two state-of-the-art ML-IAPs, Spectral Neighbor Analysis Potential (SNAP) and Allegro, on a comprehensive dataset for monolayer MoSe₂. Using density functional theory (DFT) as the reference, we evaluate their performance in capturing stress–strain behavior, phase transition energetics, defect evolution, edge stability, and fracture toughness. Allegro, a deep equivariant neural network potential, surpasses both SNAP and the classical Tersoff potential in accuracy, efficiency, and transferability. Importantly, both ML potentials accurately reproduce experimental fracture measurements and ab initio predictions of inversion domain formation—phenomena well beyond their training sets. Our findings establish ML-IAPs as viable replacements for traditional force fields in the study of non-equilibrium mechanical phenomena, enabling large-scale, high-fidelity simulations in 2D materials and beyond. In conclusion, this work provides a broadly applicable framework for data-driven modeling of structural and functional transformations under extreme conditions.

2D materials↗
DOE OSTI2026

Small-𝑥 behavior in QCD from maximal entanglement and conformal invariance

Recent evidence suggests that, at small Bjorken 𝑥, QCD evolution drives the proton into a state of maximal entanglement. If the evolution kernel is assumed to be conformally invariant—as is the case for the Balitsky-Fadin-Kuraev-Lipatov equation—we can describe it by a conformal field theory. Moreover, the central charge 𝑐 of the corresponding conformal field theory emerges as the key parameter governing the 𝑥 dependence of both the entanglement entropy and the structure function. Here we apply the exact Bethe ansatz methods to the quantum spin chain dual to Lipatov’s high energy effective action to extract the central charge of the theory, and find that 𝑐 = 1. This implies the ∼𝑥 −1/3 small 𝑥 behavior for the structure function—the prediction that can be tested at the forthcoming Electron-Ion Collider.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

EFRC Accomplishments: Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC)

The vision of IMASC is to advance the fundamental science necessary to alter fundamental aspects of the chemical industries sector and its carbon footprint. The IMASC mission is to develop design principles for increasing the efficiency of key chemical syntheses using dilute alloy catalysts by quantitatively scaling from model studies to catalytic operation using advanced experiment and theory. One of the major challenges in catalysis is predicting catalytic behavior—especially selectivity. The IMASC team developed a methodology for obtaining critical information for selected catalytic processes using dilute alloy catalysts, that is widely applicable to modeling catalytic processes.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Tuning Discharge Behavior of Hollandite α-MnO 2 in Hydrated Zinc Ion Battery by Transition Metal Substitution

The tunnel-type hollandite α-MnO 2 is a promising cathode material for rechargeable aqueous zinc-ion batteries (ZIBs) due to its low cost in synthesis and high energy density. However, irreversible structural degradation upon continuous cycling prevents the cathode from being utilized commercially. Herein, density functional theory (DFT) was utilized to conduct a systematic study on tuning the behavior of α-MnO 2 by substituting the Mn ions on the tunnel wall with a transition metal (V or Cr) during the H + -/Zn 2+ -intercalation in hydrated ZIB. Our study revealed that both substituents aid cyclability and capacity retention with Cr outperforming V. In term of discharge voltage, only the Cr-substitution displays clear promotion at the early stage of discharge. The superior performance of substituted Cr 4+ comes from its unique atomic and electronic structures. Upon discharge, it can be reduced to Cr 3+ more readily than Mn 4+ and thereby limits the formation of unstable Mn 3+ or Mn 2+ centers; the formed Cr 3+ is more stable than Mn 3+ and Mn 2+ from the reduction of Mn 4+ ; and Cr 3+ can also greatly stabilize the neighboring Mn ions. Here this study highlights the significant tuning effect of transition metal substitution on the electrochemical and physical performance of α-MnO 2 as a cathode in hydrated ZIBs.

25 ENERGY STORAGE↗
DOE OSTI2023

Beyond AdS 2 /dCFT 1 : insertions in two Wilson loops

We consider two-point correlators of local operator insertions in a system of two Wilson-Maldacena loops in $\mathcal{N}$ = 4 supersymmetric Yang-Mills theory on both sides of the AdS/CFT correspondence. On the holographic side the correlator of two Wilson-Maldacena loops is given by a classical string world-sheet which in one phase connects two asymptotically AdS 2 regions and in the other phase is given by two disconnected AdS 2 caps; this configuration breaks supersymmetry as well as conformal invariance. We present a complete systematic account of the string world-sheet fluctuations, including the fermionic sector, and study the behavior of the holographic two-point correlators. On the field theory side we compute certain two-point correlators of local operator insertions by resumming sets of ladder diagrams. Our results demonstrate the efficacy of previously developed methods in tackling this non-conformal, non-susy regime.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗