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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Phase Diagram of the Easy-Axis Triangular-Lattice 𝐽 1 −𝐽 2 Model

Our work studies the largely unexplored phase diagram of the S = 1/2 easy-axis triangular-lattice J 1 -J 2 model, motivated by recent interest in rare-earth and transition-metal compounds that exhibit strong quantum fluctuations and broad spin excitation continua—hallmarks of spin-liquid behavior. Combining density-matrix renormalization group simulations with analytical insights, we present a comprehensive study of the model that describes these materials. Our results provide compelling evidence for a robust spin-liquid phase stabilized between the exotic supersolid Y phase and the collinear stripe phase, remarkably persistent even in the presence of symmetry-breaking anisotropy. Additionally, we analyze the supersolid Y phase, offering a quantitative characterization of its order parameters and clarifying the surprising absence of a ferromagnetic moment; both features relevant to recent experiments on Ising-like triangular-lattice magnets. Altogether, our work provides the necessary framework and important theoretical guidance to the ongoing searches of the spin liquid and other exotic states in the rare-earth and transition-metal triangular-lattice compounds, as well as for advancing the understanding of anisotropic-exchange magnets.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Applying machine learning and quantum chemistry to predict the glass transition temperatures of polymers

Glass transition temperature (T g ) is important for understanding the physical and mechanical properties of a polymer material because it relates to the thermal energy required to transition between a hard glassy state and a soft rubbery one. Over the years, various models have been developed for predicting this thermal property from molecular structure to aid in designing novel polymers in selected classes. This work builds on those efforts by utilizing both machine learning (ML) and quantum chemistry (QC) techniques to develop models that can predict T g values from the molecular structure under different data availability scenarios and for a wide variety of polymer types. For the ML model, a graph convolutional network (GCN) was used to map topological polymer features; this model was trained against a dataset of more than 7500 T g values and resulted in a root mean square error (RMSE) of 38.1 °C. The QC-based regression model was trained on 83 T g values and produced an RMSE of 34.5 °C. In conclusion, this work demonstrated that while both model techniques produce accurate predictions and are suitable for different data availability scenarios, the QC-based regression model offered a more interpretable model framework with significantly less training data.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Solid–liquid transition in a skyrmion matter

We report Monte-Carlo studies of the orientational order and melting of a 2D skyrmion lattice containing more than one million spins. Two models have been investigated, a microscopic model of lattice spins with Dzyaloshinskii–Moryia interaction that possesses skyrmions, and the model in which skyrmions are treated as point particles with repulsive interaction derived from a spin model. They produce similar results. The skyrmion lattice exhibits a sharp one-step transition between solid and liquid phases on temperature and the magnetic field. This solid–liquid transition is characterized by the kink in the magnetization. Here, the field-temperature phase diagram is computed. We show that the application of the field gradient to a 2D system of skyrmions produces a solid–liquid interface that must be possible to observe in experiments.

2D melting↗
DOE OSTI2025

Probing topological phases in a perturbed Kane-Mele model via RKKY interaction: Application to monolayer jacutingaite Pt 2 ⁢HgSe 3

Quantum spin Hall insulators (QSHIs) leverage strong spin-orbit coupling (SOC) for efficient spin manipulation, making them promising for spintronics. Here, in this study, we investigate the noncollinear Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction between two magnetic impurities in a perturbed Kane-Mele model with strong SOC, relevant to monolayer jacutingaite Pt 2 ⁢HgSe 3 as a prominent QSHI. Following the previous studies that mainly focused on the model and its general applications, we provide a systematic examination of the effects of various perturbations and strong spin-orbit hybridizations, which drive phase transitions that have not been extensively explored before. By incorporating these perturbations into the model and accurately accounting for spin-orbit hybridizations through spin-space Green's functions and the RKKY interactions, we uncover distinct, relative (rather than absolute) signatures of different phase transitions. These phase transitions are induced by both static and dynamic perturbations on the magnetic impurities. Notably, we identify additional phases emerging from the interplay with the magnetic substrate. All these influence the switching between ferromagnetic and antiferromagnetic, as well as clockwise and counterclockwise magnetic interactions. Our results provide a practical way to track topological phases through magnetic properties, offering new insights into phase control and spin manipulation in QSHIs.

Kane-Mele model↗
DOE OSTI2025

Enhanced Boundary Layer Height Detection Using Ceilometer, Surface Meteorology, and Radiation Products With a Random Forest Ensemble Method

This study develops and evaluates a Random Forest (RF) model for estimating planetary boundary layer height (PBLH) using 9 years of data from the Atmospheric Radiation Measurement Southern Great Plains (ARM SGP) user facility, with potential application in the NOAA Surface Radiation (SURFRAD) Network. The model integrates ceilometer, surface meteorology, and radiation measurements, and is trained using thermodynamic PBLH estimates derived from radiosondes. This approach aims to bridge gaps between aerosol-based and thermodynamic-based PBLH estimates. The RF model outperformed traditional methods during daytime and better captured transition periods, demonstrating improved accuracy and robustness. At ARM SGP, it showed a substantial reduction in both bias and RMSE, with a bias near zero (−4.9 m) compared with traditional Haar Wavelet (HW) (70.9 m) and Vaisala BL-View software (124.1 m), and an RMSE of 303.2 m, lower than both BL-View (566.9 m) and HW (404.6 m). During daytime hours, RF consistently outperformed both alternatives, maintaining lower bias and RMSE across all periods. At a second evaluation site, RF achieved the lowest overall RMSE (323.7 m), similar to HW (326.4 m) and significantly better than BL-View (738.3 m). However, all models showed reduced accuracy under stable nighttime conditions, limiting the reliability of PBLH estimates. Key predictors for the model included the lifting condensation level height (LCLH), aerosol gradients, and month for seasonal variability. The study underscores the potential of integrating machine learning with multiple data sets such as surface energy and thermodynamic data to advance PBLH estimation.

boundary layer height↗
DOE OSTI2024

Model-independent predictions for decays of double-heavy hadrons into pairs of heavy hadrons

Double-heavy hadrons can decay into pairs of heavy hadrons through transitions from confining Born-Oppenheimer potentials to heavy-hadron-pair potentials with the same Born-Oppenheimer quantum numbers. The states of the double-heavy hadron are constrained by a Born-Oppenheimer exclusion principle from the identical heavy quarks. The states of a pair of identical heavy hadrons are constrained by exclusion principles from identical particles. The transitions are also constrained by conservation of angular momentum and parity. From these constraints, we derive model-independent selection rules for decays of double-heavy hadrons into pairs of heavy hadrons. The coupling potentials are expressed as sums of products of Born-Oppenheimer transition amplitudes and angular-momentum coefficients. If there is a single dominant Born-Oppenheimer transition amplitude, it factors out of the coupling potentials between double-heavy hadrons in the same Born-Oppenheimer multiplet and pairs of heavy hadrons in specific heavy-quark-spin-symmetry multiplets and out of the corresponding partial decay rates. As examples, we discuss the Born-Oppenheimer potentials and multiplets for conventional double-heavy baryons and for double-heavy tetraquark mesons. We also discuss the relative partial decay rates for conventional double-heavy baryons into pairs of heavy hadrons.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Prediction of O and OH Adsorption on Transition Metal Oxide Surfaces from Bulk Descriptors

In the search for stable and active catalysts, density functional theory and machine learning (ML) based models can accelerate the screening of materials. While stability is conveniently addressed on the bulk level of computation, the modelling of catalytic activity requires expensive surface simulations. Here, in this work, we develop models for the surface adsorption energy of O and OH intermediates across a consistent and extensive dataset of pure transition metal oxide surfaces. We show that adsorption energies across metal oxidation states of +2 to +6 are well captured from the metal-oxygen bond strength extracted from the bulk level calculation. Specifically, we calculate the integrated crystal orbital Hamiltonian population (ICOHP) of the metal-oxygen bond in the bulk oxide and employ a simple normalization scheme to obtain a strong correlation with adsorption energetics. By combining our ICOHP descriptor with non DFT features in a Gaussian Process regression (GPR) model, we achieve high model accuracy with mean absolute errors of 0.166 and 0.219 eV for OH and O adsorption, respectively. By targeting the O-OH adsorption energy difference with our GPR model, we predict the the oxygen evolution reaction (OER) activity from bulk descriptors only. Furthermore, we utilize the strong correlation between the COHP and metal oxygen bond lengths to rapidly predict adsorption energetics and catalytic activity from the optimized bulk geometry. Our approach can enable an efficient search for active catalysts by eliminating the need for surface calculations in the initial screening phase.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

New avenues for |∆ B | = 2 processes beyond neutron-antineutron oscillations

We explore baryon-number-violating (|∆ B | = 2) processes beyond the well-known neutron-antineutron ($n - \bar{n}$) oscillations, focusing on the $Λ - \bar{Λ}$ system. The presence of a strange quark in the Λ baryon introduces a new set of six-quark operators roughly of the form (uds) 2 , which are different from the (udd) 2 operators responsible for oscillations. Using the Standard Model Effective Field Theory (SMEFT), we classify all dimension-9 operators that cause |∆ B | = 2 transitions and study their UV completions mediated by exotic scalar fields with trilinear interactions. We demonstrate that in these models, oscillations can occur at tree level, with $n - \bar{n}$ mixing potentially appearing at higher loop levels. We employ a chiral effective theory to constrain the effective mass mixing δm Λ , deriving bounds from current experimental limits on $n - \bar{n}$ oscillations and dinucleon decays such as pp → K + K + . These bounds indicate that $Λ - \bar{Λ}$ oscillations probe a complementary parameter space, sensitive to baryon-number violation at scales up to 10 2 − 10 3 TeV. We show that the existing indirect bounds make it challenging to provide a competitive bound on δm Λ at BESIII.

Baryon/Lepton Number Violation↗
DOE OSTI2026

Chirality reversal at finite magnetic impurity strength and local signatures of a topological phase transition

Here, we study the honeycomb lattice with a single magnetic impurity modeled by adding imaginary next-nearest-neighbor hopping 𝑖⁢ℎ on a single hexagon. This Haldane defect gives a topological mass term to the gapless Dirac cones and generates chirality. For a small density of defects, Neehus et al. [Phys. Rev. Lett. 135, 126604 (2025)] found that the system's chirality reverses at a critical ℎ 𝑐 ≈ 0.95 associated with an unexpected tricritical point of Dirac fermions at zero defect density. We investigate this zero-density limit by analyzing a single defect and computing two experimentally relevant measures of chirality: (1) orbital magnetization via local Chern marker, a bulk probe of all occupied states; and (2) electronic currents of low-energy states. Both probes show a chirality reversal at a critical ℎ 𝑐 ≈ 0.9–1.0. Motivated by this consistency, we propose a defect-scale toy model whose low-energy states reverse their chirality at ℎ$^{'}_{c}$ ≈ 0.87. Remarkably, the same pair of zero-energy bound states also generates the critical point ℎ 𝑐 in the full impurity projected T-matrix. Our results show how the chirality reversal produced by an impurity can be observed either in local probes or in the global topology, and suggest a possible role of the microscopic defect structure at the critical point.

Chern insulators↗
DOE OSTI2024

Multiphysics Degradation Modeling of Energy Storage Materials via RKPM with a Neural Network-Enhancement

In energy storage materials, strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking during charge/discharge cycling, resulting in reduced performance and service life. A coupled electro-chemo-mechanical reproducing kernel particle method (RKPM) formulation is developed, and a patch-test is formulated to certify optimal convergence of the proposed RKPM method for the coupled physics system. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based model construction by RKPM is then used to represent the complex material microstructures for modeling the coupled physics of these systems. Further, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1, 2] is introduced to effectively model damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022. [2] Baek, J., Chen, J. S., "A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture", Computer Methods in Applied Mechanics and Engineering Vol. 410, 116590, 2024.

electro-chemo-mechanical coupling↗
DOE OSTI2024

Leveraging a Neural Network-Enhanced Reproducing Kernel Particle Method for Multiphysics Degradation Modeling of Energy Storage Materials

Energy storage materials exhibit strong electro-chemo-mechanical coupling and highly anisotropic material properties, contributing to the formation and propagation of micro-cracking during charge/discharge cycling and resulting in reduced performance and service life. A coupled electro-chemo-mechanical reproducing kernel particle method (RKPM) formulation has been developed to analyze this system. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based model construction by RKPM is used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electro-chemo-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in meshing such complex geometries and capturing crack propagation due to mesh dependency. Here, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1, 2] is introduced to effectively model damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. NN-RKPM is additionally used to inform how crack opening and closure in turn affect the coupled chemical equations and material microstructure. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022. [2] Baek, J., Chen, J. S., "A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture", Computer Methods in Applied Mechanics and Engineering Vol. 410, 116590, 2024.

degradation↗
DOE OSTI2025

Density Functional Tight Binding Insights into Plasmonic Silver–Platinum Nanoparticles and Alloys for Enhanced Photocatalysis

Developing accurate and efficient Slater-Koster (SK) tight-binding parameter sets is essential for quantum plasmonic studies of alloyed metal nanoparticles, as conventional time dependent density functional theory (TD-DFT) calculations are computationally prohibitive for larger clusters. In this work, we develop and validate density functional tight binding (DFTB) parameter sets for both ground state (GS-SK) and excited state (ES-SK) calculations to study the structural, electronic, and optical properties of silver (Ag), platinum (Pt), and Ag–Pt nanoalloys. Our investigation of the ground state properties demonstrates that the GS-SK parameters enable DFTB to closely reproduce the electronic structures of platinum clusters with diverse sizes and geometries – showing qualitative agreement with DFT for density of states (DOS) profiles and energy levels. The ES-SK parameters accurately describe excited-state properties compared to TD-DFT reference calculations, including the broad, featureless absorption profiles of Pt that are dominated by interband transitions. Using the ES-SK parameters within a real-time TD-DFTB framework, we compute size-dependent optical absorption spectra of Ag, Pt and Ag-Pt nanocubes containing up to 1099 atoms (size ∼4.18 nm). A detailed study of Ag–Pt and Pt-Ag core–shell nanoparticles shows quenching of the Ag plasmon resonance even at monolayer coverage for Ag-Pt, but not for Pt-Ag. We also show how to define submonolayer Ag-core Pt-shell cubic structures that have similar optical properties to those generated experimentally for much larger particles, which offers potential for describing plasmon-enhanced photocatalysis. Collectively, the GS-SK and ES-SK parameter sets provide an accurate, computationally efficient approach for modeling the complex optical and electronic behavior of noble–transition metal nanostructures and their alloys.

SPR↗
DOE OSTI2025

Probing soft X-ray induced photoreduction of a model Mn-complex at cryogenic conditions

Soft X-ray absorption spectroscopy of first row transition elements at their respective L -edges provides important information about the oxidation and spin states of the metal centers. However, the associated sample damage in radiation-sensitive samples substantially alters the electronic and chemical structures of redox-active metal centers. Here, we measure the soft X-ray spectrum of the model Mn III (acac) 3 complex containing a redox-active Mn III metal center in an octahedral environment with a superconducting transition-edge sensor detector. To reduce the secondary damage resulting primarily from the diffusion of radicals and electrons, the spectra are collected at 30 K and 80 K on solid samples. Starting from the first scan, we detect the contribution of X-ray induced sample damage leading to a change in the Mn II intensity. However, at low temperatures, particularly at 30 K, we do not observe a gradual increase in the radiation damage with successive scans with the X-ray beam at the same spot. At our estimated dose of 90 kGy, we find 62% of Mn III (acac) 3 is still intact at 30 K. However, at room temperature, we see a gradual increase in radiation damage with increasing numbers of scans at the same spot, which is consistent with the possibility of increased diffusion rates of secondary radicals and electrons as noted in other studies.

L-edge spectroscopy↗
DOE OSTI2024

Advanced CO 2 Capture Solvent Systems for Dynamic Power Generation: Quarterly Research Performance Progress Report, QR4 (Q4FY24)

We developed an integrated Computational Fluid Dynamics (CFD) model to simulate the multi-physics coupled cooling process of mixed gas by cold water within a Direct Contact Cooler (DCC) equipped with a rotating packing bed (RPB). The model captures the interactions between fluid dynamics, heat transfer, mass transport, and phase transitions, while accounting for key operational variables such as RPB rotational speed and the mass flow rates of both liquid and gas. The CFD model has been validated using experimental data, specifically by comparing predicted outflow gas and liquid temperatures to measured results. Our findings demonstrate the significant effects of RPB rotational speed and mass flow rates on cooling performance, providing valuable insights for optimizing DCC efficiency in industrial applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Modeling kinetic effects of charged vacancies on electromechanical responses of ferroelectrics: Rayleighian approach

Understanding the time-dependent effects of charged vacancies on the electromechanical responses of materials is at the forefront of research for designing materials exhibiting metal-insulator transitions and memristive behavior. A Rayleighian approach is used to develop a model for studying the nonlinear kinetics of the reaction leading to generation of vacancies and electrons via the dissociation of vacancy-electron pairs. Also, diffusion and elastic effects of charged vacancies are considered to model polarization-electric potential and strain-electric potential hysteresis loops. The model captures multiphysics phenomena by introducing couplings among polarization, the electric potential, stress, strain, and concentrations of charged (multivalent) vacancies and electrons (treated as classical negatively charged particles), where the concentrations can vary due to association-dissociation reactions. A derivation of coupled time-dependent equations based on the Rayleighian approach is presented. Three limiting cases of the governing equations are considered, highlighting the effects of (1) nonlinear reaction kinetics on the generation of charged vacancies and electrons, (2) Vegard's law (i.e., the concentration-dependent local strain) on asymmetric strain-electric potential relations, and (3) coupling between a fast component and the slow component of the net polarization on the polarization-electric-field relations. The Rayleighian approach discussed in this work should pave the way for developing a multiscale modeling framework in a thermodynamically consistent manner while capturing multiphysics phenomena in ferroelectric materials. Published by the American Physical Society 2025

Kumar, Rajeev (ORCID:0000000194943488)↗
DOE OSTI2024

Magnetic, charge, and bond order in the two-dimensional Su-Schrieffer-Heeger-Holstein model

Most nonperturbative numerical studies of electron-phonon interactions focus on model Hamiltonians where the electrons interact with a phonon branch via a single type of microscopic mechanism. Two commonly explored couplings in this context are the Holstein and Su-Schrieffer-Heeger (SSH) interactions, which describe phonons modulating the on-site energy and intersite electron hopping, respectively. Many materials, however, have multiple phonon branches that can each interact with electronic degrees of freedom in different ways. We present here a determinant quantum Monte Carlo study of the half-filled two-dimensional (bond) SSH-Holstein Hamiltonian, where electrons couple to different phonon branches via either the Holstein or SSH mechanism. As a result, we map the model's phase diagram and determine the nature of the transitions between charge-density wave, bond-order wave, and antiferromagnetic order.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Measuring X-Ray Emission Line Shapes in Neutral Species for XRISM Calibration

Space X-ray spectrometers such as the Resolve instrument on XRISM require precise calibration in order to interpret the spectra of astrophysical objects. Key components of the calibration are the energy scale and the core line spread function, both of which vary with photon energy. A major issue in the calibration of high-resolution spectrometers is locating good calibrators with well-known and stable intrinsic line shapes. Neutral fluorescence is widely used, but inner-shell transitions in neutral atoms often exhibit complex, poorly documented line shapes that vary with excitation conditions. Here, in this study, we present empirical measurements of K-shell transitions in neutral O and F below 1 keV using an engineering model XRISM calorimeter array, an electron bombardment modulated X-ray source, and an electron beam ion trap (EBIT) to provide a precise energy reference. In addition, we report measurements of the Mo Lα complex with the transition-edge microcalorimeter spectrometer (TEMS), which reveal strong satellite structure and sensitivity of the line shape to the incident exciting spectrum. Together, these results demonstrate the need for empirical line-shape models, highlight the nonstationary nature of neutral fluorescence features, and define a path toward developing transfer standards for XRISM and future precision instruments such as Athena/X-IFU.

Astronomy and AstroPhysics↗
DOE OSTI2024

Gallery of soft modes: Theory and experiment at a ferromagnetic quantum phase transition

We examine the low-energy excitations in the vicinity of the quantum critical point in LiHoF4, a physical realization of the Transverse Field Ising Model, focusing on the long-range fluctuations which soften to zero energy at the ferromagnetic quantum phase transition. Microwave spectroscopy in tunable loop-gap resonator structures identifies and characterizes the electronuclear soft mode and higher-energy electronuclear states as a function of frequency and magnetic fields applied transverse and parallel to the Ising axis. These are understood in the context of a theoretical model of a soft electronuclear mode that interacts with soft photons as well as soft phonons. We identify competing infrared divergences at the quantum critical point, coming from the photons and the electronuclear soft mode. It is an incomplete cancellation of these divergences that leads to the muted but distinct signatures observed in the experiments. The application of a longitudinal magnetic field gaps the soft mode. As a result, measurements well away from the quantum critical point reveal a set of "Walker'' modes associated with ferromagnetic domain dynamics.

Dipolar interaction↗