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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 OSTI2022

Magnon heat transport in a two-dimensional Mott insulator

Whether or not anomalies in the thermal conductivity in insulating cuprates can be attributed to antiferromagnetic order and magnons in a 2D Mott insulator remains an intriguing open question. To shed light on this issue, we investigate the thermal conductivity $\kappa$ and its relationship with the specific heat $c_v$ in the half-filled 2D single-band Hubbard model, using the numerically exact determinant quantum Monte Carlo algorithm and maximum entropy analytic continuation. At low temperatures where the charge degrees of freedom are gapped-out and $c_v$ exhibits a clear magnon peak, we observe that thermal conductivity $\kappa$ also tends to form a peak at similar temperatures. Reducing temperature further produces a sharp upturn in $\kappa$, associated with an increasing mean-free path. Here we identify this as the high-temperature side of the anomalous peak in insulating cuprates, where the mean-free path eventually is cut-off by other scattering effects, including phonons, disorder, and physical size. Different scattering effects in our model are identified and analyzed in the thermal diffusivity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

A novel conditional generative model for efficient ensemble forecasts of state variables in large-scale geological carbon storage

Integrating monitoring data to efficiently update reservoir pressure and CO 2 plume distribution forecasts presents a significant challenge in geological carbon storage (GCS) applications. Inverse modeling techniques are commonly used to fuse observational data and refine reservoir model parameters, thereby improving state variable forecasts. However, these techniques often rely on linear or Gaussian assumptions, which can limit their effectiveness in accurately predicting state variables. Moreover, simulating large-scale three-dimensional (3D) GCS problems is computationally expensive, making iterative runs in inverse problems prohibitive. To address these challenges, we propose a conditional generative model utilizing the score-based diffusion method for real-time 3D pressure and saturation field distribution predictions. Our approach involves solving the score function with a mini-batch-based Monte Carlo estimator to generate labeled data. This data is subsequently employed to train a fully connected neural network, enabling it to learn the conditional sample generator within a supervised learning framework. This method enables the rapid generation of a large ensemble of predictions, facilitating comprehensive uncertainty quantification of state variables. Here we applied our method to forecast the dynamic 3D distributions of pressure and saturation fields over a 30-year injection period. The statistical assessment with low root mean square error (RMSE) values demonstrates that our method can accurately predict the spatiotemporal distributions of both pressure and saturation fields. Moreover, the developed conditional generative model shows high computational efficiency by generating 100 ensemble forecasts of 3D state variables in less than 10 min. The consistency between ensemble averages and ground truth values further illustrates the model’s capability to capture state variable dynamics during the CO 2 plume injection process. Notably, the ground truth values fall within the ensemble forecasts, indicating that our uncertainty quantification effectively captures variability and potential noise in the observations. Thus, the developed conditional generative model proves to be a more efficient, accurate, and practical tool for GCS applications, facilitating timely risk analysis and informed decision-making.

58 GEOSCIENCES↗
DOE OSTI2024

SEI Formation and Lithium-Ion Electrodeposition Dynamics in Lithium Metal Batteries via First-Principles Kinetic Monte Carlo Modeling

The stabilization and enhanced performance of lithium metal batteries (LMBs) depend on the formation and evolution of the Solid Electrolyte Interphase (SEI) layer as a critical component for regulating the Li metal electrodeposition processes. This study employs a first-principles kinetic Monte Carlo (kMC) model to simulate the SEI formation and Li + electrodeposition processes on a lithium metal anode, integrating both the electrochemical electrolyte reduction reactions and the diffusion events giving place to the SEI aggregation processes during battery charge and discharge processes. The model replicates the competitive interactions between organic and inorganic SEI components, emphasizing the influence of the cycling regime. Results indicate that grain boundaries within the SEI facilitate faster lithium-ion transport compared to crystalline regions, crucial for improving the performance and stability of LMBs. The findings underscore the importance of dynamic SEI modeling for further development of next-generation high-energy-density batteries.

25 ENERGY STORAGE↗
DOE OSTI2025

Determining hexavalent chromium transport properties in alkaline nuclear waste using nuclear magnetic resonance spectroscopy

This study focuses on the transport properties of hexavalent chromium, specifically the chromate anion, to improve predictive models and environmental remediation strategies for Cr(VI) migration. Using 53 Cr Nuclear Magnetic Resonance (NMR) spectroscopy, the research quantifies chromate in multicomponent electrolytes replicating nuclear waste conditions at the Hanford Site in Washington State. The consistency of the 53 Cr NMR signal integral with chromate concentration, despite varying matrix compositions, establishes it as a reliable concentration indicator. The transport properties of chromate in an alkaline solution were assessed using relaxation-based measurements via saturation recovery and Carr-Purcell-Meiboom-Gill experiments, determining spin-lattice and spin-spin relaxation times. These measurements, combined with the Bloembergen-Purcell-Pound equation, helped estimate the rotational correlation time and the 53 Cr self-diffusion coefficient using Stokes-Einstein-Debye and Stokes-Einstein equations. Direct measurements were obtained through pulsed field gradient stimulated echo 53 Cr NMR spectroscopy. Monte Carlo simulations further estimated uncertainty propagation. The results enhance comprehension of chromate transport and highlight prospects for identifying transport properties of NMR-active nuclei, traditionally considered unreachable.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

GrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects

Simulation-based approaches to microstructure generation can suffer from a variety of limitations, such as high memory usage, long computational times, and difficulties in generating complex geometries. Generative machine learning models present a way around these issues, but they have previously been limited by the fixed size of their generation area. Here, we present a new microstructure generation methodology leveraging advances in inpainting using denoising diffusion models to overcome this generation area limitation. We show that microstructures generated with the presented methodology are statistically similar to grain structures generated with a kinetic Monte Carlo simulator, SPPARKS.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Short Range Order in Disordered Spinel and the Impact on Cation Vacancy Transport

Spinels are important complex oxides for use in radiation damage environments and resulting from the corrosion of steels. It is known that, in these environments, normal spinels exist with some concentration of antisite cation pairs known as inversion. In this work we show that even in highly disordered states characterized by high levels of inversion, spinel still shows some short range order (SRO) that manifests itself in antisite chains. The propensity to form these antisite chains is confirmed through Monte Carlo simulations which find that the length of chains which can form depend on the spinel chemistry. We also consider the effect of antisite chains on the diffusivity of cation vacancies and find the effect strongly depends on the spinel chemistry. At the extremes, chains in FeCr2O4 significantly increase vacancy transport but in MgAl2O4 chains have the inverse effect of drastically reducing vacancy mobility. The explanation of these dramatically different effects results from the assessment of the thermodynamic stability of the antisite chains and contrasting attractive/repulsive interactions of vacancies with the chains.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Sensor Distortion Effects in Photon Monte Carlo Simulations

We present a detailed method to simulating sensor distortions using a photon and electron Monte Carlo method. We use three-dimensional electrostatic simulations to parameterize the perturbed electric field profile for nonideal sensor details. We follow the conversion of simulated photons, and the subsequent response of the converted electrons to the electric field pattern. These nonideal sensor details can be implemented efficiently in a Monte Carlo approach. We demonstrate that the nonideal sensor distortions have a variety of observable consequence including the modification of the astrometric pattern; the distortion of the electron diffusion size and shape; and the distortion of flats. We show analytic validation of the diffusion physics, reproduce two kinds of edge distortion, and show qualitative validation of field-free regions, lithography errors, and fringing. We also demonstrate that there are two related effects of doping variation having different observable consequences. We show that field distortions from accumulated electrons lead to intensity-dependent point-spread functions and the sublinear variance in flats. The method is implemented in the Photon Simulator and the code is publicly available.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Unveiling the Origin of Morphological Instability in Topologically Complex Electrocatalytic Nanostructures

Coarsening and degradation phenomena in metals have largely focused on thermally driven processes, such as bulk and surface diffusion. However, dramatic coarsening has been reported in high-surface-area, nanometer-sized Pt-based catalysts during potential cycling in an electrolyte at room temperature─a temperature too low for the process to be explained purely by surface mobility values measured in both vacuum and electrolytes (∼10 –22 and ∼10 –18 cm 2 /s, respectively). This morphological evolution must be due to a different mechanism for mass transport that is sensitive to electrochemical conditions (e.g., electrolyte composition, potential limits, and scan rate). However, there have been no notable studies of electrochemically induced coarsening in nanometer-sized electrocatalysts. Here, we unveil the origins of coarsening in an electrolyte through coupled in situ experiments and atomistic kinetic Monte Carlo (kMC) simulations. Our work demonstrates electrochemical coarsening is driven by two concurrent mechanisms that can be explained at the atomistic level: (i) dissolution/redeposition during the reduction of an oxidized species and (ii) rapid surface diffusion of undercoordinated atoms.

gold↗
DOE OSTI2023

Expandable Li Percolation Network: The Effects of Site Distortion in Cation-Disordered Rock-Salt Cathode Material

Cation-disordered rock-salt (DRX) materials receive intensive attention as a new class of cathode candidates for high-capacity lithium-ion batteries (LIBs). Unlike traditional layered cathode materials, DRX materials have a three-dimensional (3D) percolation network for Li + transportation. The disordered structure poses a grand challenge to a thorough understanding of the percolation network due to its multiscale complexity. In this work, we introduce the large supercell modeling for DRX material Li 1.16 Ti 0.37 Ni 0.37 Nb 0.10 O 2 (LTNNO) via the reverse Monte Carlo (RMC) method combined with neutron total scattering. Here, through a quantitative statistical analysis of the material’s local atomic environment, we experimentally verified the existence of short-range ordering (SRO) and uncovered an element-dependent behavior of transition metal (TM) site distortion. A displacement from the original octahedral site for Ti 4+ cations is pervasive throughout the DRX lattice. Density functional theory (DFT) calculations revealed that site distortions quantified by the centroid offsets could alter the migration barrier for Li + diffusion through the tetrahedral channels, which can expand the previously proposed theoretical percolating network of Li. The estimated accessible Li content is highly consistent with the observed charging capacity. The newly developed characterization method here uncovers the expandable nature of the Li percolation network in DRX materials, which may provide valuable guidelines for the design of superior DRX materials.

25 ENERGY STORAGE↗
DOE OSTI2025

Overcoming time and complexity limitations in molecular dynamics investigations of equilibrium melting

Abstract A hybrid Monte-Carlo molecular-dynamics method for determining solidus and liquidus compositions in multicomponent systems is presented that overcomes both the time limitations in conventional molecular dynamics that prevent the evolution of distinct solid and liquid compositions via diffusion and the complexity challenge that prevents use of thermodynamic assessment in systems of many components. This hybrid method is validated in the Cu–Ni system against an independent assessment of solidus and liquidus compositions based on the regular solution model. Strategies for efficient mapping of different phase diagrams, based on the thermodynamic parameter T 0 , the temperature at which two phases of the composition X 0 have equal free energies, are presented and then demonstrated for the copper-nickel fully miscible system and the gold–silicon eutectic system. A calculation of the solidus and liquidus sampled during the equilibrium melting of equiatomic CrMnFeCoNi is performed, indicating that this method has potential to be extended to the study of many component alloys.

Au-Si↗
DOE OSTI2026

Integrated Strategies for Overcoming Resolution Limits in Electron Beam Lithography of Chemically Amplified Resists

Electron beam lithography (EBL) of chemically amplified resists (CARs) faces fundamental challenges, including stochastic electron scattering and acid diffusion, that limit resolution and reproducibility. Using SU-8 as a model CAR, this study systematically investigated complementary strategies to address these challenges, combining multipass exposure, proximity effect correction (PEC) with midrange correction factors, base quencher incorporation, and post-exposure bake (PEB) suppression. Monte Carlo simulations and calibrated PEC modeling revealed that extending the point spread function to include a midrange scattering component significantly improved critical dimension (CD) control across varying pattern densities, correcting deviations that conventional two-term PEC failed to capture. Multipass exposure, particularly 4-pass writing with a 25% offset, redistributed the dose to average stochastic beam and scattering fluctuations, reducing line-width roughness by more than 50% and yielding more uniform nanoscale features. Photoacid confinement was investigated by adding urea as a base quencher, which successfully reduced acid diffusion but introduced substantial sensitivity penalties without improving ultimate resolution or Z-factor performance, underscoring the trade-offs of chemical versus physical confinement. Suppressing PEB most directly minimized acid diffusion, resulting in improved Z-factors and reproducible 30 nm half-pitch dense line/space patterns. Overall, these results demonstrated that PEC with midrange correction, multipass strategies, quencher additives, and PEB-free processing addresses different aspects of the EBL process window and that their integration provides a comprehensive framework for managing stochastic scattering, diffusion, and chemical amplification effects. This framework advances dense nanoscale patterning in CARs and establishes guiding principles for optimizing resist design and process strategies in high-resolution EBL and potentially other advanced lithographies, such as extreme ultraviolet (EUV) lithography.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Cosmic ray transport in mixed magnetic fields and their role on the observed anisotropies

ABSTRACT There is a growing set of observational data demonstrating that cosmic rays exhibit small-scale anisotropies (5°–30°) with amplitude deviations lying between 0.01–0.1 per cent that of the average cosmic ray flux. A broad range of models have been proposed to explain these anisotropies ranging from finite-scale magnetic field structures to dark matter annihilation. The standard diffusion transport methods used in cosmic ray propagation do not capture the transport physics in a medium with finite-scale or coherent magnetic field structures. Here, we present a Monte Carlo transport method, applying it to a series of finite-scale magnetic field structures to determine the requirements of such fields in explaining the observed cosmic ray, small-scale anisotropies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Spin dynamics of the antiferromagnetic Heisenberg model on a kagome bilayer

We research the spin dynamics of a classical Heisenberg antiferromagnet with nearest-neighbor interactions on a quasi-two-dimensional kagome bilayer. This geometrically frustrated lattice consists of two kagome layers connected by a triangular-lattice linking layer. By combining Monte Carlo with precessional spin dynamics simulations, we compute the dynamical structure factor of the classical spin liquid in the kagome bilayer and investigate the thermal and dilution effects. While the low-frequency and long-wavelength dynamics of the cooperative paramagnetic phase is dominated by spin diffusion, weak magnon excitations persist at higher energies, giving rise to the half-moon pattern in the dynamical structure factor. In the presence of spin vacancies, the dynamical properties of the diluted system can be understood within the two-population picture. The spin diffusion of the “correlated” spin clusters is mainly driven by the zero-energy weather-vane modes, giving rise to an autocorrelation function that decays exponentially with time. On the other hand, the diffusive dynamics of the quasifree “orphan” spins leads to a distinctive longer-time power-law tail in the autocorrelation function. We discuss the implications of our work for the glassy behaviors observed in the archetypal frustrated magnet SrCr 9p Ga 12-9 pO 19 (SCGO).

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Molecular Dynamics Simulation of Pore-Size Effects on Gas Adsorption Kinetics in Zeolites

Strong gas-mineral interactions or slow adsorption kinetics require a molecular-level understanding of both adsorption and diffusion for these interactions to be properly described in transport models. In this combined molecular simulation and experimental study, noble gas adsorption and mobility is investigated in two naturally abundant zeolites whose pores are similar in size (clinoptilolite) and greater than (mordenite) the gas diameters. Simulated adsorption isotherms obtained from grand canonical Monte Carlo simulations indicate that both zeolites can accommodate even the largest gas (Rn). However, gas mobility in clinoptilolite is significantly hindered at pore-limiting window sites, as seen from molecular dynamics simulations in both bulk and slab zeolite models. Further, experimental gas adsorption isotherms for clinoptilolite confirm the presence of a kinetic barrier to Xe uptake, resulting in the unusual property of reverse Kr/Xe selectivity. Finally, a kinetic model is used to fit the simulated gas loading profiles, allowing a comparison of trends in gas diffusivity in the zeolite pores.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Development of interatomic potential and effect of ordering on defect properties in CrMnV

Developing materials that can withstand extreme environments, such as high radiation doses and elevated temperatures, is crucial for next-generation particle accelerators, including the 2.4 MW Long-Baseline Neutrino Facility. High-Entropy Alloys have emerged as promising candidates for beam window materials due to their superior mechanical strength, corrosion resistance, and radiation tolerance. In this study, we focus on the Cr–Mn–V alloy system, developing and employing machine-learning interatomic potentials (MLIPs) to investigate the formation of an ordered phase and its influence on defect properties. Using hybrid Monte Carlo-Molecular Dynamics simulations, we observe the formation of a B2-ordered phase at lower temperatures, consistent with Density Functional Theory (DFT) predictions. Ordered structures display a bimodal distribution of migration energies and reduced mean square displacement values, indicating suppressed vacancy diffusion. Our results also show that the migration energy barrier varies based on the atomic species, with Mn and V exhibiting the highest and lowest average barriers, respectively. These findings suggest that atomic ordering inhibits defect mobility, potentially enhancing the radiation resistance of CrMnV alloys. The validated MLIP provides a reliable framework for simulations that are faster than traditional DFT while maintaining the accuracy required to study defect and ordering properties.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Rotational Dynamics and Transition Mechanisms of Surface-Adsorbed Proteins

Assembly of biomolecules at solid-water interfaces requires molecules to traverse complex orientation-dependent energy landscapes through processes that are poorly understood, largely due to the dearth of in-situ single molecule measurements and statistical analyses of the rotational dynamics that define directional selection. Emerging capabilities in high-speed atomic force microscopy and machine learning have allowed us to directly determine the orientational energy landscape and observe and quantify the rotational dynamics for protein nanorods on the surface of muscovite mica under a variety of conditions. Comparisons with kinetic Monte Carlo simulations show that the transition rates between adjacent orientation-specific energetic minima can largely be understood through traditional models of in-plane Brownian rotation across a biased energy landscape, with resulting transition rates that are exponential in the energy-barriers between states. However, transitions between more distant angular states are decoupled from barrier height, with jump-size distributions showing a power-law decay that is characteristic of a non-classical Levy-flight random walk, indicating that large jumps are enabled by alternative modes of motion via activated states. The findings provide new insights into the dynamics of biomolecules at solid-liquid interfaces that lead to self-assembly, epitaxial matching and other orientationally anisotropic outcomes and define a general procedure for exploring such dynamics with implications for hybrid biomolecular-inorganic materials design.

Orientational energy landscapes, Rotational dynami↗
DOE OSTI2023

Deconstructing magnetization noise: Degeneracies, phases, and mobile fractionalized excitations in tetris artificial spin ice

Direct detection of spontaneous spin fluctuations, or “magnetization noise,” is emerging as a powerful means of revealing and studying magnetic excitations in both natural and artificial frustrated magnets. Depending on the lattice and nature of the frustration, these excitations can often be described as fractionalized quasiparticles possessing an effective magnetic charge. Here, by combining ultrasensitive optical detection of thermodynamic magnetization noise with Monte Carlo simulations, we reveal emergent regimes of magnetic excitations in artificial “tetris ice.” A marked increase of the intrinsic noise at certain applied magnetic fields heralds the spontaneous proliferation of fractionalized excitations, which can diffuse independently, without cost in energy, along specific quasi-1D spin chains in the tetris ice lattice.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Toward data assimilation of ship-induced aerosol–cloud interactions

Satellite imagery can detect temporary cloud trails or ship tracks formed from aerosols emitted from large ships traversing our oceans, a phenomenon that global climate models cannot directly reproduce. Ship tracks are observable examples of marine cloud brightening, a potential solar climate intervention that shows promise in helping combat climate change. In this paper, we demonstrate a simulation-based approach in learning the behavior of ship tracks based upon a novel stochastic emulation mechanism. Our method uses wind fields to determine the movement of aerosol–cloud tracks and uses a stochastic partial differential equation (SPDE) to model their persistence behavior. This SPDE incorporates both a drift and diffusion term which describes the movement of aerosol particles via wind and their diffusivity through the atmosphere, respectively. We first present our proposed approach with examples using simulated wind fields and ship paths. We then successfully demonstrate our tool by applying the approximate Bayesian computation method-sequential Monte Carlo for data assimilation.

54 ENVIRONMENTAL SCIENCES↗