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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Large-scale simulations of Floquet physics on near-term quantum computers

Abstract Periodically driven quantum systems exhibit a diverse set of phenomena but are more challenging to simulate than their equilibrium counterparts. Here, we introduce the Quantum High-Frequency Floquet Simulation (QHiFFS) algorithm as a method to simulate fast-driven quantum systems on quantum hardware. Central to QHiFFS is the concept of a kick operator which transforms the system into a basis where the dynamics is governed by a time-independent effective Hamiltonian. This allows prior methods for time-independent simulation to be lifted to simulate Floquet systems. We use the periodically driven biaxial next-nearest neighbor Ising (BNNNI) model, a natural test bed for quantum frustrated magnetism and criticality, as a case study to illustrate our algorithm. We implemented a 20-qubit simulation of the driven two-dimensional BNNNI model on Quantinuum’s trapped ion quantum computer. Our error analysis shows that QHiFFS exhibits not only a cubic advantage in driving frequency ω but also a linear advantage in simulation time t compared to Trotterization.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Towards Practical Quantum Simulation for High Energy Physics (Final report for DE-SC0019452)

Quantum computing offers the potential of transformative improvements in our ability to simulate strongly interacting quantum systems. Each application area of quantum simulation requires careful study and new insights to determine the potential advantages of quantum computation. In particular, high energy physics and quantum field theory present specific challenges to existing approaches. In this project we studied quantum simulation in the light-front formulation of quantum field theory. In this formulation, quantum field theory more closely resembles other many-body quantum systems such as quantum chemistry, which are well studied from the perspective of quantum computation. We considered applications suitable for both near term and future error-corrected quantum computers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

A mathematical framework for thermodynamic computing with applications to chemical reaction networks

The widespread adoption of energy-intensive computing applications has led to a growing need for energy-efficient computing approaches. Thermodynamic computing offers a promising approach for low-energy computation by leveraging the intrinsic computational capabilities of physical, chemical, or biological systems. However, the mathematical foundations of thermodynamic computing require further development to fully realize the potential energy efficiencies, as well as to assess factors like noise and operational speed. In this paper, we establish a mathematical framework for utilizing thermodynamic processes to perform fundamental operations, including addition, subtraction, multiplication, and division. We highlight the use of chemical reactions as potential computational units and explore synthetic chemical and biochemical systems as practical implementations. Additionally, we demonstrate how these principles can be applied to solving complex mathematical problems, such as ordinary differential equations (ODEs) and suggest the necessary components to implement the thermodynamic computing framework using chemical reactions based in a microfluidic device. This work enhances our understanding of thermodynamic processes for natural computing as a basis for scalable, energy-efficient computation in paradigm disruptive next-generation systems.

Cannon, William R. [Pacific Northwest National Lab↗
DOE OSTI2025

CO 2 in Ionene–Ionic Liquid Composite Membranes

Abstract Ionene – ionic liquid (IL) composites are promising materials for CO 2 separation, yet a molecular‐level understanding of their structure and its impact on CO 2 speciation, solubility, rotation, and diffusivity remains unclear. Herein, using multimodal nuclear magnetic resonance (NMR), time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS), atomic force microscopy (AFM), and molecular dynamics (MD) simulations, we reveal that the composites contain IL‐rich domains extending across hundreds of nanometres within the ionene matrix, and these bicontinuous domains span the entire membrane depth. CO 2 also absorbs into the ionene matrix, with the distribution between two CO 2 species varying with temperature and time. The rotational correlation times of these two species are on the timescale of 0.1 and 1 ns, respectively. As IL content increases, the ionic domains expand, resulting in higher CO 2 solubility due to enhanced molecular dynamics and increased free volume in both ionene backbones and IL‐rich regions. Although CO 2 diffusion in the membranes is an order of magnitude slower than in bulk IL, the activation energy for CO 2 diffusion remains comparable. Ionene‐IL composites represent a promising platform for designing CO 2 separation membranes, offering enhanced CO 2 diffusion and selectivity through IL‐rich domains, and increased CO 2 solubility and mechanical integrity from the ionene matrix.

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

Selective Recovery of Critical Minerals from Simulated Electronic Wastes Via Reaction‐Diffusion Coupling

Abstract Atom‐ and energy‐efficient chemical separations are urgently needed to meet the surging demand for critical materials that has strained supply chains and threatened environmental damage. In this study, we used reaction‐diffusion coupling to separate iron, neodymium, and dysprosium ions from model feedstocks of permanent magnets, which are typically found in electronic wastes. Feedstock solutions were placed in contact with a hydrogel loaded with potassium hydroxide and/or dibutyl phosphate, resulting in complex precipitation patterns as the various metal ions diffused into the reaction medium. Specifically, we observed the precipitation of up to 40 mM of iron from the feedstock, followed by the enrichment of 73 % dysprosium, and the extraction of >95 % neodymium product at a further distance from the solution‐gel interface. We designed a series of experiments and simulations to determine the relevant ion diffusivities, D Nd =5.4×10 −10 and D Dy =5.1×10 −10 m 2 /s, and precipitation rates, k Nd =1.0×10 −5 and k Dy =5.0×10 −3 m 9 mol −3 s −1 , which enabled a numerical model to be established for predicting the distribution of products in the reaction medium. Our proof‐of‐concept study validates reaction‐diffusion coupling as an effective and versatile approach for critical materials separations, without relying on ligands, membranes, resins, or other specialty chemicals.

Wang, Qingpu [Physical and Computational Sciences ↗
DOE OSTI2026

California Bridge to the Electron-Ion Collider (EIC) (Final Technical Report)

This report summarizes the activities and outcomes of DOE Grant DE-SC0022358, a traineeship program designed to prepare students for future nuclear physics research at the Electron-Ion Collider. The project leveraged the California EIC Consortium and statewide academic networks to provide immersive research experiences spanning universities and national laboratories. Participants engaged in theoretical, computational, and experimental research related to EIC physics and took part in professional meetings and collaborative research activities. The program supported student preparation for advanced study and research careers in nuclear physics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems

Abstract Predicting complex dynamics in physical applications governed by partial differential equations in real-time is nearly impossible with traditional numerical simulations due to high computational cost. Neural operators offer a solution by approximating mappings between infinite-dimensional Banach spaces, yet their performance degrades with system size and complexity. We propose an approach for learning neural operators in latent spaces, facilitating real-time predictions for highly nonlinear and multiscale systems on high-dimensional domains. Our method utilizes the deep operator network architecture on a low-dimensional latent space to efficiently approximate underlying operators. Demonstrations on material fracture, fluid flow prediction, and climate modeling highlight superior prediction accuracy and computational efficiency compared to existing methods. Notably, our approach enables approximating large-scale atmospheric flows with millions of degrees, enhancing weather and climate forecasts. Here we show that the proposed approach enables real-time predictions that can facilitate decision-making for a wide range of applications in science and engineering.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Thermodynamic Stability and Site‐Specific Distribution of Graphitic and Pyridinic Nitrogen in Graphene Moiré on Ru(0001)

Abstract Graphene‐like materials are of interest for large‐scale hydrogen storage applications due to their lightweight, durable, and scalable properties. Nitrogen‐doping minimizes kinetic limitations in diffusion and recombination on surfaces, however, the role of graphitic nitrogen (GN) and pyridinic nitrogen (PN) is not well understood. Nitrogen‐doped graphene is synthesized on Ru(0001) using chemical vapor deposition (CVD) of pyridine and ion irradiation. Scanning tunneling microscopy (STM), x‐ray photoelectron spectroscopy (XPS), and density functional theory (DFT) are used to identify the structure, location, and thermodynamic stability of nitrogen species within the graphene moiré. CVD of pyridine results in a low nitrogen concentration (<0.1at%), while the post‐growth nitrogen ion irradiation allows us to increase the concentration further. The concentration of GN and PN is controlled by varying the ion dose and annealing temperature. Comparison of measured and simulated STM images of GN and PN yield an excellent agreement, allowing us to confidently establish that GN is preferentially located near the center of the Atop region, while PN is located in the valley region of the graphene moiré. This report explicitly confirms the site assignments and provides a foundation for the site synthesis and analysis of structural and electronic properties that drive the reactivity of N‐doped graphene.

Gedara, Buddhika S. A. [Physical and Computational↗
DOE OSTI2022

AI and extreme scale computing to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non-precessing black hole mergers

We use artificial intelligence (AI) to learn and infer the physics of higher order gravitational wave modes of quasi-circular, spinning, non precessing binary black hole mergers. We trained AI models using 14 million waveforms, produced with the surrogate model NRHybSur3dq8, that include modes up to $\ell$ ≤ 4 and (5,5), except for (4,0) and (4,1), that describe binaries with mass-ratios $\textit{q}$ ≤ 8, individual spins $s^z_{\{1,2\}} \in$[–0.8,0.8], and inclination angle $θ \in$ [0,π]. Our probabilistic AI surrogates can accurately constrain the mass-ratio, individual spins, effective spin, and inclination angle of numerical relativity waveforms that describe such signal manifold. We compared the predictions of our AI models with Gaussian process regression, random forest, k-nearest neighbors, and linear regression, and with traditional Bayesian inference methods through the PyCBC Inference toolkit, finding that AI outperforms all these approaches in terms of accuracy, and are between three to four orders of magnitude faster than traditional Bayesian inference methods. Our AI surrogates were trained within 3.4 hours using distributed training on 1,536 NVIDIA V100 GPUs in the Summit supercomputer.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Surrogate Model Integration with MOOSE XFEM for Creep Crack Growth

Ferritic-martensitic steels are key structural materials for advanced reactors but experience time-dependent deformation and damage under prolonged high temperature and irradiation, leading to creep-driven crack initiation and growth. High-fidelity models—crystal plasticity with irradiation mechanisms, phase-field for microstructural evolution, and continuum-damage viscoplasticity—capture the underlying physics but are too computationally intensive for broad design-space exploration and uncertainty quantification. This milestone advances a scalable alternative by integrating a microstructure-sensitive surrogate creep model into the Multiphysics Object-Oriented Simulation Environment (MOOSE) finite element framework and extending it to fracture via the extended finite element method (XFEM). The surrogate model, developed with collaborators at Sandia and Los Alamos National Laboratories, maps relevant microstructural descriptors to the viscoplastic response of HT9. We embed this surrogate within a coupled deformation-damage workflow in MOOSE/XFEM to simulate creep-driven crack initiation and propagation. Implementation enhancements include updates to the material interface, a plastic correction phase involving microstructure evolution, and fracture criteria to ensure numerical robustness and compatibility with the surrogate structure. Demonstrations on canonical creep benchmarks spanning uniaxial and multiaxial states show that the surrogate reproduces key trends of high-fidelity models while substantially reducing computational cost. The resulting capability bridges physics fidelity and performance, providing a practical path to a predictive, microstructure-aware assessment of creep and fracture in reactor materials.

36 - MATERIALS SCIENCE↗
DOE OSTI2020

Two-Layer Dynamic Recycling Model (2L-DRM): Learning from Moisture Tracking Models of Different Complexity

Atmospheric moisture tracking models are used to identify and quantify sources and sinks of water in the atmospheric branch of the hydrologic cycle. These models are primarily used to investigate the origin of moisture resulting in precipitation for particular regions around the globe. Moisture tracking models vary widely in their level of complexity, depending on the number of physical processes represented. Complex models are comprehensive in their physical representation, but computationally much more expensive than simple models, which only focus on specific physical processes and use simplifying assumptions. We present the mathematical derivation of the new two-layer dynamical recycling model (2L-DRM), a simple analytical moisture tracking model that relaxes the vertically-integrated formulation of the original one-layer DRM (Dominguez et al., 2006). By comparing the simple DRM to a very complex moisture tracking model that uses water vapor tracers embedded within the Weather Research and Forecast regional climate model (WRF-WVT) for the North American Monsoon region, we pinpoint the absence of vertical wind shear as the main deficiency in the simple DRM. When comparing both simple models (DRM and 2L-DRM) to the WRF-WVT (which we treat as ‘truth’), the 2L-DRM better captures the spatial extent, the net amount and the temporal variability of precipitation that originates from oceanic and local terrestrial sources. The 2L-DRM is well suited to study the large-scale climatological sources of moisture, and for these applications, performs on-par with the much more complex and computationally demanding WRF-WVT model.

Dominguez, Francina↗
DOE OSTI2025

Accurate noncovalent interactions in atomistic systems via quantum Drude oscillators

Accurately modeling polarization and van der Waals (vdW) interactions in atomistic systems typically requires high-level quantum-mechanical methods that are computationally expensive, hence limited in applicability. To address this challenge, efficient yet physically grounded models are needed—ones that not only enable accurate predictions but also provide insight into how noncovalent interactions scale in complex molecular and material systems. This review highlights the quantum Drude oscillator (QDO) model, a physically motivated and computationally efficient framework that captures the essential features of electronic response, including polarization and dispersion forces, across a wide range of chemical and material systems. We discuss how the QDO model quantitatively reproduces the polarization response of many-electron atoms and how key components of noncovalent interactions—exchange-repulsion, polarization, and dispersion—emerge naturally in QDO dimers. Furthermore, the model provides predictive scaling laws that elucidate trends in polarizability and dispersion across the periodic table and in molecular assemblies. By uniting interpretability, accuracy, and efficiency, the QDO model offers a versatile approach for modeling noncovalent interactions in systems ranging from isolated molecules to complex condensed phases and nanostructured materials.

Khabibrakhmanov, Almaz [Univ. of Luxembourg, Luxem↗