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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Plasmonic Nonlinear Energy Transfer Enhanced Second Harmonic Generation Nanoscopy

Nonlinear optical response is a fingerprint of various physicochemical properties of materials related to symmetry, including crystallography, interfacial configuration, and carrier dynamics. However, the intrinsically weak nonlinear optical susceptibility and the diffraction limit of far-field optics restrict probing deep-subwavelength-scale nonlinear optics with measurable signal-to-noise ratio. Here, we propose an alternative approach toward efficient second harmonic generation (SHG) nanoscopy for SHG-active sample (zinc oxide nanowire; ZnO NW) using an SHG-active plasmonic nanotip. Our full-wave simulation suggests that the experimentally observed high near-field SHG contrast is possible when the nonlinear response of ZnO NW is enhanced and/or that of the tip is suppressed. Here, this result suggests possible evidence of quantum mechanical nonlinear energy transfer between the tip and the sample, modifying the nonlinear optical susceptibility. Further, this process probes the nanoscale corrosion of ZnO NW, demonstrating potential use in studying various physicochemical phenomena in nanoscale resolution.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Photon acceleration of high-intensity vector vortex beams into the extreme ultraviolet

Extreme ultraviolet (XUV) light sources allow for the probing of bound electron dynamics on attosecond scales, interrogation of high-energy-density matter, and access to novel regimes of strong-field quantum electrodynamics. Despite the importance of these applications, coherent XUV sources remain relatively rare, and those that do exist are limited in their peak intensity and spatio-polarization structure. Here, we demonstrate that photon acceleration of an optical vector vortex pulse in the moving density gradient of an electron beam–driven plasma wave can produce a high-intensity, tunable-wavelength XUV pulse with the same vector vortex structure as the original pulse. Quasi-3D, boosted-frame particlein- cell simulations show the transition of optical vector vortex pulses with 800-nm wavelengths and intensities below 10 18 W/cm 2 to XUV vector vortex pulses with 36-nm wavelengths and intensities exceeding 10 20 W/cm 2 over a distance of 1.2 cm. The XUV pulses have sub-femtosecond durations and nearly flat phase fronts. The production of such high-quality, high-intensity XUV vector vortex pulses could expand the utility of XUV light as a diagnostic and driver of novel light–matter interactions.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Lessons from combined experimental and theoretical examination of the FTIR and 2D-IR spectroelectrochemistry of the amide I region of cytochrome c</em

Amide I difference spectroscopy is widely used to investigate protein function and structure changes. In this article, we show that the common approach of assigning features in amide I difference signals to distinct secondary structure elements in many cases may not be justified. Evidence comes from Fourier transform infrared (FTIR) and 2D-IR spectroelectrochemistry of the protein cytochrome c in the amide I range, in combination with computational spectroscopy based on molecular dynamics (MD) simulations. This combination reveals that each secondary structure unit, such as an alpha-helix or a beta-sheet, exhibits broad overlapping contributions, usually spanning a large part of the amide I region, which in the case of difference absorption experiments (such as in FTIR spectroelectrochemistry) may lead to intensity-compensating and even sign-changing contributions. We use cytochrome c as the test case, as this small electron-transferring redox-active protein contains different kinds of secondary structure units. Upon switching its redox-state, the protein exhibits a different charge distribution while largely retaining its structural scaffold. Our theoretical analysis suggests that the change in charge distribution contributes to the spectral changes and that structural changes are small. However, in order to confidently interpret FTIR amide I difference signals in cytochrome c and proteins in general, MD simulations in combination with additional experimental approaches such as isotope labeling, the insertion of infrared labels to selectively probe local structural elements will be required. In case these data are not available, a critical assessment of previous interpretations of protein amide I 1D- and 2D-IR difference spectroscopy data is warranted.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Effects of Ligand Chemistry on Ion Transport in 2D Hybrid Organic–Inorganic Perovskites

2D hybrid organic–inorganic perovskites are potentially promising materials as passivation layers that can enhance the efficiency and stability of perovskite photovoltaics. The ability to suppress ion transport is proposed as a stabilization mechanism, yet an effective characterization of relevant modes of halide diffusion in 2D perovskites is nascent. In light of this knowledge gap, molecular dynamics simulations with enhanced sampling and experimental validation to systematically characterize how ligand chemistry in seven (R-NH 3 ) 2 PbI 4 systems impacts halide diffusion, particularly in the out-of-plane direction is combined. It is found that increasing stiffness and length of ligands generally inhibits ion transport, while increasing ligand polarization generally enhances it. Structural and energetic analyses of the migration pathways provide quantitative explanations for these trends, which reflect aspects of the disorder of the organic layer. Overall, this mechanistic analysis greatly enhances the current understanding of halide migration in 2D hybrid organic–inorganic perovskites and yields insights that can inform the design of future passivation materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Electron‐Assisted Generation and Straight Movement of Skyrmion Bubble in Kagome TbMn 6 Sn 6

Abstract Topological magnetic textures are promising candidates as binary data units for the next‐generation memory device. The precise generation and convenient control of nontrivial spin topology at zero field near room temperature endows the critical advantages in skyrmionic devices but is not simultaneously integrated into one material. Here, in the Kagome plane of quantum TbMn 6 Sn 6 , the expedient generation of the skyrmion bubbles in versatile forms of lattice, chain, and isolated one by converging the electron beam, where the electron intensity gradient contributes to the dynamic generation from local anisotropy variation near spin reorientation transition (SRT) is reported. Encouragingly, by utilizing the dynamic shift of the SRT domain interface, the straight movement is actualized with the skyrmion bubble slave to the SRT domain interface forming an elastic composite object, avoiding the usual deflection from the skyrmion Hall effect. The critical contribution of the SRT domain interface via conveniently electron‐assisted heating is further theoretically validated in micromagnetic simulation, highlighting the compatible application possibility in advanced devices.

Li, Zhuolin↗
DOE OSTI2023

Characterizing the Mechanisms of Ca and Mg Carbonate Ion-Pair Formation with Multi-Level Molecular Dynamics/Quantum Mechanics Simulations

The carbonate minerals of Ca and Mg are abundant throughout the lithosphere and have recently garnered significant research interest as possible long-term carbon sinks in the sequestration of atmospheric carbon dioxide. Nonetheless, an understanding of the atomic-level processes comprising their mineralization remains limited. Furthermore, we characterize and contrast the mechanisms of contact ion-pair formation in aqueous Ca and Mg carbonate systems, which represents the most fundamental step leading to the formation of their mineral solids. Utilizing multilevel embedded correlated wavefunction-based ab initio molecular dynamics/quantum mechanics simulations, we characterize not only the dynamics of these processes but also factors arising from the electronic structure of the involved species, revealing further details of the fundamentally different mechanisms for the interconversion between the contact ion-pairs and solvent-shared ion-pairs of Ca versus Mg carbonate.

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

Artificial neural network potentials for mechanics and fracture dynamics of two-dimensional crystals **

Understanding the mechanics and failure of materials at the nanoscale is critical for their engineering and applications. The accurate atomistic modeling of brittle failure with crack propagation in covalent crystals requires a quantum mechanics-based description of individual bond-breaking events. Artificial neural network potentials (NNPs) have emerged to overcome the traditional, physics-based modeling tradeoff between accuracy and accessible time and length scales. Previous studies have shown successful applications of NNPs for describing the structure and dynamics of molecular systems and amorphous or liquid phases of materials. However, their application to deformation and failure processes in materials is still uncommon. In this study, we discuss the apparent limitations of NNPs for the description of deformation and fracture under loadings and propose a way to generate and select training data for their employment in simulations of deformation and fracture simulations of crystals. We applied the proposed approach to 2D crystalline graphene, utilizing the density-functional tight-binding method for more efficient and extensive data generation in place of density functional theory. Then, we explored how the data selection affects the accuracy of the developed artificial NNPs. It revealed that NNP’s reliability should not only be measured based on the total energy and atomic force comparisons for reference structures but also utilize comparisons for physical properties, e.g. stress–strain curves and geometric deformation. In sharp contrast to popular reactive bond order potentials, our optimized NNP predicts straight crack propagation in graphene along both armchair and zigzag (ZZ) lattice directions, as well as higher fracture toughness of ZZ edge direction. Our study provides significant insight into crack propagation mechanisms on atomic scales and highlights strategies for NNP developments of broader materials.

2D materials↗
DOE OSTI2025

Microwave microscope studies of trapped vortex dynamics in superconductors

Trapped vortices in superconductors introduce residual resistance in superconducting radio-frequency (SRF) cavities and disrupt the operation of superconducting quantum and digital electronic circuits. Understanding the detailed dynamics of trapped vortices under oscillating magnetic fields is essential for advancing these technologies. We have developed a near-field magnetic microwave microscope to study the dynamics of a limited number of trapped vortices under the probe when stimulated by a localized rf magnetic field. By measuring the local second-harmonic response (𝑃 2⁢f ) at subfemto-Watt levels, we isolate signals exclusively arising from trapped vortices, excluding contributions from surface defects and Meissner screening currents. Toy models of niobium superconductor hosting vortex pinning sites are introduced and studied with time-dependent Ginzburg-Landau (TDGL) simulations of probe/sample interaction to better understand the measured second-harmonic response. The simulation results demonstrate that the second-harmonic response of trapped vortex motion under a localized rf magnetic field shares key features with the experimental data. Here, this measurement technique provides access to vortex dynamics at the micrometer scale, such as depinning events and spatially resolved pinning properties, as demonstrated in measurements on a niobium film with an antidot flux pinning array.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Enhanced radiative and thermal properties from surface encapsulation of InAs nanowires

In this work, photoluminescence (PL), quantum efficiency and carrier dynamics are investigated in indium arsenide (InAs) nanowires (NWs) with various surface treatments, including a molecular beam epitaxy (MBE)-grown semiconductor shell passivation, sulfur-passivation, alumina (Al 2 O 3 ) coating by atomic layer deposition (ALD) and polydimethylsiloxane (PDMS) spin-coating. The ALD-dielectric layer-coated InAs core-shell NWs show a maximum 13-fold increase in PL intensity. In contrast to the previous reports, this enhancement is found to be due to increased radiative rate from an enhanced Purcell factor, better thermal conductance and higher carrier injection within the NWs instead of improved surface quality. Numeric simulations confirm the experimentally observed increased radiative rate. Further improvements are suggested with even thicker capped InAs NWs. Carrier lifetime in surface-treated NWs is extended and shows long-term stability, critical for practical devices.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Next-Generation Materials Design: Quantum Mechanics and Data-Driven Modeling

The future of materials design is rapidly advancing through the combination of quantum mechanics and data-driven modeling. These approaches integrate quantum principles with advanced data analysis, enabling precise insights into material behavior. This talk will highlight recent progress in using these methods for computational design, particularly in high-entropy alloy catalysts, emphasizing the role of hierarchical machine-learning architectures for accurate predictions. Additionally, I will discuss our work on developing machine learning interatomic potentials (MLPs) for single-element metals, metal oxides, and alloys under extreme conditions, focusing on melting behavior and phase properties at high temperatures and pressures. We have also refined our MLP models to capture dynamic surface interactions, such as CO2 and CO adsorption on MgO, using both static and molecular dynamics simulations. These models maintain high accuracy while significantly reducing computational costs compared to first-principles calculations. By enabling efficient and accurate simulations, this work supports broader community adoption, optimizes datasets for materials discovery, and extends the accessible time, size, and environmental conditions beyond the limits of experiments and traditional simulations.

machine learning↗
DOE OSTI2021

High-speed impact of micron-sized diesel drop trains—Splashing dynamics, secondary droplet formation, and effects of pre-existing film thickness

This work investigates surface impingement of monodispersed diesel drop trains using computational simulations. The three-dimensional simulations include high-speed impact of micrometer-sized fuel drops onto initially dry and wetted stainless steel substrates. The drop size and impact velocity are representative of fuel injection conditions in internal combustion engines (ICEs). The drop trains serve as a simplified representation of fuel spray. To accurately capture the film that naturally develops on initially dry substrates, a dynamic diesel-stainless steel contact angle model was developed using experiments of single drop impact under ICE representative temperature. Simulations are presented for a highly splashing case and the effects of a pre-existing film on splashing dynamics were investigated, including the temporal evolution of splashed mass and film thickness. It was concluded that for thin films, the effect of pre-existing film thickness is less significant than in single drop impingement. Secondary droplet characterization was performed on simulation results, leading to instantaneous and time-averaged distributions of secondary droplet size, velocity magnitude, and trajectory angle. It was found that for each drop impingement, approximately 58% of the splashed mass is from the impinging drop itself, while the remainder of the splashed mass is composed of film liquid. From a high resolution case, details of secondary droplet formation are observed and three distinct phases of secondary droplet formation are identified. The detailed analysis of drop train impingement under engine-relevant conditions serves as a first step toward a robust understanding of fuel impingement and the development of cleaner and more efficient ICEs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Thermalization and criticality on an analogue–digital quantum simulator

Abstract Understanding how interacting particles approach thermal equilibrium is a major challenge of quantum simulators 1,2 . Unlocking the full potential of such systems towards this goal requires flexible initial state preparation, precise time evolution and extensive probes for final state characterization. Here we present a quantum simulator comprising 69 superconducting qubits that supports both universal quantum gates and high-fidelity analogue evolution, with performance beyond the reach of classical simulation in cross-entropy benchmarking experiments. This hybrid platform features more versatile measurement capabilities compared with analogue-only simulators, which we leverage here to reveal a coarsening-induced breakdown of Kibble–Zurek scaling predictions 3 in theXYmodel, as well as signatures of the classical Kosterlitz–Thouless phase transition 4 . Moreover, the digital gates enable precise energy control, allowing us to study the effects of the eigenstate thermalization hypothesis 5–7 in targeted parts of the eigenspectrum. We also demonstrate digital preparation of pairwise-entangled dimer states, and image the transport of energy and vorticity during subsequent thermalization in analogue evolution. These results establish the efficacy of superconducting analogue–digital quantum processors for preparing states across many-body spectra and unveiling their thermalization dynamics.

Science & Technology - Other Topics↗
DOE OSTI2025

Teacher-student training improves the accuracy and efficiency of machine learning interatomic potentials

Machine learning interatomic potentials (MLIPs) are revolutionizing the field of molecular dynamics (MD) simulations. Recent MLIPs have tended towards more complex architectures trained on larger datasets. The resulting increase in computational and memory costs may prohibit the application of these MLIPs to perform large-scale MD simulations. Herein, we present a teacher-student training framework in which the latent knowledge from the teacher (atomic energies) is used to augment the students' training. We show that the light-weight student MLIPs have faster MD speeds at a fraction of the memory footprint compared to the teacher models. Remarkably, the student models can even surpass the accuracy of the teachers, even though both are trained on the same quantum chemistry dataset. Our work highlights a practical method for MLIPs to reduce the resources required for large-scale MD simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Neural-network model for force prediction in multi-principal-element alloys

Atomistic simulations can provide useful insights into the physical properties of multi-principal-element alloys. However, classical potentials mostly fail to capture key quantum (electronic-structure) effects. Here we present a deep 3D convolutional neural network (3D CNN) based framework combined with a voxelization technique to design interatomic potentials for chemically complex alloys. We highlight the performance of the 3D CNN model and its efficacy in computing potentials using the medium-entropy alloy TaNbMo. In order to provide insights into the effect of voxel resolution, we implemented two approaches based on the inner and outer bounding boxes. An efficient 3D CNN model, which is as accurate as the density-functional theory (DFT) approach, for calculating potentials will provide a promising schema for accurate atomistic simulations of structure and dynamics of general multi-principle element alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Dynamically suppressing cavity dephasing induced by frequency fluctuations of a coupled nonlinear mode

High-coherence superconducting cavities offer a promising platform for quantum information, with long coherence times and negligible intrinsic dephasing. However, cavity control generally relies on nonlinear Josephson elements whose frequency fluctuations are inherited by the cavity as dephasing, potentially limiting control fidelities and eroding the noise bias used in error-correction protocols. Here, we introduce Stark-Assisted Flux-noise Evasion (SAFE), a hardware-efficient protocol that protects the cavity from inherited dephasing using only a weak off-resonant microwave drive applied to the nonlinear element. As a concrete setup, we analyze a 3D superconducting cavity dispersively coupled to a flux-tunable transmon (FTT) subject to $1/f$ flux noise. Analytical predictions are confirmed by Monte Carlo simulations with realistic parameters, which show that SAFE can extend the cavity dephasing time by more than an order of magnitude while keeping residual drive-induced decoherence subdominant.

Lu, Yunwei [Northwestern U.] (ORCID:00000003401756↗
DOE OSTI2025

Numerical simulations of liquid jetting with solid inclusions

The dynamics of finite-sized particles in fluids, and their influence on the overall flow, are of great interest across several industrial, environmental, and medical fields. In the context of inkjet printing, the presence of solid inclusions can be either intentional, as in additive manufacturing, or unintentional, as in standard printing processes. These inclusions can strongly impact the jetting process, causing effects such as jet asymmetry, bubble entrapment, and the formation of satellite droplets. Understanding and controlling particle behavior is therefore essential, particularly to predict how and when particles are ejected over multiple jetting cycles. It is therefore critical to develop reliable models that allow for a deeper understanding of the complex interplay between particle and fluid during the whole printing process. To address this, we present a tailored implementation of the Color-Gradient multicomponent Lattice Boltzmann Method for fully resolved three-dimensional (3D) simulations of multicycle liquid jetting with particles. Our method supports realistic parameter settings aligned with industrial inkjet systems, and we provide both qualitative and quantitative validation against experimental data. Additionally, we introduce a simplified model based on the Stokes drag law, in which solid particles are represented as point particles and do not influence the fluid flow. Despite this limitation, the model offers a computationally efficient means to explore the vast parameter space typically encountered in industrial applications, allowing, e.g., identifying critical ejection regions and estimating the number of cycles required for particle release. These qualitative insights are valuable for guiding and complement fully two-way coupled simulations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Coarse-Graining Conformational Dynamics with Multidimensional Generalized Langevin Equation: How, When, and Why

A data-driven ab initio generalized Langevin equation (AIGLE) approach is developed to learn and simulate high-dimensional, heterogeneous, coarse-grained (CG) conformational dynamics. Constrained by the fluctuation–dissipation theorem, the approach can build CG models in dynamical consistency (DC) with all-atom molecular dynamics. Here, we also propose practical criteria for AIGLE to enforce long-term DC. Case studies of a toy polymer, with 20 CG sites, and the alanine dipeptide, with two dihedral angles, elucidate why one should adopt AIGLE or its Markovian limit for modeling CG conformational dynamics in practice.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Interstitial proton transport through defective MXenes

Proton transport across nanometer-thick membranes in an aqueous medium is important for applications in energy and molecular sieving. Recently, Hu et al. [Nature 516(7530), 227–230 (2014)] experimentally demonstrated proton tunneling through 2D materials like graphene and hexagonal boron nitride, opening up a wide range of applications in hydrogen-based technologies such as fuel cells. Here, we demonstrate proton transport in an aqueous medium across a 2D cubic Ti 2 C membrane, a representative defective MXene, from ab initio molecular dynamics simulations. We observe bidirectional translocation of protons, which occurs through the interstitial vacancies in the surface. We show from our simulations that water dissociates on the membrane and the dissociated proton moves into the interstitial sites in the membrane. The proton hops from interstitial-to-interstitial and transports across the membrane. Here, we also show that this interstitial proton transport is associated with an induced electric field that is modulated with bidirectional transport of protons across the membrane.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗