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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Structural and electronic characteristics of amorphous Ge 8 Sb 2 Te 11

GeTe-rich phase-change materials can be utilized in rewriteable optical memory due to the large contrast in reflectivity between amorphous and crystalline phases. Here we explored the structure and electronic properties of amorphous Ge 8 Sb 2 Te 11 using ab initio molecular dynamics simulations. The results indicate that amorphous Ge 8 Sb 2 Te 11 is dominantly composed of Ge-, Sb- and Te-centered octahedrons with distortions, while 30.4 % of Ge-centered clusters are in the form of tetrahedrons which are randomly distributed. The 5-fold rings possess a large proportion, and the Gesingle bondTe and Sbsingle bondTe bonds present larger formation energies than other bonds, leading to the ABAB bonding sequence (A: Ge and Sb, B: Te). The lone pair electrons locating at the opposite direction of bonds possess a large fraction of 14.8 %, which may enhance the distortions of local clusters. Importantly, these structural properties lead to the high stability of amorphous Ge 8 Sb 2 Te 11 and thus long data retention in the information storage.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Monte Carlo Explicitly Correlated Second-Order Many-Body Green’s Function Calculations of Semiconductor Band Gaps

A systematically converging series of ab initio, post-density-functional, size-consistent, electron-correlated approximations is desired for predictive computing of felectronic band structures of insulating, semiconducting, and metallic solids. A series that meets all of these desiderata (except the applicability to metals) is ab initio many-body Green's function theory based on Gaussian-type-orbital (GTO) basis sets. Here, its leading-order approximation, the second-order Green's function (GF2) method in the diagonal and frequency-independent approximations with the aug-cc-pVDZ basis set, is applied to the fundamental band gaps of three semiconductors (diamond, silicon, and silicon carbide in the zincblende structure) using cluster models. Corrections are made to the basis-set-incompleteness errors by the explicit-correlation (F12) ansatz (GF2-F12) for the valence band edges. The crystals are modeled as surface-passivated clusters of increasing sizes, whose wave functions are expanded by up to 2709 GTO basis functions. Immense computational costs of these calculations are overcome by the highly scalable stochastic algorithm of the Monte Carlo GF2-F12 method, whose operation cost per state increases only as a cubic power of system size, which has a tiny memory footprint and easily achieves near-perfect parallel efficiency on thousands of CPUs or on hundreds of GPUs. The correlated, F12-corrected highest-occupied and lowest-unoccupied molecular-orbital energy (HOMO-LUMO) gap is 5.78 ± 0.07 eV for C 87 H 76 as compared with the experimental value of the fundamental (indirect) band gap of bulk diamond at 5.48 eV. The correlated, F12-corrected HOMO-LUMO gaps for Si 75 H 76 and Si 32 C 43 H 76 are 2.56 ± 0.15 eV and 3.50 ± 0.12 eV, respectively, which are expected to decrease further with increasing cluster sizes. As a result, the experimental fundamental (indirect) band gaps of bulk silicon and silicon carbide are 1.17 eV and 2.42 eV, respectively.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Spinodal enhancement of fluctuations in nucleus-nucleus collisions

Subensemble Acceptance Method (SAM) [1, 2] is an essential link between measured event-by-event fluctuations and their grand canonical theoretical predictions such as lattice QCD. The method allows quantifying the global conservation law effects in fluctuations. In its basic formulation, SAM requires a sufficiently large system such as created in central nucleus-nucleus collisions and sufficient space-momentum correlations. Directly in the spinodal region of the First Order Phase Transition (FOPT) different approximations should be used that account for finite size effects. Thus, we present the generalization of SAM applicable in both the pure phases, metastable and unstable regions of the phase diagram [3]. Obtained analytic formulas indicate the enhancement of fluctuations due to crossing the spinodal region of FOPT and are tested using molecular dynamics simulations. A rather good agreement is observed. Using transport model calculations with interaction potential we show that the spinodal enhancement of fluctuations survives till the later stages of collision via the memory effect [4]. However, at low collision energies the space-momentum correlation is not strong enough for this signal to be transferred to second and third order cumulants measured in momentum subspace. This result agrees well with recent HADES data on proton number fluctuations at $\sqrt{S_{NN}}$ = 2.4 GeV which are found to be consistent with the binomial momentum space acceptance [5].

Poberezhnyuk, Roman↗
DOE OSTI2022

Optimized low-depth quantum circuits for molecular electronic structure using a separable-pair approximation

We present a classically tractable model that leads to optimized low-depth quantum circuits leveraging separable-pair approximations. The obtained circuits are well suited as a baseline circuit for emerging quantum hardware and can, in the long term, provide significantly improved initial states for quantum algorithms. The associated wave functions can be represented with linear memory requirement, which allows classical optimization of the circuits and naturally defines a minimum benchmark for quantum algorithms. In this work we employ directly determined pair-natural orbitals within a basis-set-free approach. This leads to accurate representation of the one- and many-body parts for weakly correlated systems and we explicitly illustrate how the model can be integrated into other quantum algorithms for stronger correlated systems.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

A multilayer network analysis of Alzheimer's disease pathogenesis: Roles for p‐tau, synaptic peptides, and physical activity

INTRODUCTION: In the aging brain, cognitive abilities emerge from the coordination of complex pathways arising from a balance between protective lifestyle and environmental factors and accumulation of neuropathologies. METHODS: As part of the Rush Memory and Aging Project (n = 440), we measured accelerometer-based actigraphy, cognitive performance, and after brain autopsy, selected reaction monitoring mass spectrometry. Multilevel network analysis was used to examine the relationships among the molecular machinery of vesicular neurotransmission, Alzheimer's disease (AD) neuropathology, cognition, and late-life physical activity. RESULTS: Synaptic peptides involved in neuronal secretory function were the most influential contributors to the multilayer network, reflecting the complex interdependencies among AD pathology, synaptic processes, and late-life cognition. Older adults with lower physical activity evidenced stronger adverse relationships among phosphorylated tau peptides, markers of synaptic integrity, and tangle pathology. DISCUSSION: Network-based approaches simultaneously model interdependent biological processes and advance understanding of the role of physical activity in age-associated cognitive impairment. Highlights: Network-based approaches simultaneously model interdependent biological processes. Secretory synaptic peptides were influential contributors to the multilayer network. Older adults with lower physical activity had adverse relationships among pathology. There was interdependence among phosphorylated tau, synaptic integrity, and tangles. Network methods elucidate the role of physical activity in cognitive impairment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

In-depth quantitative proteomic characterization of organotypic hippocampal slice culture reveals sex-specific differences in biochemical pathways

Sex differences in the brain of mammals range from neuroarchitecture through cognition to cellular metabolism. The hippocampus, a structure mostly associated with learning and memory, presents high vulnerability to neurodegeneration and aging. Therefore, we explored basal sex-related differences in the proteome of organotypic hippocampal slice culture, a major in vitro model for studying the cellular and molecular mechanisms related to neurodegenerative disorders. Results suggest a greater prevalence of astrocytic metabolism in females and significant neuronal metabolism in males. The preference for glucose use in glycolysis, pentose phosphate pathway and glycogen metabolism in females and high abundance of mitochondrial respiration subunits in males support this idea. An overall upregulation of lipid metabolism was observed in females. Upregulation of proteins responsible for neuronal glutamate and GABA synthesis, along with synaptic associated proteins, were observed in males. In general, the significant spectrum of pathways known to predominate in neurons or astrocytes, together with the well-known neuronal and glial markers observed, revealed sex-specific metabolic differences in the hippocampus. TEM qualitative analysis might indicate a greater presence of mitochondria at CA1 synapses in females. These findings are crucial to a better understanding of how sex chromosomes can influence the physiology of cultured hippocampal slices and allow us to gain insights into distinct responses of males and females on neurological diseases that present a sex-biased incidence.

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

Lithiating magneto-ionics in a rechargeable battery

Magneto-ionics, real-time ionic control of magnetism in solid-state materials, promise ultralow-power memory, computing, and ultralow-field sensor technologies. The real-time ion intercalation is also the key state-of-charge feature in rechargeable batteries. Here, we report that the reversible lithiation/delithiation in molecular magneto-ionic material, the cathode in a rechargeable lithium-ion battery, accurately monitors its real-time state of charge through a dynamic tunability of magnetic ordering. The electrochemical and magnetic studies confirm that the structural vacancy and hydrogen-bonding networks enable reversible lithiation and delithiation in the magnetic cathode. Coupling with microwave-excited spin wave at a low frequency (0.35 GHz) and a magnetic field of 100 Oe, we reveal a fast and reliable built-in magneto-ionic sensor monitoring state of charge in rechargeable batteries. The findings shown herein promise an integration of molecular magneto-ionic cathode and rechargeable batteries for real-time monitoring of state of charge.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Proteo-Genomic Analysis Identifies Two Major Sites of Vulnerability on Ebolavirus Glycoprotein for Neutralizing Antibodies in Convalescent Human Plasma

Three clinically relevant ebolaviruses – Ebola (EBOV), Bundibugyo (BDBV), and Sudan (SUDV) viruses, are responsible for severe disease and occasional deadly outbreaks in Africa. The largest Ebola virus disease (EVD) epidemic to date in 2013-2016 in West Africa highlighted the urgent need for countermeasures, leading to the development and FDA approval of the Ebola virus vaccine rVSV-ZEBOV (Ervebo ® ) in 2020 and two monoclonal antibody (mAb)-based therapeutics (Inmazeb ® [atoltivimab, maftivimab, and odesivimab-ebgn] and Ebanga ® (ansuvimab-zykl) in 2020. The humoral response plays an indispensable role in ebolavirus immunity, based on studies of mAbs isolated from the antibody genes in peripheral blood circulating ebolavirus-specific human memory B cells. However, antibodies in the body are not secreted by circulating memory B cells in the blood but rather principally by plasma cells in the bone marrow. Little is known about the protective polyclonal antibody responses in convalescent plasma. Here we exploited both single-cell antibody gene sequencing and proteomic sequencing approaches to assess the composition of the ebolavirus glycoprotein (GP)-reactive antibody repertoire in the plasma of an EVD survivor. We first identified 1,512 GP-specific mAb variable gene sequences from single cells in the memory B cell compartment. Using mass spectrometric analysis of the corresponding GP-specific plasma IgG, we found that only a portion of the large B cell antibody repertoire was represented in the plasma. Molecular and functional analysis of proteomics-identified mAbs revealed recognition of epitopes in three major antigenic sites - the GP head domain, the glycan cap, and the base region, with a high prevalence of neutralizing and protective mAb specificities that targeted the base and glycan cap regions on the GP. Polyclonal plasma antibodies from the survivor reacted broadly to EBOV, BDBV, and SUDV GP, while reactivity of the potently neutralizing mAbs we identified was limited mostly to the homologous EBOV GP. Together these results reveal a restricted diversity of neutralizing humoral response in which mAbs targeting two antigenic sites on GP – glycan cap and base – play a principal role in plasma-antibody-mediated protective immunity against EVD.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

How arsenic makes amorphous GeSe a robust chalcogenide glass for advanced memory integration

The 3D integration technology in semiconductor fabrication requires a key component, the ovonic threshold switching (OTS) selector, to suppress the current leakage. The As doped amorphous (a-) GeSe glass is a commercialized OTS material in 3D phase-change memory, but the understanding of such a doping mechanism is still inadequate. Here we systematically explore the effect of As doping on the structural, bonding, and dynamics properties of a-GeAsSe using ab initio molecular dynamics simulations. The results reveal that As atoms form strong bonds with both Ge and Se atoms. The distorted octahedral structures and the 5-fold rings linked by atoms are increased. All of these structural features lead to a more disordered configuration. Moreover, as atoms have notably slowed down the atomic mobility, rendering a-GeAsSe a high stability. Overall, our studies offer insightful understanding of As-doping in OTS materials, paving the way for the design and application of advanced selector devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Accelerating charge estimation in molecular dynamics simulations using physics-informed neural networks: corrosion applications

Molecular Dynamics (MD) simulations are used to understand the effects of corrosion on metallic materials in salt brine. Reactive force fields in classical MD enable accurate modeling of bond formation and breakage in the aqueous medium and at the metal-electrolyte interface, while also facilitating dynamic partial charge equilibration. However, MD simulations are computationally intensive and unsuitable for modeling the long time scales characteristic of corrosive phenomena. To address this, we develop reduced-order machine learning models that provide accurate and efficient predictions of charge density in corrosive environments. Specifically, we use Long Short-Term Memory (LSTM) networks to forecast charge density evolution based on atomic environments represented by Smooth Overlap of Atomic Positions (SOAP) descriptors. A physics-informed loss function enforces charge neutrality and electronegativity equivalence. The atomic charges predicted by the deep learning model trained on this work were obtained two orders of magnitude faster than those from molecular dynamics (MD) simulations, with an error of less than 3% compared to the MD-obtained charges, even in extrapolative scenarios, while adhering to physical constraints. This demonstrates the excellent accuracy, computational efficiency, and validity of the developed model. Lastly, even though developed for corrosion, these protocols are formulated in a phenomenon-agnostic manner, allowing application to various variable-charge interatomic potentials and related fields.

Atomistic models↗
DOE OSTI2025

Hybrid MBE Route to Adsorption-Controlled Growth of BaTiO 3 Membranes with Robust Polarization Switching

Freestanding ferroelectric membranes are promising for flexible electronics, nonvolatile memory, photonics, and spintronics, but their synthesis is challenged by the need for reproducibility with precise stoichiometric control. Here, we demonstrate the adsorption-controlled growth of single-crystalline, epitaxial BaTiO 3 films by hybrid molecular beam epitaxy (MBE) on a binary oxide sacrificial layer. Using a simple water-droplet lift-off method, we obtained submillimeter- to millimeter-sized membranes that retained crystallinity, as confirmed by high-resolution X-ray diffraction, and exhibited robust tetragonal symmetry, as verified by Raman spectroscopy. Impedance spectroscopy confirmed a high dielectric constant of ∼1340, reflecting the robust dielectric response of the membranes. Ferroelectric functionality was revealed by piezoresponse force microscopy (PFM) and further verified by polarization-electric field (P-E) loop measurements with Positive-Up-Negative-Down (PUND). The P-E loops exhibited a remnant polarization of ∼5 μC cm –2 and a coercive field of ∼63 kV cm –1 . Furthermore, these results were interpreted in relation to c- and a-domain configurations.

Raman spectroscopy↗
DOE OSTI2025

Oxidative stress is a shared characteristic of ME/CFS and Long COVID

Over 65 million individuals worldwide are estimated to have Long COVID (LC), a complex multisystemic condition marked by fatigue, post-exertional malaise, and other symptoms resembling myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). With no clinically approved treatments or reliable diagnostic markers, there is an urgent need to define the molecular underpinnings of these conditions. By studying bioenergetic characteristics of peripheral blood lymphocytes in 25 healthy controls, 27 ME/CFS, and 20 LC donors, we find both ME/CFS and LC donors exhibit signs of elevated oxidative stress, especially in the memory subset. Using a combination of flow cytometry, RNA-seq, mass spectrometry, and systems chemistry analysis, we observed aberrations in reactive oxygen species (ROS) clearance pathways including elevated glutathione levels, decreases in mitochondrial superoxide dismutase protein levels, and glutathione peroxidase 4–mediated lipid oxidative damage. Strikingly, these redox pathways changes show sex-specific trends. While ME/CFS females exhibit higher total ROS and mitochondrial calcium levels, males have normal ROS levels, with pronounced mitochondrial lipid oxidative damage. In females, these higher ROS levels correlate with T cell hyperproliferation, consistent with the known role of elevated ROS in initiating proliferation. This hyperproliferation can be attenuated by metformin, suggesting this Food and Drug Administration (FDA)-approved drug as a possible treatment, as also suggested by a recent clinical study of LC patients. Moreover, these results suggest a shared mechanistic basis for the systemic phenotypes of ME/CFS and LC, which can be detected by quantitative blood cell measurements, and that effective, patient-tailored drugs might be discovered using standard lymphocyte stimulation assays.

ME/CFS↗
DOE OSTI2025

Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques

Opioids exert their analgesic effect by binding to the µ opioid receptor (MOR), which initiates a downstream signaling pathway, eventually inhibiting pain transmission in the spinal cord. However, current opioids are addictive, often leading to overdose contributing to the opioid crisis in the United States. Therefore, understanding the structure-activity relationship between MOR and its ligands is essential for predicting MOR binding of chemicals, which could assist in the development of non-addictive or less-addictive opioid analgesics. This study aimed to develop machine learning and deep learning models for predicting MOR binding activity of chemicals. Chemicals with MOR binding activity data were first curated from public databases and the literature. Molecular descriptors of the curated chemicals were calculated using software Mold2. The chemicals were then split into training and external validation datasets. Random forest, k-nearest neighbors, support vector machine, multi-layer perceptron, and long short-term memory models were developed and evaluated using 5-fold cross-validations and external validations, resulting in Matthews correlation coefficients of 0.528–0.654 and 0.408, respectively. Furthermore, prediction confidence and applicability domain analyses highlighted their importance to the models’ applicability. Our results suggest that the developed models could be useful for identifying MOR binders, potentially aiding in the development of non-addictive or less-addictive drugs targeting MOR.

Research & Experimental Medicine↗
DOE OSTI2021

A diagrammatic approach for automatically deriving analytical gradients of tensor hyper-contracted electronic structure methods

In this work, we introduce a diagrammatic approach to facilitate the automatic derivation of analytical nuclear gradients for tensor hyper-contraction (THC) based electronic structure methods. The automatically derived gradients are guaranteed to have the same scaling in terms of both operation count and memory footprint as the underlying energy calculations, and the computation of a gradient is roughly three times as costly as the underlying energy. The new diagrammatic approach enables the first cubic scaling implementation of nuclear derivatives for THC tensors fitted in molecular orbital basis (MO-THC). Furthermore, application of this new approach to THC-MP2 analytical gradients leads to an implementation, which is at least four times faster than the previously reported, manually derived implementation. Finally, we apply the new approach to the 14 tensor contraction patterns appearing in the supporting subspace formulation of multireference perturbation theory, laying the foundation for developments of analytical nuclear gradients and nonadiabatic coupling vectors for multi-state CASPT2.

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

A parallel strategy for density functional theory computations on accelerated nodes

Using the Löwdin orthonormalization of tall-skinny matrices as a proxy-app for wavefunction-based Density Functional Theory solvers, we investigate a distributed memory parallel strategy focusing on Graphics Processing Unit (GPU)-accelerated nodes as available on some of the top ranked supercomputers at the present time. Here we present numerical results in the strong limit regime, as it is particularly relevant for First-Principles Molecular Dynamics. We also examine how matrix product-based iterative solvers provide a competitive alternative to dense eigensolvers on GPUs, allowing to push the strong scaling limit of these computations to a larger number of distributed tasks. Our strategy, which relies on replicated Gram matrices and efficient collective communications using the NCCL library, leads to a time-to-solution under 0.5 s for the Löwdin orthonormalization of a tall-skinny matrix of 3000 columns on Summit at Oak Ridge Leadership Facility (OLCF). Given the similarity in computational operations between one iteration of a DFT solver and this proxy-app, this shows the possibility of solving accurately the DFT equations well under a minute for 3000 electronic wave functions, and thus perform First-Principles molecular dynamics of physical systems much larger than traditionally solved on CPU systems.

97 MATHEMATICS AND COMPUTING↗