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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 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

PyProcar: A Python library for electronic structure pre/post-processing

The PyProcar Python package plots the band structure and the Fermi surface as a function of site and/or s,p,d,f - projected wavefunctions obtained for each k-point in the Brillouin zone and band in an electronic structure calculation. This can be performed on top of any electronic structure code, as long as the band and projection information is written in the PROCAR format, as done by the VASP and ABINIT codes. PyProcar can be easily modified to read other formats as well. This package is particularly suitable for understanding atomic effects into the band structure, Fermi surface, spin texture, etc. PyProcar can be conveniently used in a command line mode, where each one of the parameters define a plot property. In the case of Fermi surfaces, the package is able to plot the surface with colors depending on other properties such as the electron velocity or spin projection. Furthermore, the mesh used to calculate the property does not need to be the same as the one used to obtain the Fermi surface. A file with a specific property evaluated for each k-point in a k-mesh and for each band can be used to project other properties such as electron–phonon mean path, Fermi velocity, electron effective mass, etc. Another existing feature refers to the band unfolding of supercell calculations into predefined unit cells.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Bound-preserving discontinuous Galerkin methods for conservative phase space advection in curvilinear coordinates

In this study, we extend the positivity-preserving method of Zhang & Shu to simulate the advection of neutral particles in phase space using curvilinear coordinates. The ability to utilize these coordinates is important for non-equilibrium transport problems in general relativity and also in science and engineering applications with specific geometries. The method achieves high-order accuracy using Discontinuous Galerkin (DG) discretization of phase space and strong stability-preserving, Runge-Kutta (SSP-RK) time integration. Special care in taken to ensure that the method preserves strict bounds for the phase space distribution function $f$; i.e., $f\in[0,1]$. The combination of suitable CFL conditions and the use of the high-order limiter proposed in Zhang & Shu (2010) is sufficient to ensure positivity of the distribution function. However, to ensure that the distribution function satisfies the upper bound, the discretization must, in addition, preserve the divergence-free property of the phase space flow. Proofs that highlight the necessary conditions are presented for general curvilinear coordinates, and the details of these conditions are worked out for some commonly used coordinate systems (i.e., spherical polar spatial coordinates in spherical symmetry and cylindrical spatial coordinates in axial symmetry, both with spherical momentum coordinates). Results from numerical experiments --- including one example in spherical symmetry adopting the Schwarzschild metric --- demonstrate that the method achieves high-order accuracy and that the distribution function satisfies the maximum principle.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Stochastic multiscale flux basis for Stokes-Darcy flows

Three algorithms are developed for uncertainty quantification in modeling coupled Stokes and Darcy flows. The porous media may consist of multiple regions with different properties. The permeability is modeled as a non-stationary stochastic variable, with its log represented as a sum of local Karhunen-Loève (KL) expansions. The problem is approximated by stochastic collocation on either tensor-product or sparse grids, coupled with a multiscale mortar mixed finite element method for the spatial discretization. A non-overlapping domain decomposition algorithm reduces the global problem to a coarse scale mortar interface problem, which is solved by an iterative solver, for each stochastic realization. In the traditional domain decomposition implementation, each subdomain solves a local Dirichlet or Neumann problem in every interface iteration. To reduce this cost, two additional algorithms based on deterministic or stochastic multiscale flux basis are introduced. The basis consists of the local flux (or velocity trace) responses from each mortar degree of freedom. It is computed for each subdomain independently before the interface iteration begins. The use of the multiscale flux basis avoids the need for subdomain solves on each iteration. The deterministic basis is computed at each stochastic collocation and used only at this realization. The stochastic basis is formed by further looping over all local realizations of a subdomain's KL region before the stochastic collocation begins. It is used over multiple realizations. Numerical tests are presented to illustrate the performance of the three algorithms, with the stochastic multiscale flux basis showing significant savings in computational cost compared to the other two algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

The role of geographical spreaders in infectious pattern formation and front propagation speeds

The pattern formation and spatial spread of infectious populations are investigated using a kernel-based Susceptible–Infectious–Recovered (SIR) model applicable across a wide range of basic reproduction numbers R O . The goal is to examine the role of geographical spreaders on transient spatial pattern formation of infectious populations and the associated maximum invasive front speeds c max . In the simulations conducted here, geographical spreaders are defined as a portion of the infected population Φ experiencing high mobility between identical communities. The spatial organization of the infected population and c max are determined when the infections are randomly initiated in space within multiple communities. For small but finite , scaling analysis and numerical simulations in 1-dimension suggest that when the spreading kernel is Gaussian-shaped, where is the inverse of the infectious duration. This finding for agrees with a diffusion-based representation of mobility in 1-D. Numerical simulations in 2-D across wide-ranging suggest that , the variance of the spatial kernel describing mobility of long-distance geographical spreaders across communities, determines the spatial organization of infections across communities. When (long-distance mobility, where is the minimum spatial extent defining adjacent communities), the infectious population will experience a transient but spatially coherent pattern with a wavelength that can be derived from the spreading kernel properties. Moreover, the 2-D simulations for the bounded kernel suggest that attainment of is also dictated by but the magnitude is not sensitive to unlike diffusion-based models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

First-Principles Modeling of Mn(II) Migration above and Dissolution from Li x Mn 2 O 4 (001) Surfaces

The density functional theory and ab initio molecular dynamics simulations are applied to investigate the migration of Mn(II) ions to above-surface sites on spinel Li x Mn 2 O 4 (001) surfaces, the subsequent Mn dissolution into the organic liquid electrolyte, and the detrimental effects on graphite anode solid electrolyte interphase (SEI) passivating films after Mn(II) ions diffuse through the separator. The dissolution mechanism proves complex; the much-quoted Hunter disproportionation of Mn(III) to form Mn(II) is far from sufficient. Key steps that facilitate Mn(II) loss include concerted liquid/solid-state motions; proton-induced weakening of Mn–O bonds forming mobile OH – surface groups; and chemical reactions of adsorbed decomposed organic fragments. Mn(II) lodged between the inorganic Li 2 CO 3 and organic lithium ethylene dicarbonate (LEDC) anode SEI components facilitate electrochemical reduction and decomposition of LEDC. Our findings help inform future design of protective coatings, electrolytes, additives, and interfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Graph-EAM: An Interpretable and Efficient Graph Neural Network Potential Framework

The development of deep learning interatomic potentials has enabled efficient and accurate computations in quantum chemistry and materials science, circumventing computationally expensive ab initio calculations. However, the huge number of learnable parameters in deep learning models and their complex architectures hinder physical interpretability and affect the robustness of the derived potential. In this work, we propose graph-EAM, a lightweight graph neural network (GNN) inspired by the empirical embedded atom method to model the interatomic potential of single-element structures. Four material systems: platinum, niobium, silicon, and amorphous-carbon, for which quantum simulation data sets are publicly available, are examined to demonstrate that graph-EAM can achieve high energy and force prediction accuracy-comparable or better than existing state-of-the-art machine learning models-with much fewer parameters. It is also shown that the explicit inclusion of the angular information via three-body atomic density increases the prediction accuracy. In conclusion, the accuracy and efficiency of potentials obtained from graph-EAM can help accelerate the molecular dynamics simulation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Scaling-Relation-Based Analysis of Bifunctional Catalysis: The Case for Homogeneous Bimetallic Alloys

Here, we present a generic analysis of the implications of energetic scaling relations on the possibilities for bifunctional gains at homogeneous bimetallic alloy catalysts. Such catalysts exhibit a large number of interface sites, where second-order reaction steps can involve intermediates adsorbed at different active sites. Using different types of model reaction schemes, we show that such site-coupling reaction steps can provide bifunctional gains that allow for a bimetallic catalyst composed of two individually poor catalyst materials to approach the activity of the optimal monomaterial catalyst. However, bifunctional gains cannot result in activities higher than the activity peak of the monomaterial volcano curve as long as both sites obey similar scaling relations, as is generally the case for bimetallic catalysts. These scaling-relation-imposed limitations could be overcome by combining different classes of materials such as metals and oxides.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Data-driven design of molecular nanomagnets

Three decades of research in molecular nanomagnets have raised their magnetic memories from liquid helium to liquid nitrogen temperature thanks to a wise choice of the magnetic ion and coordination environment. Still, serendipity and chemical intuition played a main role. In order to establish a powerful framework for statistically driven chemical design, here we collected chemical and physical data for lanthanide-based nanomagnets, catalogued over 1400 published experiments, developed an interactive dashboard (SIMDAVIS) to visualise the dataset, and applied inferential statistical analysis. Our analysis shows that the Arrhenius energy barrier correlates unexpectedly well with the magnetic memory. Furthermore, as both Orbach and Raman processes can be affected by vibronic coupling, chemical design of the coordination scheme may be used to reduce the relaxation rates. Indeed, only bis-phthalocyaninato sandwiches and metallocenes, with rigid ligands, consistently present magnetic memory up to high temperature. Analysing magnetostructural correlations, we offer promising strategies for improvement, in particular for the preparation of pentagonal bipyramids, where even softer complexes are protected against molecular vibrations.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Learning heterogeneous reaction kinetics from X-ray videos pixel by pixel

Reaction rates at spatially heterogeneous, unstable interfaces are notoriously difficult to quantify, yet are essential in engineering many chemical systems, such as batteries and electrocatalysts. Experimental characterizations of such materials by operando microscopy produce rich image datasets, but data-driven methods to learn physics from these images are still lacking because of the complex coupling of reaction kinetics, surface chemistry and phase separation. Here we show that heterogeneous reaction kinetics can be learned from in situ scanning transmission X-ray microscopy (STXM) images of carbon-coated lithium iron phosphate (LFP) nanoparticles. Combining a large dataset of STXM images with a thermodynamically consistent electrochemical phase-field model, partial differential equation (PDE)-constrained optimization and uncertainty quantification, we extract the free-energy landscape and reaction kinetics and verify their consistency with theoretical models. We also simultaneously learn the spatial heterogeneity of the reaction rate, which closely matches the carbon-coating thickness profiles obtained through Auger electron microscopy (AEM). Across 180,000 image pixels, the mean discrepancy with the learned model is remarkably small (<7%) and comparable with experimental noise. Our results open the possibility of learning nonequilibrium material properties beyond the reach of traditional experimental methods and offer a new non-destructive technique for characterizing and optimizing heterogeneous reactive surfaces.

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

Identifications and classifications of human locomotion using Rayleigh-enhanced distributed fiber acoustic sensors with deep neural networks

Abstract This paper reports on the use of machine learning to delineate data harnessed by fiber-optic distributed acoustic sensors (DAS) using fiber with enhanced Rayleigh backscattering to recognize vibration events induced by human locomotion. The DAS used in this work is based on homodyne phase-sensitive optical time-domain reflectometry (φ-OTDR). The signal-to-noise ratio (SNR) of the DAS was enhanced using femtosecond laser-induced artificial Rayleigh scattering centers in single-mode fiber cores. Both supervised and unsupervised machine-learning algorithms were explored to identify people and specific events that produce acoustic signals. Using convolutional deep neural networks, the supervised machine learning scheme achieved over 76.25% accuracy in recognizing human identities. Conversely, the unsupervised machine learning scheme achieved over 77.65% accuracy in recognizing events and human identities through acoustic signals. Through integrated efforts on both sensor device innovation and machine learning data analytics, this paper shows that the DAS technique can be an effective security technology to detect and to identify highly similar acoustic events with high spatial resolution and high accuracies.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Resurrection of ancestral effector caspases identifies novel networks for evolution of substrate specificity

Apoptotic caspases evolved with metazoans more than 950 million years ago (MYA), and a series of gene duplications resulted in two subfamilies consisting of initiator and effector caspases. The effector caspase genes (caspases-3, -6, and -7) were subsequently fixed into the Chordata phylum more than 650 MYA when the gene for a common ancestor (CA) duplicated, and the three effector caspases have persisted throughout mammalian evolution. All caspases prefer an aspartate residue at the P1 position of substrates, so each caspase evolved discrete cellular roles through changes in substrate recognition at the P4 position combined with allosteric regulation. We examined the evolution of substrate specificity in caspase-6, which prefers valine at the P4 residue, compared with caspases-3 and -7, which prefer aspartate, by reconstructing the CA of effector caspases (AncCP-Ef1) and the CA of caspase-6 (AncCP-6An). We show that AncCP-Ef1 is a promiscuous enzyme with little distinction between Asp, Val, or Leu at P4. The specificity of caspase-6 was defined early in its evolution, where AncCP-6An demonstrates a preference for Val over Asp at P4. Structures of AncCP-Ef1 and of AncCP-6An show a network of charged amino acids near the S4 pocket that, when combined with repositioning a flexible active site loop, resulted in a more hydrophobic binding pocket in AncCP-6An. The ancestral protein reconstructions show that the caspase-hemoglobinase fold has been conserved for over 650 million years and that only three substitutions in the scaffold are necessary to shift substrate selection toward Val over Asp.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Lagrangian statistics in weakly forced two-dimensional turbulence

Measurements of Lagrangian single-point and multiple-point statistics in a quasi-two-dimensional stratified layer system are reported. The system consists of a layer of salt water over an immiscible layer of Fluorinert and is forced electromagnetically so that mean-squared vorticity is injected at a well-defined spatial scale r i . Simultaneous cascades develop in which enstrophy flows predominately to small scales whereas energy cascades, on average, to larger scales. Lagrangian correlations and one- and two-point displacements are measured for random initial conditions and for initial positions within topological centers and saddles. Some of the behavior of these quantities can be understood in terms of the trapping characteristics of long-lived centers, the slow motion near strong saddles, and the rapid fluctuations outside of either centers or saddles. We also present statistics of Lagrangian velocity fluctuations using energy spectra in frequency space and structure functions in real space. We compare with complementary Eulerian velocity statistics. We find that simultaneous inverse energy and enstrophy ranges present in spectra are not directly echoed in real-space moments of velocity difference. Nevertheless, the spectral ranges line up well with features of moment ratios, indicating that although the moments are not exhibiting unambiguous scaling, the behavior of the probability distribution functions is changing over short ranges of length scales. Furthermore, implications for understanding weakly forced 2D turbulence with simultaneous inverse and direct cascades are discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

CMaize: Simplifying inter-package modularity from the build up

There is a growing desire for inter-package modularity within the chemistry software community to reuse encapsulated code units across a variety of software packages. Most comprehensive efforts at achieving inter-package modularity will quickly run afoul of a very practical problem, being able to cohesively build the modules. Writing and maintaining build systems has long been an issue for many scientific software packages that rely on compiled languages such as C/C++. The push for inter-package modularity compounds this issue by additionally requiring binary artifacts from disparate developers to interoperate at a binary level. Thankfully, the de facto build tool for C/C++, CMake, is more than capable of supporting the myriad of edge cases that complicate writing robust build systems. Unfortunately, writing and maintaining a robust CMake build system can be a laborious endeavor because CMake provides few abstractions to aid the developer. Further, the need to significantly simplify the process of writing robust CMake-based build systems, especially in inter-package builds, motivated us to write CMaize. In addition to describing the architecture and design of CMaize, the article also demonstrates how CMaize is used in production-level software.

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

Anisotropic interactions for continuum modeling of protein–membrane systems

In this work, a model for anisotropic interactions between proteins and cellular membranes is proposed for large-scale continuum simulations. The framework of the model is based on dynamic density functional theory, which provides a formalism to describe the lipid densities within the membrane as continuum fields while still maintaining the fidelity of the underlying molecular interactions. Within this framework, we extend recent results to include the anisotropic effects of protein–lipid interactions. As applications, we consider two membrane proteins of biological interest: a RAS–RAF complex tethered to the membrane and a membrane embedded G protein-coupled receptor. A strong qualitative and quantitative agreement is found between the numerical results and the corresponding molecular dynamics simulations. Combining the scope of continuum level simulations with the details from molecular level particle simulations enables research into protein–membrane behaviors at a more biologically relevant scale, which crucially can also be accessed via experiment.

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

Phase dynamics in an AC-driven multiterminal Josephson junction analog

In the presence of an AC drive, multiterminal Josephson junctions exhibit the inverse AC Josephson effect, where the oscillations of the superconducting phase of each junction can lock onto one another or onto the external drive. The competition between these different phase-locked states results in a complex array of quantized voltage plateaus whose stability strongly depend on the circuit parameters of the shunted junctions. This phase diagram cannot be explored with low-temperature transport experiments alone, given the breadth of the parameter space, so we present an easily tunable analog circuit whose dynamical properties emulate those of a three-terminal junction. We focus on the observation of the multiterminal inverse AC Josephson effect, and we discuss how to identify Shapiro steps associated with each of the three junctions as well as their quartet states. We only observe integer phase-locked states in strongly overdamped networks, but fractional Shapiro steps appear as well when the quality factor of the junctions increases. Finally, we discuss the role of transverse coupling in the synchronization of the junctions.

36 MATERIALS SCIENCE↗