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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 OSTI2018

A new potential energy surface and state-to-state quantum dynamics of the $\mathrm{Li + HF → H + LiF}$ reaction

A new global potential energy surface (PES) for the ground ($\tilde{X}^2A'$) state of the Li + HF → LiF + H reactive system is constructed by fitting 12,399 points computed at the UCCSD(T)-F12b/aug-cc-pV5Z level of theory. The use of a neural network method helps to achieve a high-fidelity fit with a root mean square error of 0.88 meV. Quantum dynamics calculations with full Coriolis coupling on the new PES yield both differential and integral cross sections over a range of energy. In agreement with experiment, the product angular distribution depends sensitively on collision energy, but all with a forward bias. Moderate internal excitation is found in the LiF product. The energy dependence of the integral cross section is in reasonably good accord with the experimental excitation function, although some quantitative differences persist. This late-barrier reaction shows a dramatic vibrational enhancement of reactivity. The mode specificity is rationalized using the Sudden Vector Projection model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1991

Improved techniques for outgoing wave variational principle calculations of converged state-to-state transition probabilities for chemical reactions

Improved techniques and well-optimized basis sets are presented for application of the outgoing wave variational principle to calculate converged quantum mechanical reaction probabilities. They are illustrated with calculations for the reactions D + H2 yields HD + H with total angular momentum J = 3 and F + H2 yields HF + H with J = 0 and 3. The optimization involves the choice of distortion potential, the grid for calculating half-integrated Green's functions, the placement, width, and number of primitive distributed Gaussians, and the computationally most efficient partition between dynamically adapted and primitive basis functions. Benchmark calculations with 224-1064 channels are presented.

Mielke, Steven L.↗
DOE OSTI2025

Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions

Particle collisions at accelerators like the Large Hadron Collider (LHC), recorded by experiments such as ATLAS and CMS, enable precise standard model measurements and searches for new phenomena. Simulating these collisions significantly influences experiment design and analysis but incurs immense computational costs, projected at millions of CPU-years annually during the high luminosity LHC (HL-LHC) phase. Currently, simulating a single event with Geant4 consumes around 1000 CPU seconds, with calorimeter simulations especially demanding. To address this, we propose a conditioned quantum-assisted generative model, integrating a conditioned variational autoencoder (VAE) and a conditioned restricted Boltzmann machine (RBM). Our RBM architecture is tailored for D-Wave’s Pegasus-structured advantage quantum annealer for sampling, leveraging the flux bias for conditioning. This approach combines classical RBMs as universal approximators for discrete distributions with quantum annealing’s speed and scalability. We also introduce an adaptive method for efficiently estimating effective inverse temperature, and validate our framework on Dataset 2 of CaloChallenge.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Materials genome innovation for computational software (magics) center

Functional layered material (LM) architectures will dominate nanomaterials science in this century. We have developed theory, modeling, simulation, and software and data tools that enhance understanding and AI guide synthesis, enable characterization of complex structures, and improve capabilities in the predictive design and growth of LMs. Research at the Center has focused on: Computational synthesis and characterization: AI guided synthesis and experimental synthesis of stacked LMs with tailored properties via optimized chemical vapor deposition (CVD) growth and liquid-phase exfoliation; study defects, edges, grain boundaries, wrinkling of atomic layers and their effects on chemical, mechanical, electrical, and optical properties. Far-from-equilibrium processes: Joint experimental and simulation based probe of electronic processes with NAQMD and ultrafast X-ray free-electron laser (XFEL) and ultrafast electron diffraction (UED) facilities at Stanford. Experimentally validate NAQMD by ultrafast electron diffraction and X-ray spectroscopy studies of structural and excited state dynamics, shape fluctuations, and phonon dynamics. Scalable software: Simulation engines for desktop-to-exascale platforms using low-overhead, linear-scaling QMD algorithms; divide-conquer-recombine NAQMD with electronic excitations; extended-Lagrangian reactive molecular dynamics (RMD), machine learning (ML) based neural-network quantum molecular dynamics (NNQMD), and super-state accelerated molecular dynamics (AMD) and kinetic Monte Carlo codes; thermal and electrical transport software; and design 3D architectures of LMs with desired functionality using scalable software. Distribution of software and data, and training: Software and simulation-experimental data generated within the Center are distributed to the materials science community via Berkeley Materials Project (MP) framework. We have also organized three workshops for software distribution and training at USC (Nov. 2017, Mar. 2018) and Gaithersburg, MD (Nov. 2018) to train researchers, with the last one in focused on underrepresented groups, in collaboration with Howard University which is one of the largest HBCUs. The Center supported a total of 46 personnel and 6 undergraduate students. These include 14 faculty, 11 postdoctoral research associates, 20 graduate research assistants, and mentored 6 undergraduate students. This resulted in the publications of 63 research papers that include 46 publications on Reactive and Quantum Dynamics Simulations, 13 publications on Machine Learning for Quantum Materials, and 4 publications on Quantum Computing.

2D Materials↗
DOE OSTI2024

Subnanometer Scale Mapping of Hydrogen Doping in Vanadium Dioxide

Hydrogen donor doping of correlated electron systems such as vanadium dioxide (VO 2 ) profoundly modifies the ground state properties. The electrical behavior of H x VO 2 is strongly dependent on the hydrogen concentration; hence, atomic scale control of the doping process is necessary. It is however a nontrivial problem to quantitatively probe the hydrogen distribution in a solid matrix. As hydrogen transfers its sole electron to the material, the ionization mechanism is suppressed. Here in this study, a methodology mapping the doping distribution at subnanometer length scale is demonstrated across a H x VO 2 thin film focusing on the oxygen-hydrogen bonds using electron energy loss spectroscopy (EELS) coupled with first-principles EELS calculations. The hydrogen distribution was revealed to be nonuniform along the growth direction and between different VO 2 grains, calling for intricate hydrogenation mechanisms. Our study points to a powerful approach to quantitatively map dopant distribution in quantum materials relevant to energy and information science

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Velocity-Space Hybridization of Direct Simulation Monte Carlo and a Quasi-Particle Boltzmann Solver

This paper presents a new method for modeling rarefied gas flows based on hybridization of direct simulation Monte Carlo (DSMC) and discrete velocity method (DVM)-based quasi-particle representations of the velocity distribution function. It is aimed at improving the resolution of the tails of the distribution function (compared with DSMC) and computational efficiency (compared with DVM). Details of the method, such as the collision algorithm and the particle merging scheme, are discussed. The hybrid approach is applied to the study of noise in a Maxwellian distribution, computation of electron-impact ionization rate coefficient, as well as numerical simulation of a supersonic Couette flow. The hybrid-based solver is compared with pure DSMC and DVM approaches in terms of accuracy, computational speed, and memory use. It is shown that such a hybrid approach can provide a lower computational cost than a pure DVM approach, while being able to retain accuracy in modeling high-velocity tails of the distribution function. For problems where trace species have a significant impact on the flow physics, the proposed method is shown to be capable of providing better computational efficiency and accuracy compared with standard fixed-weight DSMC.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

CFD Analysis of RLUOB Zone 1 HEPA Filter Plenum and Testing Manifolds

The purpose of this computational fluid dynamics (CFD) analysis is to ensure that Camfil Farr’s upstream and downstream injection and sampling manifolds can meet or exceed the requirements outlined in the ASME AG-1 1997 a(2000) Code on Nuclear Air and Gas Treatment for the testing of HEPA and adsorbent filters. This paper will present a numerical simulation of airflow in the Radiological Laboratory Utility Office Building (RLUOB) zone 1 HEPA filter plenum and testing manifolds using the commercial CFD software ANSYS FLUENT 2020R1. The CFD analysis focuses on the investigation of the air flow distribution and air-aerosol mixing uniformity. The evaluation was done for all steps of the modeling process: grid generation, physics setup, simulation, and post-processing. The mass flow rate in each section of the zone 1 injection and sampling manifolds is also reported.

42 ENGINEERING↗
DOE OSTI2017

Computed Axial Lithography (CAL):Toward Single Step 3D Printing of Arbitrary Geometries

Most additive manufacturing processes today operate by printing voxels (3D pixels) serially point-by-point to build up a 3D part. In some more recently-developed techniques, for example optical printing methods such as projection stereolithography [Zheng et al. 2012], [Tumbleston et al. 2015], parts are printed layer-by-layer by curing full 2d (very thin in one dimension) layers of the 3d part in each print step. There does not yet exist a technique which is able to print arbitrarily-defined 3D geometries in a single print step. If such a technique existed, it could be used to expand the range of printable geometries in additive manufacturing and relax constraints on factors such as overhangs in topology optimization. It could also vastly increase print speed for 3D parts. In this work, we develop the principles for an approach for single exposure 3D printing of arbitrarily defined geometries. Our approach, termed Computed Axial Lithgography (CAL), is based on tomographic reconstruction, with mathematical optimization to generate a set of projections to optically define an arbitrary dose distribution within a target volume. We demonstrate the potential ability of the technique to print 3D parts using a prototype CAL system based on sequential illumination from many angles. Finally, we propose new hardware designs which will help us to realize true single-shot arbitrary-geometry 3D CAL.

42 ENGINEERING↗
DOE OSTI2020

Shear-induced lift force on spheres in a viscous linear shear flow at finite volume fractions

Several studies have shown a significant increase in drag on a distribution of solid spherical particles within a fluid with increasing particle volume fraction. As a result, many empirical drag laws accounting for the dependence on the Reynolds number and volume fraction can be found in the literature. This study investigates the possibility of a similar effect of the particle volume fraction on the mean hydrodynamic lift force on randomly distributed spherical particles in a linear shear flow. Particle-resolved direct numerical simulations are performed to evaluate the mean lift force, and the results are compared with the case of an isolated particle in a linear shear flow for the same Reynolds number and shear rate. The mean lift force acting on the particles appears to remain nearly the same as that on an isolated particle. However, due to the influence of neighboring particles, there is a substantial force variation in transverse directions on each individual particle, whose magnitude is comparable to the mean drag force. The distribution of drag force in a linear shear flow is shown to be nearly the same as in a uniform flow at the same volume fraction and Reynolds number. A simple stochastic model based on a Gaussian distribution is presented for the lift force variation, and its performance is compared to the prediction of the deterministic pairwise interaction extended point-particle model.

42 ENGINEERING↗
DOE OSTI2018

The non-equilibrium attractor for kinetic theory in relaxation time approximation

I demonstrate that the concept of a non-equilibrium attractor can be extended beyond the lowest-order moments typically considered in hydrodynamic treatments. Using a previously obtained exact solution to the relaxation-time approximation Boltzmann equation for a transversally homogeneous and boost-invariant system subject to Bjorken flow, I derive an equation obeyed by all moments of the one-particle distribution function. Using numerical solutions, I show that, similar to the pressure anisotropy, all moments of the distribution function exhibit attractor-like behavior wherein all initial conditions converge to a universal solution after a short time with the exception of moments which are sensitive to modes with zero longitudinal momentum and high transverse momentum. In addition, I compute the exact solution for the distribution function itself on very fine lattices in momentum space and demonstrate that (a) an attractor for the full distribution function exists and (b) solutions with generic initial conditions relax to this solution, first at low momentum and later at high momentum.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Optimal resource allocation for flexible-grid entanglement distribution networks

We use a genetic algorithm (GA) as a design aid for determining the optimal provisioning of entangled photon spectrum in flex-grid quantum networks with arbitrary numbers of channels and users. After introducing a general model for entanglement distribution based on frequency-polarization hyperentangled biphotons, we derive upper bounds on fidelity and entangled bit rate for networks comprising one-to-one user connections. Simple conditions based on user detector quality and link efficiencies are found that determine whether entanglement is possible. We successfully apply a GA to find optimal resource allocations in four different representative network scenarios and validate features of our model experimentally in a quantum local area network in deployed fiber. Our results show promise for the rapid design of large-scale entanglement distribution networks.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2002

Research in Computational Astrobiology

We present results from several projects in the new field of computational astrobiology, which is devoted to advancing our understanding of the origin, evolution and distribution of life in the Universe using theoretical and computational tools. We have developed a procedure for calculating long-range effects in molecular dynamics using a plane wave expansion of the electrostatic potential. This method is expected to be highly efficient for simulating biological systems on massively parallel supercomputers. We have perform genomics analysis on a family of actin binding proteins. We have performed quantum mechanical calculations on carbon nanotubes and nucleic acids, which simulations will allow us to investigate possible sources of organic material on the early earth. Finally, we have developed a model of protobiological chemistry using neural networks.

Chaban, Galina↗
DOE OSTI2020

An analysis of the spatio-temporal resolution of the immersed boundary method with direct forcing

The immersed boundary method (IBM) with direct forcing is very popular in the simulation of rigid particulate flows. In the IBM, an interaction force is introduced at the interface between fluid and particle in order to approximate the no-slip boundary condition. The interaction force is calculated through dividing the velocity difference (or error) between fluid and particle at the interface by the time step. Here, a dynamic equation for the velocity difference is derived. Additionally, analyses on the dynamic equation provide a few new findings: (i) The interaction force is the solution of a least-squares error problem, with the direct implication that the Lagrangian marker distribution has no effect on the large scale flow structure once the distribution of Lagrangian markers become saturated along the interface (i.e., each marker remains properly correlated with all its neighbors); (ii) The Lagrangian volume-weight is a relaxation factor to control how fast the velocity error decays to the ideal value of zero; (iii) The optimal choice of the Lagrangian volume-weight is the largest value permissible by a stability condition. A comprehensive convergence analysis with regard to the spatial and temporal resolution is presented for the velocity error and also for the shear-stress and surface pressure. In three simple canonical problems, it is analytically and numerically shown that the IBM results converge to the theoretical solutions obtained with precise imposition of no-slip and no-penetration boundary conditions. It is observed that it is not necessary to match the Lagrangian marker volume-weight to that of the local Eulerian cell volume and in fact this matching leads to lower than optimal computational efficiency. However, it is found that extremely high Eulerian grid resolution and small time step have to be used to obtain high precision simulation results. Especially, the time step should be inversely proportional to the particle Reynolds number for low Reynolds number flows. For high frequency oscillation problems, the grid size needs to be reduced by a factor of the square root of the frequency, and the time step to be reduced by a factor of the frequency. The theoretical findings here can be used to alleviate the technical difficulties in simulating non-spherical particles by not requiring the Lagrangian marker distribution to match the Eulerian grids and also in the implementation of IBM on non-uniform Eulerian grids. The present work also provides simple practical guidance on the choice of temporal and spatial resolution so as to control the simulation error a priori.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Scaling Ensembles of Data-Intensive Quantum Chemical Calculations for Millions of Molecules

Deep learning models are efficient computational tools that can accelerate the inverse design of molecules with desired functional properties by generating predictions at a fraction of the time required by traditional quantum chemical approaches. To ensure that a model maintains accuracy and transferability across broad regions of the chemical space explored during the inverse design, it must be trained on massively large volumes of simulation data. This requires running large-scale ensemble quantum chemical calculations on high-performance computing (HPC) systems for data collection. However, the efficient execution of such large ensemble calculations and the management of large volumes of output data require tools that can judiciously utilize computational resources and manage metadata overhead on the file system. Therefore, we present a high-performance, scalable, ensemble management framework for performing data-intensive quantum chemical electronic structure calculations for organic molecules. This framework provides abstractions to plug different ab initio, first principles, and first principles-based semi-empirical methods and executes them efficiently at large scale on HPC systems. It dynamically distributes tasks to resources and uses tiered storage for managing large collections of files. We employed this framework to process over ten million organic molecules and generate open-source datasets that provide UV-vis absorption spectra by running time-dependent density-functional tight-binding calculations. It is the largest database containing molecular optical spectra that were simulated with quantum chemical methods in a consistent manner.

Mehta, Kshitij↗
DOE OSTI2015

Effect of the shape on the spin state and exchange in quantum dots. Feynman path integral analysis

The ab initio computer simulation of the mixed quantum states of 1–5-nm model ellipsoid quantum dots with “soft” walls containing two and three quantum-indistinguishable nonrelativistic electrons has been performed by the path integral method. The calculation has been carried out beyond the single-electron and mean-field approximations with the fundamentally exact inclusion of Coulomb and exchange correlations of all orders and the spin variable. Distributions over the eigenfunctions of the spin-squared operator, as well as the equilibrium spin numbers, have been obtained depending on the shape of a quantum dot and the temperature. The complete set of basis functions symmetrized in permutations according to the spin of the system has been obtained by application of the Young symmetry operators. The dependence of the energy on the shape of the quantum dot corresponds to the negative sign of the surface tension at its boundary. The calculation indicates that the spin magnetic susceptibility in the system of two electrons decreases strongly for spherical quantum dots (“pairing” of spins) and the temperature dependences have a pronounced maximum whose position depends on the shape of the quantum dot. For three electrons in an oblate quantum dot, the inversion of the energy levels of spin states is observed and affects the spin magnetic susceptibility. The results indicate a strong dependence of the energy of collective spin states of electrons on the detailed inclusion of exchange and Coulomb spatial correlations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Fabricating Single Crystal Quantum Dot Solids

The central goal of this research program was to establish fundamental synthesis and processing principles to enable the fabrication of single crystal quantum dot solids (QDS) with programmable structure (i.e., hexagonal and square) and composition. Our approach towards that goal leveraged access to and experience with unique in-situ, multi-probe characterization techniques to understand and ultimately control the fundamental relationship between processing conditions and nucleation and growth of QDS. The program integrated synthesis and fabrication (Hanrath) with in-situ TEM analysis (Kourkoutis) and computational modeling (Clancy) to gain atomic-level insights into the underlying physical phenomena governing assembly and attachment and to guide the development of optimized processing methods. We have established a foundational understanding of physicochemical interactions of self-assembly at a fluid interface, superlattice structure transformation pathways, the critical role of disorder during the initial dimerization of colloidal quantum dot monomers, and the residual strain distribution within the inter-dot epitaxial bridge which hampers the formation of novel electronics states of the quantum dot solids. Collectively, these insights have contributed towards advancing the mechanistic understanding of the complex choreography of assembly and attachment as well as understanding what currently limits further advances in high-fidelity quantum dot solids and tiles.

36 MATERIALS SCIENCE↗
DOE OSTI2014

The impacts of electronic state hybridization on the binding energy of single phosphorus donor electrons in extremely downscaled silicon nanostructures

We present the density functional theory calculations of the binding energy of the Phosphorus (P) donor electrons in extremely downscaled single P-doped Silicon (Si) nanorods. In past studies, the binding energy of donor electrons was evaluated for the Si nanostructures as the difference between the ionization energy for the single P-doped Si nanostructures and the electron affinity for the un-doped Si nanostructures. This definition does not take into account the strong interaction of donor electron states and Si electron states explicitly at the conductive states and results in a monotonous increase in the binding energy by reducing the nanostructure's dimensions. In this paper, we introduce a new approach to evaluate the binding energy of donor electrons by combining the projected density of states (PDOS) analysis and three-dimensional analysis of associated electron wavefunctions. This enables us to clarify a gradual change of the spatial distribution of the 3D electron wavefunctions (3DWFs) from the donor electron ground state, which is fully localized around the P donor site to the first conductive state, which spreads over the outer Si nanorods contributing to current conduction. We found that the energy of the first conductive state is capped near the top of the atomistic effective potential at the donor site with respect to the surrounding Si atoms in nanorods smaller than about 27 a{sub 0}. This results in the binding energy of approximately 1.5 eV, which is virtually independent on the nanorod's dimensions. This fact signifies a good tolerance of the binding energy, which governs the operating temperature of the single dopant-based transistors in practice. We also conducted the computationally heavy transmission calculations of the single P-doped Si nanorods connected to the source and drain electrodes. The calculated transmission spectra are discussed in comparison with the atomistic effective potential distributions and the PDOS-3DWFs method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Simulation of energy absorption spectrum in NaI crystal detector for multiple gamma energy using Monte Carlo method

The spectrum of gamma energy absorption in the NaI crystal (scintillation detector) is the interaction result of gamma photon with NaI crystal, and it’s associated with the photon gamma energy incoming to the detector. Through a simulation approach, we can perform an early observation of gamma energy absorption spectrum in a scintillator crystal detector (NaI) before the experiment conducted. In this paper, we present a simulation model result of gamma energy absorption spectrum for energy 100-700 keV (i.e. 297 keV, 400 keV and 662 keV). This simulation developed based on the concept of photon beam point source distribution and photon cross section interaction with the Monte Carlo method. Our computational code has been successfully predicting the multiple energy peaks absorption spectrum, which derived from multiple photon energy sources.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗