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DOE OSTI2022

Transmission Electron Microscopy Study on the Effect of Thermal and Electrical Stimuli on Ge2Te3 Based Memristor Devices

Memristor devices fabricated using the chalcogenide Ge 2 Te 3 phase change thin films in a metal-insulator-metal structure are characterized using thermal and electrical stimuli in this study. Once the thermal and electrical stimuli are applied, cross-sectional transmission electron microscopy (TEM) and X-ray energy-dispersive spectroscopy (XEDS) analyses are performed to determine structural and compositional changes in the devices. Electrical measurements on these devices showed a need for increasing compliance current between cycles to initiate switching from low resistance state (LRS) to high resistance state (HRS). The measured resistance in HRS also exhibited a steady decrease with increase in the compliance current. High resolution TEM studies on devices in HRS showed the presence of residual crystalline phase at the top-electrode/dielectric interface, which may explain the observed dependence on compliance current. XEDS study revealed diffusion related processes at dielectric-electrode interface characterized, by the separation of Ge 2 Te 3 into Ge- and Te- enriched interfacial layers. This was also accompanied by spikes in O level at these regions. Furthermore, in-situ heating experiments on as-grown thin films revealed a deleterious effect of Ti adhesive layer, wherein the in-diffusion of Ti leads to further degradation of the dielectric layer. This experimental physics-based study shows that the large HRS/LRS ratio below the current compliance limit of 1 mA and the ability to control the HRS and LRS by varying the compliance current are attractive for memristor and neuromorphic computing applications.

Shallcross, Austin↗

NASA NTRS1988

Accuracy requirements and benchmark experiments for CFD validation

The role of experiment in the development of Computation Fluid Dynamics (CFD) for aerodynamic flow prediction is discussed. The CFD verification is a concept that depends on closely coordinated planning between computational and experimental disciplines. Because code applications are becoming more complex and their potential for design more feasible, it no longer suffices to use experimental data from surface or integral measurements alone to provide the required verification. Flow physics and modeling, flow field, and boundary condition measurements are emerging as critical data. Four types of experiments are introduced and examples given that meet the challenge of validation: flow physics experiments; flow modeling experiments; calibration experiments; and verification experiments. Measurement and accuracy requirements for each of these differ and are discussed. A comprehensive program of validation is described, some examples given, and it is concluded that the future prospects are encouraging.

Marvin, Joseph G.↗

NASA NTRS1986

Influence of bulk turbulence and entrance boundary layer thickness on the curved duct flow field

The objective of this investigation was the experimental evaluation of bulk turbulence and boundary thickness influence on the secondary flow development in a square, 90 deg turning duct. A three dimensional laser velocimetry system was utilized to measure the mean and fluctuating components of velocity in the large curved duct facility. The three dimensional development of the viscous shear layers in the curved duct has a strong influence on the complete flow field. Since ducted three dimensional flows are found in many engineering applications, including gas turbine engines, and contain high turbulence levels and high wall heat transfer rates, they present a difficult challenge to computational fluid mechanics codes. Turbulence modeling remains one of constraints to CFD advance due to inadequate physical understanding and experimental definition of turbulent shear flows. The results of this investigation expand the curved duct data base to higher turbulence levels and thicker entrance boundary layers. The experimental results provide a challenging benchmark data base for computational fluid dynamics code development and validation. The variation of inlet bulk turbulence intensity provides additional information to aid in turbulence model evaluation.

Crawford, Roger A.↗

NASA NTRS1988

Accuracy requirements and benchmark experiments for CFD validation

The role of experiment in the development of Computational Fluid Dynamics (CFD) for aerodynamic flow prediction is discussed. The CFD verification is a concept that depends on closely coordinated planning between computational and experimental disciplines. Because code applications are becoming more complex and their potential for design more feasible, it no longer suffices to use experimental data from surface or integral measurements alone to provide the required verification. Flow physics and modeling, flow field, and boundary condition measurements are emerging as critical data. Four types of experiments are introduced and examples are given that meet the challenge of validation: flow physics experiments; flow modeling experiments; calibration experiments; and verification experiments. Measurement and accuracy requirements for each of these differ and are discussed. A comprehensive program of validation is described, some examples given, and it is concluded that the future prospects are encouraging.

Marvin, Joseph G.↗

NASA NTRS1990

Influence of bulk turbulence and entrance boundary layer thickness on the curved duct flow field

Shohadaee, Ahmad A.↗

NASA NTRS1994

Modeling composite and fluffy grains: The effects of porosity

Recent studies of interplanetary and interstellar dust provide evidence that cosmic dust grains are fluffy, composite objects, highlighting the need for models of the electromagnetic scattering by these grains. Effective medium theory (EMT) with Mie-type series solutions has been used to explore the effects of porosity which would be important in composite dust particles. While this indirect approach is both flexible and computationally efficient, it is not necessarily a good approximation. The need for EMT and its rather restrictive assumptions may be circumvented through a direct computation of the scattering properties via finite element methods, such as the discrete dipole approximation (DDA). Recently, the utility of the DDA method has been advanced significantly through improvements in theory, in numerical algorithms, and in computer hardware. Extensive calculations with the DDA method are used here to examine more directly the effects of porosity. A particular emphasis is placed upon developing a valid methodology. For both solid and porous targets we establish both numerical and physical convergence properties over the range of size parameter that is required for our study. DDA cross sections for grains with a range of porosity are compared to those computed by the EMT/series expansion technique to examine the applicability of several mixing rules, including two extensions of the Bruggeman rule. We show that for particles with Rayleigh vacuum inclusions, the extension proposed by Rouleau & Martin is quite successful. We also investigate the effects of larger, non-Rayleigh vacuum inclusions for various levels of porosity and find that they can be significant.

Wolff, Michael J.↗

NASA NTRS2005

Foreign Object Damage Identification in Turbine Engines

This report summarizes the collective work of a five-person team from different organizations examining the problem of detecting foreign object damage (FOD) events in turbofan engines from gas path thermodynamic and bearing accelerometer sensors, and determining the severity of damage to each component (diagnosis). Several detection and diagnostic approaches were investigated and a software tool (FODID) was developed to assist researchers detect/diagnose FOD events. These approaches include (1) fan efficiency deviation computed from upstream and downstream temperature/ pressure measurements, (2) gas path weighted least squares estimation of component health parameter deficiencies, (3) Kalman filter estimation of component health parameters, and (4) use of structural vibration signal processing to detect both large and small FOD events. The last three of these approaches require a significant amount of computation in conjunction with a physics-based analytic model of the underlying phenomenon the NPSS thermodynamic cycle code for approaches 1 to 3 and the DyRoBeS reduced-order rotor dynamics code for approach 4. A potential application of the FODID software tool, in addition to its detection/diagnosis role, is using its sensitivity results to help identify the best types of sensors and their optimum locations within the gas path, and similarly for bearing accelerometers.

Strack, William↗

NASA NTRS2010

Reliability Quantification of Advanced Stirling Convertor (ASC) Components

The Advanced Stirling Convertor, is intended to provide power for an unmanned planetary spacecraft and has an operational life requirement of 17 years. Over this 17 year mission, the ASC must provide power with desired performance and efficiency and require no corrective maintenance. Reliability demonstration testing for the ASC was found to be very limited due to schedule and resource constraints. Reliability demonstration must involve the application of analysis, system and component level testing, and simulation models, taken collectively. Therefore, computer simulation with limited test data verification is a viable approach to assess the reliability of ASC components. This approach is based on physics-of-failure mechanisms and involves the relationship among the design variables based on physics, mechanics, material behavior models, interaction of different components and their respective disciplines such as structures, materials, fluid, thermal, mechanical, electrical, etc. In addition, these models are based on the available test data, which can be updated, and analysis refined as more data and information becomes available. The failure mechanisms and causes of failure are included in the analysis, especially in light of the new information, in order to develop guidelines to improve design reliability and better operating controls to reduce the probability of failure. Quantified reliability assessment based on fundamental physical behavior of components and their relationship with other components has demonstrated itself to be a superior technique to conventional reliability approaches based on utilizing failure rates derived from similar equipment or simply expert judgment.

Shah, Ashwin R.↗

DOE OSTI2025

Opportunities and Challenges of Computational Electromagnetics Methods for Superconducting Circuit Quantum Device Modeling: A Practical Review

High-fidelity numerical methods that model the physical layout of a device are essential for the design of many technologies. For methods that characterize electromagnetic effects, these numerical methods are referred to as computational electromagnetics (CEM) methods. Although the CEM research field is mature, emerging applications can still stress the capabilities of the techniques in use today. The design of superconducting circuit quantum devices falls in this category due to the unconventional material properties and important features of the devices covering nanometer to centimeter scales. Such multiscale devices can stress the fundamental properties of CEM tools which can lead to an increase in simulation times, a loss in accuracy, or even cause no solution to be reliably found. While these challenges are being investigated by CEM researchers, knowledge about them is limited in the broader community of users of these CEM tools. This review is meant to serve as a practical introduction to the fundamental aspects of the major CEM techniques that a researcher may need to choose between to model a device, as well as provide insight into what steps they may take to alleviate some of their challenges. Our focus is on highlighting the main concepts without rigorously deriving all the details, which can be found in many textbooks and articles. After covering the fundamentals, we discuss more advanced topics related to the challenges of modeling multiscale devices with specific examples from superconducting circuit quantum devices. We conclude with a discussion on future research directions that will be valuable for improving the ability to successfully design increasingly more sophisticated superconducting circuit quantum devices. Although our focus and examples are taken from this area, researchers from other fields will still benefit from the details discussed here.

Elkin, Samuel T. [Virginia Tech., Blacksburg]↗

DOE OSTI2023

Comparison of Equilibrium and Nonequilibrium Approaches for Relative Binding Free Energy Predictions

Alchemical relative binding free energy calculations have recently found important applications in drug optimization. A series of congeneric compounds are generated from a preidentified lead compound, and their relative binding affinities to a protein are assessed in order to optimize candidate drugs. While methods based on equilibrium thermodynamics have been extensively studied, an approach based on nonequilibrium methods has recently been reported together with claims of its superiority. However, these claims pay insufficient attention to the basis and reliability of both methods. Here we report a comparative study of the two approaches across a large data set, comprising more than 500 ligand transformations spanning in excess of 300 ligands binding to a set of 14 diverse protein targets. Ensemble methods are essential to quantify the uncertainty in these calculations, not only for the reasons already established in the equilibrium approach but also to ensure that the nonequilibrium calculations reside within their domain of validity. If and only if ensemble methods are applied, we find that the nonequilibrium method can achieve accuracy and precision comparable to those of the equilibrium approach. Compared to the equilibrium method, the nonequilibrium approach can reduce computational costs but introduces higher computational complexity and longer wall clock times. There are, however, cases where the standard length of a nonequilibrium transition is not sufficient, necessitating a complete rerun of the entire set of transitions. This significantly increases the computational cost and proves to be highly inconvenient during large-scale applications. Our findings provide a key set of recommendations that should be adopted for the reliable implementation of nonequilibrium approaches to relative binding free energy calculations in ligand-protein systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Generalized Brønsted‐Evans‐Polanyi Relationships for Reactions on Metal Surfaces from Machine Learning

Abstract Brønsted‐Evans‐Polanyi (BEP) relationships, i. e., a linear scaling between reaction and activation energies, lie at the core of computational design of heterogeneous catalysts. However, BEPs are not general and often require reparameterization for each class of reactions. Here we construct generalized BEPs (gBEPs), which can predict activation energies for a diverse dataset of reactions of C, O, N and H containing molecules on metal surfaces. In a first step we develop a set of descriptors based on scaling relationships that can capture the change in chemical identity of reactants during the reaction. Subsequently, we use the reaction energy, these descriptors and a single descriptor for the surface structure to parameterize machine learning based regression approaches for the prediction of activation energies. The best approach we developed shows a Mean Absolute Error (MAE) of 0.11 eV for the training set (80 % of the data set) and 0.23 eV for the test set (20 % of the data set). The methodology presented here allows to calculate activation energies within fractions of seconds on a typical personal computer and due to its generality, accuracy and simplicity in application it might prove to be useful in transition metal catalyst design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Physics informed deep neural network embedded in a chemical transport model for the Amazon rainforest

Secondary organic aerosols (SOA) are fine particles in the atmosphere, which interact with clouds, radiation and affect the Earth’s energy budget. SOA formation involves chemistry in gas phase, aqueous aerosols, and clouds. Simulating these chemical processes involve solving a stiff set of differential equations, which are computationally expensive steps for three-dimensional chemical transport models. Deep neural networks (DNNs) are universal function approximators that could be used to represent the complex nonlinear changes in aerosol physical and chemical processes; however, key challenges such as generalizability to extended time periods, preservation of mass balance, simulating sparse model outputs, and maintaining physical constraints have limited their use in atmospheric chemistry. Here, we develop an approach of using a physics-informed DNN that overcomes previous such challenges and demonstrates its applicability for the chemical formation processes of isoprene epoxydiol SOA (IEPOX-SOA) over the Amazon rainforest. The DNN is trained with data generated by simulating IEPOX-SOA over the entire atmospheric column, using the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem). The trained DNN is then embedded within WRF-Chem to replace the computationally expensive default solver of IEPOX-SOA formation. The trained DNN predictions generalizes well with the default model simulation of the IEPOX-SOA mass concentrations and its size distribution (20 size bins) over several days of simulations in both dry and wet seasons. The embedded DNN reduces the computational expense of WRF-Chem by a factor of 2. Our approach shows promise in terms of application to other computationally expensive chemistry solvers in climate models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Shadow Distillation: Quantum Error Mitigation with Classical Shadows for Near-Term Quantum Processors

Mitigating errors in quantum information processing devices is especially important in the absence of fault tolerance. An effective method in suppressing state-preparation errors is using multiple copies to distill the ideal component from a noisy quantum state. Here, we use classical shadows and randomized measurements to circumvent the need for coherent access to multiple copies at an exponential cost. We study the scaling of resources using numerical simulations and find that the overhead is still favorable compared to full state tomography. We optimize measurement resources under realistic experimental constraints and apply our method to an experiment preparing a Greenberger-Horne-Zeilinger state with trapped ions. In addition to improving stabilizer measurements, the analysis of the improved results reveals the nature of errors affecting the experiment. Hence, our results provide a directly applicable method for mitigating errors in near-term quantum computers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRSDate not supplied

RCA: Modeling Tools for CLmax Prediction

TACP06 is aimed at further research in maturing eddy-resolving modeling tools, in their accuracy and efficiency, and demonstrating the tools for application to the prediction of aircraft maximum lift (CLmax). The goal is to achieve CLmaxprediction accuracy of the same level as in aircraft certification flight tests. This will require comparison of various modeling approaches against experimental results from the planned wind tunnel tests, down selecting an approach, further maturing the technology and validating against flight test data. Advances in computational fluid dynamics (CFD) over the last several decadeshas fundamentally changed the aerospace design process. Advanced simulation capabilities not only enable reductions in ground-based and in-flight testing requirements, but also provide added physical insight, enable superior designs at reduced cost and risk, and open upnew frontiers in aerospace vehicle design and performance. The NASA sponsored CFD Vision 2030 Study, while highlighting these accomplishments, brought out several challenges and deficiencies in the computational technology and developed a research roadmap for advancing the state-of-the-art required for enabling NASA missions in aeronautics and space applications. Based on TC TACP01 research completed in May 2018, it became clear that unsteady flow simulation capability is needed for expanding the role of CFD across the entire flight envelope to enable design of future advanced aircraft and space vehicles, and that capability will be the primary outcome of this technical challenge.

Mujeeb R Malik↗

DOE OSTI2022

Constructing approximately diagonal quantum gates

We study a method of producing approximately diagonal 1-qubit gates. For each positive integer, the method provides a sequence of gates that are defined iteratively from a fixed diagonal gate and an arbitrary gate. These sequences are conjectured to converge to diagonal gates doubly exponentially fast and are verified for small integers. We systemically study this conjecture and prove several important partial results. Some techniques are developed to pave the way for a final resolution of the conjecture. The sequences provided here have applications in quantum search algorithms, quantum circuit compilation, generation of leakage-free entangled gates in topological quantum computing, etc.

Computer Science↗

DOE OSTI2024

Lie–Poisson Neural Networks (LPNets): Data-based computing of Hamiltonian systems with symmetries

An accurate data-based prediction of the long-term evolution of Hamiltonian systems requires a network that preserves the appropriate structure under each time step. Every Hamiltonian system contains two essential ingredients: the Poisson bracket and the Hamiltonian. Hamiltonian systems with symmetries, whose paradigm examples are the Lie–Poisson systems, have been shown to describe a broad category of physical phenomena, from satellite motion to underwater vehicles, fluids, geophysical applications, complex fluids, and plasma physics. The Poisson bracket in these systems comes from the symmetries, while the Hamiltonian comes from the underlying physics. We view the symmetry of the system as primary, hence the Lie–Poisson bracket is known exactly, whereas the Hamiltonian is regarded as coming from physics and is considered not known, or known approximately. Using this approach, we develop a network based on transformations that exactly preserve the Poisson bracket and the special functions of the Lie–Poisson systems (Casimirs) to machine precision. We present two flavors of such systems: one, where the parameters of transformations are computed from data using a dense neural network (LPNets), and another, where the composition of transformations is used as building blocks (G-LPNets). We also show how to adapt these methods to a larger class of Poisson brackets. We apply the resulting methods to several examples, such as rigid body (satellite) motion, underwater vehicles, a particle in a magnetic field, and others. The methods developed in this paper are important for the construction of accurate data-based methods for simulating the long-term dynamics of physical systems.

Data-based modeling↗

DOE OSTI2026

Advancing density functional tight-binding method for large organic molecules through equivariant neural networks

Semi-empirical quantum-mechanical (QM) methods have become valuable tools for studying complex (bio)molecular systems due to their balance between computational efficiency and accuracy. A key aspect of these methods is their parameterization, which not only governs the reliability of the results but also provides an opportunity to enhance their overall performance. In our previous work [J. Phys. Chem. Lett., 2021, 11, 16], we advanced the third-order semi-empirical density functional tight-binding (DFTB3) method for computing multiple properties of small molecules by developing the machine learning (ML) potential NN rep to bridge the gap between DFTB3 electronic components and those of the hybrid DFT-PBE0 functional. To overcome the limitations of NN rep , we introduce the EquiDTB framework, which leverages physics-inspired equivariant neural networks (NN) to parameterize scalable and transferable many-body Δ TB potentials, replacing the standard pairwise DFTB repulsive potential. This advancement extends the applicability of our ML-corrected DFTB approach to larger molecules and non-covalent systems (including only C, N, O, and H atoms), going beyond the chemical space represented in the training QM datasets. The enhanced performance of EquiDTB over the standard TB methods is demonstrated by the accurate computation of the atomic forces of S66x8 molecular dimers, as well as their interaction energies. Moreover, EquiDTB can be effectively employed to explore the potential energy surfaces of large and flexible drug-like molecules—for example, to determine the minimum energy path between isomers, analyze structural transitions during dynamical simulations, compute vibrational modes, and investigate energetic rankings. The performance for single molecules slightly decreases when the DFTB electronic energy is reduced to first-order but remains superior to standard TB methods. Our work thus demonstrates that an optimal integration of an equivariant NN with QM datasets can advance the DFTB method while maintaining high efficiency, paving the way for reliable (bio)molecular simulations.

Medrano Sandonas, Leonardo [Technische Universität↗

DOE OSTI2020

An Adaptive-Mesh-Refinement Based Computational Tool for Simulating Catalysis at Mesoscale

In this work, we present a computational tool for mesoscale applications using open-source exascale- computing compatible adaptive-mesh-refinement (AMR) library, AMReX [2]. AMReX is software library that enables development of application solvers with block-structured Cartesian AMR. Our tool has capabilities to include realistic geometry representation, chemical species transport, reactions and thermodynamics that are critical for capturing mesoscale physics. A significant achievement is the ability of our solver to automatically import electron microscopy data in the form of a stereolithography (STL) or pixelated file format (mrc, tiff) without undergoing the tedious task of unstructured mesh generation. This feature allows for rapid simulation of catalyst particles with complex morphologies using an immersed-boundary formulation. The use of AMR allows for higher resolutions at catalyst surface interfaces, which in turn provides an accurate description of surface reactions and transport. Our solver uses a hybrid distributed and shared memory parallelism (OpenMP/GPU-based) with which strong scaling up to 10,000 processors for realistic catalyst particle simulations have been demonstrated.

BIOMASS FUELS,MATHEMATICS AND COMPUTING↗