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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Machine Learning assisted optimization and parameter space exploration dataset of spin ice Hamiltonian

This repository contains both simulated and experimental structure factor data for the data challenge involving the inverse scattering problem. The simulated data were generated during a machine-learning-assisted optimization routine described in ref[1]. The experimental structure factor was measured on a rare-earth oxide, Dy2Ti2O7 using diffuse neutron scattering from time-of-flight techniques on the CORELLI instrument at the Spallation Neutron Source, Oak Ridge National Laboratory. A Metropolis Monte Carlo code implemented to run in a High-performance computing setting was used to calculated simulated structure factors for the spin-ice Hamiltonian at 680 mK, which is the same temperature as for the experimental data. The total size of all the files in this repository is 5.12 GB. A detailed description of the files is given below. ExperimentalData_630mK.dat – A linearized version of 3-dimensional experimental data of size 61×81×21. This data was processed to remove an estimation of non-magnetic background, including nuclear scattering signal and instrumentation background. Parameters.dat – 6700 samples were evaluated over the 4-dimensional parameter space (J_1, J_2, J_3 and J_(3^' )). There is an additional parameter, D in the spin Hamiltonian to mimic the dipolar interaction between magnetic ions. However, this parameter, D was fixed to a value determined by prior work. This file contains five columns for the parameters J_1, J_2, J_3, J_(3^' ) and D respectively. 3D_Simulation_Data.dat – The simulated structure factor, S(Q) data are included in this file. Each raw contains a linearized array of 3D volumes of S(Q) calculated for the parameter set given in the corresponding row of the file Parameters.dat. The size of the volume data was matched to the experimental data. Qx(h,-h,0).dat, Qy(k,k,-2k).dat, Qz(l,l,l).dat – These files contain the h, k, and l values along with the reciprocal vectors [h,-h,0], [k,k,-2k] and [l,l,l] respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Use of a segmented cell for the combinatorial development of platinum group metal-free electrodes for polymer electrolyte fuel cells

The development of novel platinum group metal (PGM)-free catalysts is a difficult task, particularly when coupled with the integration into a cathode catalyst layer (CCL). The optimization of such PGM-free electrode structures is often non-linear and iterative, making it a demanding task due to the high number of parameters that affect performance. To accelerate both materials discovery and electrode development, this work demonstrates the application of a high-resolution segmented cell (SC) with 121 segments of 0.413 cm 2 size for combinatorial high-throughput PGM-free catalyst screening and CCL optimization. The approach utilizes three flow-field strips with active areas of 4.45 cm 2 each, distributed over 11 segments. Electrodes with identical catalyst material and fabrication method result in reproducible data with typically less than 10% variation between each segment and match in performance with differential single cell data. High throughput testing of combinatorial sample sets, with varied PGM-free electrocatalyst materials or electrode composition, demonstrated the ability to rapidly discern high performing outliers, and establish or confirm trends in electrode optimization. The results indicated significant performance benefits of electrodes with ionomer content of 45 wt% over lower values, and of water rich inks with 82% water content over those with 50%.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2024

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

Enzymes can be engineered at the level of their amino acid sequences to optimize key properties such as expression, stability, substrate range, and catalytic efficiency or even to unlock new catalytic activities not found in nature. Because the search space of possible proteins is vast, enzyme engineering usually involves discovering an enzyme starting point that has some level of the desired activity followed by directed evolution to improve its “fitness” for a desired application. Recently, machine learning (ML) has emerged as a powerful tool to complement this empirical process. ML models can contribute to (1) starting point discovery by functional annotation of known protein sequences or generating novel protein sequences with desired functions and (2) navigating protein fitness landscapes for fitness optimization by learning mappings between protein sequences and their associated fitness values. In this Outlook, we explain how ML complements enzyme engineering and discuss its future potential to unlock improved engineering outcomes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

ORNL_AISD_NiNb

This dataset describes the nickel-niobium solid solution binary alloy, where the two constituent elements nickel (Ni) and niobium (Nb) are randomly placed on an underlying crystal lattice. This dataset for nickel-niobium (Ni-Nb) alloys available includes the formation energy and bulk modulus for each crystal structure. Each atomic sample has a disordered phase which is obtained starting from an initial regular crystal structure of type body-centered cubic (BCC), face-centered cubic (FCC), or hexagonal compact packed (HCP). The geometry optimization ensures that all the alloy samples reached the equilibrium with negative formation energy. We perform geometry optimizations using the LAMMPS simulation package [1], a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. We utilized the embedded atom model (EAM) potential for Ni and Nb developed in a previous study [2]. The potential could describe behaviors of the liquid and solid phases of Ni-Nb alloy. The structural factors and angular distributions of three atoms are well-matched with X-ray and ab initio-based molecular dynamics data. We prepared the three different crystals with different initial lattice parameters (3.52 Ã… for FCC, 3.32 Ã… for BCC, and 3.5 Ã… for HCP). We performed energy minimization in two steps. Firstly, we minimized the structures with an isotropic unit cell to minimize the side effects from our arbitrary lattice parameters for all other compositions. Then, we applied geometry optimization with a triclinic (non-orthogonal) unit cell to fully minimize the stress components to calculate the elastic constants. In this procedure, we chose 10,000 as the maximum number of allowable steps aimed at obtaining fully relaxed atomic geometries. The dataset consists of three sets of crystal structures. The first set contains 46,086 irregular crystal structures, each of them with 54 atoms, obtained through optimization starting from a regular BCC crystal structure. The second set contains 24,543 irregular crystal structures, each of them with 32 atoms, obtained through optimization starting from a regular FCC crystal structure. The third set contains 39,303 irregular crystal structures, each of them with 48 atoms, obtained through optimization starting from a regular HCP crystal structure. The atomic configurations within each set span the possible compositional range. The three sets have been unified in a global dataset, which is extremely heterogeneous in terms of crystal structures, lattice volumes, and atomic configurations. Organization of files inside the dataset: the dataset contains three subdirectories called • BCC_opt • FCC_opt • HCP_opt based on the type of initial regular structure used to start the geometry optimization. Inside each of these folders, every atomic structure is identified by a string “A_B_Câ€, where A denotes the number of Nb in the system, B denotes index of structure with a given Nb number, and C denotes the total number of structures generated with a given Nb number. For each optimized crystal structure identified by the unique string of characters “A_B_Câ€, three files are provided: • A_B_C_opt.xyz: The optimized geometries in xyz format • A_B_C_opt.cfg: The optimized geometries in cfg format. It includes cell information and atomic energy, and forces calculated from LAMMPS. • A_B_C.elastic: Raw data of 21 elastic constants from LAMMPS output. • A_B_C.bulk: Calculated upper and lower bounds of bulk modulus and averaged one based on Voigt-Reuss-Hill approach from *.elastic. References: [1] A. P. Thompson, H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier, P. J. in 't Veld, A. Kohlmeyer, S. G. Moore, T. D. Nguyen, R. Shan, M. J. Stevens, J. Tranchida, C. Trott, and S. J. Plimpton. LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comp. Phys. Comm., 271:108171, 2022. [2] Y Zhang, R Ashcraft, MI Mendelev, CZ Wang, and KF Kelton. Experimental and molecular dynamics simulation study of structure of liquid and amorphous ni62nb38 alloy. The Journal of chemical physics, 145(20):204505, 2016.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Budget constrained machine learning for early prediction of adverse outcomes for COVID-19 patients

Abstract The combination of machine learning (ML) and electronic health records (EHR) data may be able to improve outcomes of hospitalized COVID-19 patients through improved risk stratification and patient outcome prediction. However, in resource constrained environments the clinical utility of such data-driven predictive tools may be limited by the cost or unavailability of certain laboratory tests. We leveraged EHR data to develop an ML-based tool for predicting adverse outcomes that optimizes clinical utility under a given cost structure. We further gained insights into the decision-making process of the ML models through an explainable AI tool. This cohort study was performed using deidentified EHR data from COVID-19 patients from ProMedica Health System in northwest Ohio and southeastern Michigan. We tested the performance of various ML approaches for predicting either increasing ventilatory support or mortality. We performed post hoc analysis to obtain optimal feature sets under various budget constraints. We demonstrate that it is possible to achieve a significant reduction in cost at the expense of a small reduction in predictive performance. For example, when predicting ventilation, it is possible to achieve a 43% reduction in cost with only a 3% reduction in performance. Similarly, when predicting mortality, it is possible to achieve a 50% reduction in cost with only a 1% reduction in performance. This study presents a quick, accurate, and cost-effective method to evaluate risk of deterioration for patients with SARS-CoV-2 infection at the time of clinical evaluation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Membrane-electrode structures for molecular catalysts for use in fuel cells and other electrochemical devices

Water soluble catalysts, (M)meso-tetra(N-Methyl-4-Pyridyl)Porphinepentachloride (M=Fe, Co, Mn & Cu), have been incorporated into the polymer binder of oxygen reduction cathodes in membrane electrode assemblies used in PEM fuel cells and found to support encouragingly high current densities. The voltages achieved are low compared to commercial platinum catalysts but entirely consistent with the behavior observed in electroanalytical measurements of the homogeneous catalysts. A model of the dynamics of the electrode action has been developed and validated and this allows the MEA electrodes to be optimized for any chemistry that has been demonstrated in solution. It has been shown that improvements to the performance will come from modifications to the structure of the catalyst combined with optimization of the electrode structure and a well-founded pathway to practical non-platinum group metal catalysts exists.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2016

A Ka-band radial relativistic backward wave oscillator with GW-class output power

A novel radial relativistic backward wave oscillator with a reflector is proposed and designed to generate GW-level high power microwaves at Ka-band. The segmented radial slow wave structure and the reflector are matched to enhance interaction efficiency. We choose the volume wave TM{sub 01} mode as the working mode due to the volume wave characteristic. The main structural parameters of the novel device are optimized by particle-in-cell simulation. High power microwaves with power of 2 GW and a frequency of 29.4 GHz are generated with 30% efficiency when the electron beam voltage is 383 kV, the beam current is 17 kA, and the guiding magnetic field is only 0.6 T. Simultaneously, the highest electric field in the novel Ka-band device is just about 960 kV/cm in second slow wave structure.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Anatase TiO{sub 2} nanowires functionalized by organic sensitizers for solar cells: A screened Coulomb hybrid density functional study

The adsorption of two different organic molecules cyanidin glucoside (C{sub 21}O{sub 11}H{sub 20}) and TA-St-CA on anatase (101) and (001) nanowires has been investigated using the standard and the range separated hybrid density functional theory calculations. The electronic structures and optical spectra of resulting dye–nanowire combined systems show distinct features for these types of photochromophores. The lowest unoccupied molecular orbital of the natural dye cyanidin glucoside is located below the conduction band of the semiconductor while, in the case of TA-St-CA, it resonates with the states inside the conduction band. The wide-bandgap anatase nanowires can be functionalized for solar cells through electron-hole generation and subsequent charge injection by these dye sensitizers. The intermolecular charge transfer character of Donor-π-Acceptor type dye TA-St-CA is substantially modified by its adsorption on TiO{sub 2} surfaces. Cyanidin glucoside exhibits relatively stronger anchoring on the nanowires through its hydroxyl groups. The atomic structures of dye–nanowire systems re-optimized with the inclusion of nonlinear solvation effects showed that the binding strengths of both dyes remain moderate even in ionic solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Grand canonically optimized grain boundary phases in hexagonal close-packed titanium

Grain boundaries (GBs) profoundly influence the properties and performance of materials, emphasizing the importance of understanding the GB structure and phase behavior. As recent computational studies have demonstrated the existence of multiple GB phases associated with varying the atomic density at the interface, we introduce a validated, open-source GRand canonical Interface Predictor (GRIP) tool that automates high-throughput, grand canonical optimization of GB structures. While previous studies of GB phases have almost exclusively focused on cubic systems, we demonstrate the utility of GRIP in an application to hexagonal close-packed titanium. We perform a systematic high-throughput exploration of tilt GBs in titanium and discover previously unreported structures and phase transitions. In low-angle boundaries, we demonstrate a coupling between point defect absorption and the change in the GB dislocation network topology due to GB phase transformations, which has important implications for the accommodation of radiation-induced defects.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Application of differential resonant high-energy X-ray diffraction to three-dimensional structure studies of nanosized materials: A case study of Pt–Pd nanoalloy catalysts

Atoms in many of the increasingly complex nanosized materials of interest to science and technology do not necessarily occupy the vertices of Bravais lattices. The atomic scale structure of such materials is difficult to determine by traditional X-ray diffraction and so their functional properties remain difficult to optimize by rational design. Here, the three-dimensional structure of Pt x Pd 100– x nanoalloy particles is determined, where x = 0, 14, 36, 47, 64 and 100, by a non-traditional technique involving differential resonant high-energy X-ray diffraction experiments conducted at the K edge of Pt and Pd. The technique is coupled with three-dimensional modeling guided by the experimental total and element-specific atomic pair distribution functions. Furthermore, using DFT (density functional theory) calculation based on the positions of atoms in the obtained three-dimensional structure models, the catalytic performance of Pt–Pd particles is explained. Furthermore, differential resonant high-energy X-ray diffraction is shown to be an excellent tool for three-dimensional structure studies of nanosized materials. The experimental and modeling procedures are described in good detail, to facilitate their wider usage.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Structure, Elastic Constants and XRD Spectra of Extended Solids under High Pressure

We present results of evolutionary simulations based on density functional calculations of a potentially new type of energetic materials called extended solids: P-N and N-H. High-density structures with covalent bonds generated using variable and fixed concentration methods were analysed in terms of thermo-dynamical stability and agreement with experimental X-ray diffraction (XRD) spectra. X-ray diffraction spectra were calculated using a virtual diffraction algorithm that computes kinematic diffraction intensity in three-dimensional reciprocal space before being reduced to a two-theta line profile. Calculated XRD patterns were used to search for the structure of extended solids present at experimental pressures by optimizing data according to experimental XRD peak position, peak intensity and theoretically calculated enthalpy. Elastic constants has been calculated for thermodynamically stable structures of P-N system.

Batyrev, I. G.↗
DOE OSTI2021

Method and apparatus for shape-based energy analysis of solids

A computer-readable medium stores instructions including storing a model of a physical structure and defining a mesh for the model. The mesh includes finite elements, each defined by a respective set of edges. The instructions include, for each finite element, identifying a governing differential equation and a set of complementary functions that exactly satisfy the governing differential equation. The instructions include determining an applied physical stimulus for the physical structure. The instructions include generating an energy optimization model that minimizes a difference between internal energy and external energy of the finite elements in response to the applied physical stimulus. The model includes a matrix of respective scalar multipliers for the complementary functions for each finite element. The instructions include transforming the matrix and calculating a physical parameter of interest. The instructions include, in response to the physical parameter not satisfying a design parameter, updating the model of the physical structure.

Spears, Robert E.↗
DOE OSTI2018

Epitaxial InGaAs/InAlAs/AlAs Structures for Heterobarrier Varactors with Low Leakage Current

The quality of heteroboundaries and optimal conditions for epitaxial growth are critical parameters for obtaining low leakage currents of heterobarrier varactors in the InGaAs/InAlAs/AlAs material system. Grown by molecular-beam epitaxy, three-barrier heterobarrier varactor structures adjacent to InAlAs/AlAs/InAlAs barrier layers by additional mismatched InGaAs layers subjected to compressive stress show, under optimal epitaxy conditions, extremely low levels of leakage current density (not more than 0.06 A/cm{sup 2} at a voltage of 5 V and 85°C) with relatively thin AlAs inserts (with a thickness of 2 nm).

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

LatticeAnalytics: Strut-Level Visualization and Inspection of Additively Manufactured Lattice Structures

Additive manufacturing (AM) is revolutionizing the production of custom components with complex internal geometries, essential for high-performance applications in diverse fields such as medicine and defense. These AM parts optimize strength while minimizing weight by utilizing internal lattice structures consisting of large quantities of small interconnected struts. However, the complexity of these structures, combined with the challenges of using X-ray Computed Tomography (XCT) data, makes validation of part reliability difficult. This ultimately inhibits the development of novel parts for our collaborating material scientists. Here, we introduce LatticeAnalytics, a novel framework specifically designed for visual inspection of defects in these lattice structures. Our framework offers an end-to-end solution that includes the data management of XCT scans, enables remote access for geographically dispersed teams through a web-based dashboard, and incorporates novel visualizations. Our analysis is facilitated by a coarse alignment between the lattice’s nominal model, a spatial graph, and the XCT data. We employ a simple VR-based approach for fast and rough alignment, followed by an offline registration and identification of the struts. With the nodes and struts aligned and identified in the volume, our framework allows querying of subvolumes containing a single strut at multiple resolutions. This avoids computation over the entire lattice and also allow for easy parallelization of down-stream computations, such as strut-specific metrics. To depict a fast overview of the strut quality, we introduce two innovative visual encodings, crucial for our collaborators’ research in creating novel AM parts: the Contour View and the Roughness Map, which depict critical geometrical and surface features of individual struts in standardized two 2D views. We evaluated the integrated system through expert interviews. The feedback confirms the framework’s practicality and its effectiveness in enhancing current inspection workflows. It solves major bottlenecks for our collaborators, ultimately helping them create novel parts with advanced properties.

Miao, Haichao [Lawrence Livermore National Laborat↗
DOE OSTI2020

Smart Composite Pressure Vessels (SCPV) with Integrated Health Monitoring

The U.S. Department of Energy (DOE) is promoting and developing more energy efficient and environmentally friendly technologies that will enable America to use less petroleum. Hydrogen fuel cells – which directly convert the chemical energy in hydrogen to electricity with only water and heat as byproducts – are a very attractive solution that can enable this to happen. To date, DOE's efforts have culminated in commercial demonstration of on-board vehicular hydrogen storage systems that can allow for a driving range of greater than 300 miles. This requires storing 5 Kgs of hydrogen onboard a light vehicle. In order to store this quantity of hydrogen, the hydrogen gas needs to be stored in a composite overwrapped pressure vessel (COPV) at a very high internal pressure. The objective of the program was to demonstrate continuous and predictable health-monitoring of composite pressure vessels. A higher confidence in the operational safety of the vessels will lead to reduced burst factor of safety imposed by regulatory standards and hence will help reduce the structural wall thickness that drives the cost of the vessel. Highly optimized Type III (metal lined) and Type IV (polymer lined) COPVs that are manufactured using filament winding process have been designed and qualified for the above mentioned application. However, the main structural component of the COPVs is carbon fiber and the high cost of carbon fiber composite in a pressure vessel is a primary challenge in reducing the cost of gaseous hydrogen storage. Continuous and remote monitoring of structural health of the COPVs as well as optimization of the strength translation of carbon fibers have the potential to allow for reduced factor of safety, thereby reduced amount of carbon and associated cost of the vessel. In the current program, the project team including Steelhead Composites (SHC), University of Tennessee, Knoxville (UTK), Teijin Carbon America, Oak Ridge National Laboratory (ORNL) and LUNA designed, fabricated, and tested smart composite pressure vessels with integrated sensors. Iterative loops of design and testing using coupons, subscale STEB vessels and full size vessels proved that a new generation of high performance carbon fiber can be used for efficient design of Type 3 vessels for H2 storage. Novel analysis techniques were developed to predict the initiation and propagation of interlaminar damage inside the composite shell due to impact damage, a real threat in practical operation. Such an analysis scheme is typically not used in tank design but is an essential tool for health monitoring of composite vessels. Remote sensing of the key signatures of the tank’s operating parameters such as pressure, acceleration and humidity was demonstrated using a unique device that can wirelessly transmit and stream the data to a remote server. Fiber optic sensors were successfully integrated during fabrication of the tanks. These sensors provide a wealth of information regarding the structural health of the vessel when it is subsequently pressurized or subjected to impact damage. Excellent correlation was demonstrated between the fiber optic sensor and mechanical strain gage data, and between the measurements and analytical predictions.

08 HYDROGEN↗
DOE OSTI2019

Acceleration of tensor-product operations for high-order finite element methods

This article is devoted to graphics processing unit (GPU) kernel optimization and performance analysis of three tensor-product operations arising in finite element methods. We provide a mathematical background to these operations and implementation details. Achieving close to peak performance for these operators requires extensive optimization because of the operators’ properties: low arithmetic intensity, tiered structure, and the need to store intermediate results during the kernel execution. We give a guided overview of optimization strategies and we present a performance model that allows us to compare the efficacy of these optimizations against an empirically calibrated roofline.

Świrydowicz, Kasia↗
DOE OSTI2023

Crystallographic Insight of Reduced Lattice Volume Expansion in Mesoporous Cu 2+ -Doped TiNb 2 O 7 Microspheres during Li + Insertion

TiNb 2 O 7 represents a promising anode material for lithium-ion batteries (LIBs), but its practical applications are currently hampered by the non-negligible volumetric expansion and contraction during the charge/discharge process and the sluggish ion/electron kinetics. Here a combination technique is reported by systematically optimizing the porous and spherical morphology, crystal structure, and surface decoration of mesoporous Cu 2+ -doped TiNb 2 O 7 microspheres to enhance the electrochemical Li + storage performance and stability simultaneously. The Cu 2+ dopants preferentially replace Ti 4+ in crystal lattices, which decreases the Li + diffusion barrier and increases the electronic conductivity, as confirmed by density functional theory (DFT) calculation and demonstrated by diverse electrochemical characterizations. The successful Cu 2+ doping significantly reduces the lattice expansion coefficient from 7.26% to 4.61% after Li + insertion along the b-axis of TiNb 2 O 7 , as visualized from in situ and ex situ XRD analysis. The optimal 5% Cu 2+ -doped TiNb 2 O 7 with surface coating of N-doped carbon exhibits significantly enhanced specific capacity and rate and cyclic performances in both half- and full-cell configurations, demonstrating an excellent electrochemical behavior for fast-charging LIB applications.

36 MATERIALS SCIENCE↗