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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Machine learned features from density of states for accurate adsorption energy prediction

Materials databases generated by high-throughput computational screening, typically using density functional theory (DFT), have become valuable resources for discovering new heterogeneous catalysts, though the computational cost associated with generating them presents a crucial roadblock. Hence there is a significant demand for developing descriptors or features, in lieu of DFT, to accurately predict catalytic properties, such as adsorption energies. Here, we demonstrate an approach to predict energies using a convolutional neural network-based machine learning model to automatically obtain key features from the electronic density of states (DOS). The model, DOSnet, is evaluated for a diverse set of adsorbates and surfaces, yielding a mean absolute error on the order of 0.1 eV. In addition, DOSnet can provide physically meaningful predictions and insights by predicting responses to external perturbations to the electronic structure without additional DFT calculations, paving the way for the accelerated discovery of materials and catalysts by exploration of the electronic space.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

High-throughput calculations of charged point defect properties with semi-local density functional theory—performance benchmarks for materials screening applications

Abstract Calculations of point defect energetics with Density Functional Theory (DFT) can provide valuable insight into several optoelectronic, thermodynamic, and kinetic properties. These calculations commonly use methods ranging from semi-local functionals with a-posteriori corrections to more computationally intensive hybrid functional approaches. For applications of DFT-based high-throughput computation for data-driven materials discovery, point defect properties are of interest, yet are currently excluded from available materials databases. This work presents a benchmark analysis of automated, semi-local point defect calculations with a-posteriori corrections, compared to 245 “gold standard” hybrid calculations previously published. We consider three different a-posteriori correction sets implemented in an automated workflow, and evaluate the qualitative and quantitative differences among four different categories of defect information: thermodynamic transition levels, formation energies, Fermi levels, and dopability limits. We highlight qualitative information that can be extracted from high-throughput calculations based on semi-local DFT methods, while also demonstrating the limits of quantitative accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Itinerant Magnetism in Hydride-Synthesized CaCo 12 B 6

A new compound in the underexplored Ca–Co–B phase space has been discovered, validating high-throughput computations from the Open Quantum Materials Database, which predicted thermodynamic stability for CaCo 12 B 6 in the SrNi 12 B 6 structure type. The synthetic effects of different boron precursors and the advantages of using CaH 2 instead of Ca metal were demonstrated by the short synthesis duration and high purity of CaCo 12 B 6 , in contrast with traditional synthesis routes. Powder X-ray diffraction (PXRD) confirmed that CaCo 12 B 6 shares the SrNi 12 B 6 structure ( R $\bar{3}$m (#166), a = 9.469(4) Å, c = 7.468(2) Å, Z = 3) and is water- and air-stable. High-temperature in situ PXRD indicates that CaCo 12 B 6 is stable below 1050 K under vacuum in a silica capillary. CaCo 12 B 6 decomposes between 693 and 773 K during spark-plasma sintering. Density functional theory calculations indicate that CaCo 12 B 6 is metallic with a ferromagnetic ground state. X-ray absorption near-edge spectroscopy and Bader charge analysis indicate that Co atoms in CaCo 12 B 6 lack ionic character. Magnetometry reveals room-temperature paramagnetism with μ eff = 1.7(1)μ B per Co atom and a Weiss constant of +190(10)K. Ferromagnetic ordering occurs below 172(1)K, resulting in a saturation moment of 0.46 μ B per Co atom. Our findings demonstrate that the hydride route is a viable strategy for discovery of new ternary alkaline-earth-transition metal borides analogous to rare-earth-containing counterparts.

diffraction↗
DOE OSTI2026

Dataset describing two reference models for full-spectral lighting and daylight simulations together with implementations for two software systems

A dataset of two spectral lighting simulation reference models - one office and one factory hall - is presented. It aims to demonstrate and support full-spectral daylight and electric lighting simulations and facilitate evaluation of non-visual effects of light. The dataset includes Rhino CAD geometry, comprehensive spectral material and light source data and window system BSDF data. Example implementations in the two software tools, Radiance and OWL, enable reproducible workflows and support adoption in other software. The dataset is openly available on Zenodo. The office model reproduces Room 518 at the University of Innsbruck, including a west-facing façade and interior furnishings. The factory hall model follows the proposed geometry in the European standard 15193 for building energy performance. Interior reflectances in the office were measured in-situ using a handheld spectrometer. Exterior spectra and factory hall materials matching specified reflectances were obtained from an online spectral materials database. Glazing transmittance was derived from IGDB data using LBNL Optics/WINDOW. BSDFs for venetian blinds at various tilt angles, and for a diffusing pane adapted from the Complex Glazing Database, were generated in WINDOW. Luminaires in both models are specified with photometric files (Eulumdat/IES) and lamp spectra (Fluorescent 840, 4000 K LED). The provided example implementations (Radiance, OWL) include prepared input data and scripts to run first spectral simulations; example results are also included. The dataset is prepared to support reuse by researchers, designers and software developers for method validation, software engineering and comparison, and development of spectral metrics and controls.

Geisler-Moroder, David↗
DOE OSTI2023

An integrated online radioassay data storage and analytics tool for nEXO

Large-scale low-background detectors are increasingly used in rare-event searches as experimental collaborations push for enhanced sensitivity. However, building such detectors, in practice, creates an abundance of radioassay data especially during the conceptual phase of an experiment when hundreds of materials are screened for radiopurity. A tool is needed to manage and make use of the radioassay screening data to quantitatively assess detector design options. We have developed a Materials Database Application for the nEXO experiment to serve this purpose. Furthermore, this paper describes this database application, explains how it functions, and discusses how it streamlines the design of the experiment.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

High-Throughput Screening for Boride Superconductors

A high-throughput screening using density functional calculations is performed to search for stable boride superconductors from the existing materials database. The workflow employs the fast frozen-phonon method as the descriptor to evaluate the superconducting properties quickly. Twenty-three stable candidates were identified during the screening. The superconductivity was obtained earlier experimentally or computationally for almost all found binary compounds. Previous studies on ternary borides are very limited. Here our extensive search among ternary systems confirmed superconductivity in known systems and found several new compounds. Among these discovered superconducting ternary borides, TaMo 2 B 2 shows the highest superconducting temperature of ∼12 K. Most predicted compounds were synthesized previously; therefore, our predictions can be examined experimentally. Our work also demonstrates that the boride systems can have diverse structural motifs that lead to superconductivity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Fast and Accurate Machine Learning Strategy for Calculating Partial Atomic Charges in Metal–Organic Frameworks

Computational high-throughput screening using molecular simulations is a powerful tool for identifying top-performing metal–organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges are often required to model the electrostatic interactions between the MOF and the adsorbate, especially when the adsorption involves molecules with dipole or quadrupole moments such as water and CO 2 . Although ab initio methods can be used to calculate accurate partial atomic charges, these methods are impractical for screening large material databases because of the high computational cost. We developed a random forest machine learning model to predict the partial atomic charges in MOFs using a small yet meaningful set of features that represent both the elemental properties and the local environment of each atom. The model was trained and tested on a collection of about 320 000 density-derived electrostatic and chemical (DDEC) atomic charges calculated on a subset of the Computation-Ready Experimental Metal–Organic Framework (CoRE MOF-2019) database and separately on charge model 5 (CM5) charges. The model predicts accurate atomic charges for MOFs at a fraction of the computational cost of periodic density functional theory (DFT) and is found to be transferable to other porous molecular crystals and zeolites. In conclusion, a strong correlation is observed between the partial atomic charge and the average electronegativity difference between the central atom and its bonded neighbors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A framework to evaluate machine learning crystal stability predictions

The rapid adoption of machine learning in various scientific domains calls for the development of best practices and community agreed-upon benchmarking tasks and metrics. We present Matbench Discovery as an example evaluation framework for machine learning energy models, here applied as pre-filters to first-principles computed data in a high-throughput search for stable inorganic crystals. We address the disconnect between (1) thermodynamic stability and formation energy and (2) retrospective and prospective benchmarking for materials discovery. Alongside this paper, we publish a Python package to aid with future model submissions and a growing online leaderboard with adaptive user-defined weighting of various performance metrics allowing researchers to prioritize the metrics they value most. To answer the question of which machine learning methodology performs best at materials discovery, our initial release includes random forests, graph neural networks, one-shot predictors, iterative Bayesian optimizers and universal interatomic potentials. We highlight a misalignment between commonly used regression metrics and more task-relevant classification metrics for materials discovery. Accurate regressors are susceptible to unexpectedly high false-positive rates if those accurate predictions lie close to the decision boundary at 0 eV per atom above the convex hull. The benchmark results demonstrate that universal interatomic potentials have advanced sufficiently to effectively and cheaply pre-screen thermodynamic stable hypothetical materials in future expansions of high-throughput materials databases.

Riebesell, Janosh↗
DOE OSTI2025

Wide-ranging predictions of new stable compounds powered by recommendation engines

The computational search for new stable inorganic compounds is faster than ever, thanks to high-throughput density functional theory (DFT). However, stable compound searches remain highly expensive because of the enormous search space and the cost of DFT calculations. To aid these searches, recommendation engines have been developed. We conduct a systematic comparison of the performance of previously developed recommendation engines, specifically ones based on elemental substitution, data mining, and neural network prediction of formation enthalpy. After identifying ways to improve the recommendation engines, we find the neural network to be superior at recommending stable Heusler compounds. Armed with improved recommendation engines, we identify tens of thousands of compounds that are stable at zero temperature and pressure, now available in the Open Quantum Materials Database. We summarize this diverse pool of compounds, including the elusive mixed anion compounds, and two of their many applications: thermoelectricity and solar thermochemical fuel production.

Science & Technology - Other Topics↗
NASA NTRS1993

An evaluation of ablative materials for a lunar transfer vehicle aerobrake

An evaluation of the utility of an ablative thermal protection system (TPS) for use on a lunar mission return aerobrake has been completed. Requirements are established and criteria developed specifically for the ablator application. A quantitative and qualitative multi-attribute utility analysis is employed to establish a relative ranking among candidates and a performance threshold. A review of the applicable existing ablator material database is made and appropriate thermochemical/melting ablation analysis is employed to evaluate thermal performance. Ablatives are shown to be potentially both performance capable and cost effective in single-use roles. Reusable surface insulation is also shown to be competitive as an ablator with certain modifications.

Lane, J. G.↗
NASA NTRS1994

Development of processing techniques for advanced thermal protection materials

The effort, which was focused on the research and development of advanced materials for use in Thermal Protection Systems (TPS), has involved chemical and physical testing of refractory ceramic tiles, fabrics, threads and fibers. This testing has included determination of the optical properties, thermal shock resistance, high temperature dimensional stability, and tolerance to environmental stresses. Materials have also been tested in the Arc Jet 2 x 9 Turbulent Duct Facility (TDF), the 1 atmosphere Radiant Heat Cycler, and the Mini-Wind Tunnel Facility (MWTF). A significant part of the effort hitherto has gone towards modifying and upgrading the test facilities so that meaningful tests can be carried out. Another important effort during this period has been the creation of a materials database. Computer systems administration and support have also been provided. These are described in greater detail below.

Selvaduray, Guna S.↗
NASA NTRS1997

Validation of Framework Code Approach to a Life Prediction System for Fiber Reinforced Composites

The grant was conducted by the MMC Life Prediction Cooperative, an industry/government collaborative team, Ohio Aerospace Institute (OAI) acted as the prime contractor on behalf of the Cooperative for this grant effort. See Figure I for the organization and responsibilities of team members. The technical effort was conducted during the period August 7, 1995 to June 30, 1996 in cooperation with Erwin Zaretsky, the LERC Program Monitor. Phil Gravett of Pratt & Whitney was the principal technical investigator. Table I documents all meeting-related coordination memos during this period. The effort under this grant was closely coordinated with an existing USAF sponsored program focused on putting into practice a life prediction system for turbine engine components made of metal matrix composites (MMC). The overall architecture of the NMC life prediction system was defined in the USAF sponsored program (prior to this grant). The efforts of this grant were focussed on implementing and tailoring of the life prediction system, the framework code within it and the damage modules within it to meet the specific requirements of the Cooperative. T'he tailoring of the life prediction system provides the basis for pervasive and continued use of this capability by the industry/government cooperative. The outputs of this grant are: 1. Definition of the framework code to analysis modules interfaces, 2. Definition of the interface between the materials database and the finite element model, and 3. Definition of the integration of the framework code into an FEM design tool.

Gravett, Phillip↗
NASA NTRS2000

Long-Term Durability Analysis of a 100,000+ Hr Stirling Power Convertor Heater Head

DOE and NASA have identified Stirling Radioisotope Power Systems (SRPS) as the power supply for deep space exploration missions the Europa Orbiter and Solar Probe. As a part of this effort, NASA has initiated a long-term durability project for critical hot section components of the Stirling power convertor to qualify flight hardware. This project will develop a life prediction methodology that utilizes short-term (t < 20,000 hr) test data to verify long-term (t > 100,000 hr) design life. The project consists of generating a materials database for the specific heat of alloy, evaluation of critical hermetic sealed joints, life model characterization, and model verification. This paper will describe the qualification methodology being developed and provide a status for this effort.

Bartolotta, Paul A.↗
NASA NTRS2002

A "Tread" Ahead of the Competition

Through a consortium formed by NASA's Glenn Research Center and the Great Lakes Industrial Technology Center, Goodyear acquired a software use agreement for NASA's Micromechanics Analysis Code (MAC), in an effort to design stronger and safer tires. Based on the Generalized Method of Cells micromechanics methodology, the MAC technology has allowed Goodyear scientists and engineers to perform structural analysis of composite laminates for tires all in one step, rather than the several steps previously required. Currently, the MAC code can be used to assess and optimize composite laminates; set cord-spacing guidelines (the diameter and distance between cords may impact the overall durability of a tire); explain structural impact of geometrical configurations; and compare the behavior of the different wire constructions that make up a tire. Furthermore, the MAC software contains a built-in material database and several representative volume elements for a wider choice of composites to better represent the plies and belts in a tire. The end result of the process ultimately reduces the time spent on building, testing, and adjusting tires.

Source record↗
NASA NTRS2003

Membrane-Based Gas Traps for Ammonia, Freon-21, and Water Systems to Simplify Ground Processing

Gas traps are critical for the smooth operation of coolant loops because gas bubbles can cause loss of centrifugal pump prime, interference with sensor readings, inhibition of heat transfer, and blockage of passages to remote systems. Coolant loops are ubiquitous in space flight hardware, and thus there is a great need for this technology. Conventional gas traps will not function in micro-gravity due to the absence of buoyancy forces. Therefore, clever designs that make use of adhesion and momentum are required for adequate separation, preferable in a single pass. The gas traps currently used in water coolant loops on the International Space Station are composed of membrane tube sets in a shell. Each tube set is composed of a hydrophilic membrane (used for water transport and capture of bubbles) and a hydrophobic membrane (used for venting of air bubbles). For the hydrophilic membrane, there are two critical pressures, the pressure drop and the bubble pressure. The pressure drop is the decrease in system pressure across the gas trap. The bubble pressure is the pressure required for air bubbles to pass across the water filled membrane. A significant difference between these pressures is needed to ensure complete capture of air bubbles in a single pass. Bubbles trapped by the device adsorb on the hydrophobic membrane in the interior of the hydrophilic membrane tube. After adsorption, the air is vented due to a pressure drop of approximately 1 atmosphere across the membrane. For water systems, the air is vented to the ambient (cabin). Because water vapor can also transport across the hydrophobic membrane, it is critical that a minimum surface area is used to avoid excessive water loss (would like to have a closed loop for the coolant). The currently used gas traps only provide a difference in pressure drop and bubble pressure of 3-4 psid. This makes the gas traps susceptible to failure at high bubble loading and if gas venting is impaired. One mechanism for the latter is when particles adhere to the hydrophobic membrane, promoting formation of a water layer about it that can blind the membrane for gas transport (Figure 1). This mechanism is the most probable cause for observed failures with the existing design. The objective of this project was to devise a strategy for choosing new membrane materials (database development and procedure), redesign of the gas trap to mitigate blinding effects, and to develop a design that can be used in ammonia and Freon-21 coolant loops.

Ritchie, Stephen M. C.↗
NASA NTRS2005

Polymers in Space: Applications in the NASA Life Support and Habitation Program

Outline of Content to be Presented: Session 1: Background on Human Space Flight, NASA Human Space Flight Programs: Apollo, Shuttle, ISS, U.S. Vision for Space Exploration, Goals of Human Spaceflight. Session. 2: Use of Polymers in NASA Technology Development, Life Support & Habitation Program, Spacecraft and Space Suit Requirements and Constraints Applications - Past, Current, Future Technologies in Development. Session 3: NASA Materials Database, Classes of Useful Polymers and Composites, Unique Requirements on Polymers in Space Applications of Synthetic and Biological Polymers. Session 4: Design of Polymer Parts for a Lunar Space Suit, Sample Activities for Teachers to Use in High School Classrooms.

Campbell, Paul D.↗
NASA NTRS2022

Virtual Assistant for First Responders Using Natural Language Understanding and Optical Character Recognition

Commercial deep learning capabilities are available for many applications such as computer vision processing and intelligent chat bots. The Google Cloud Platform product Google Dialogflow provides lifelike conversational artificial intelligence (AI) using machine learning (ML) to generate natural conversations between computers and humans. This ML utilizes natural language understanding (NLU) to recognize a user’s intent and extracts key information into a form of entities. We have developed a user-friendly application through understanding the hazardous material database, first aid safety guidelines and observing the process of first responders who access this information in the field. We created the Trusted and Explainable Artificial Intelligence for Saving Lives (TruePAL) virtual assistant using Dialogflow1 and TensorFlow2 paired with EasyOCR.3 The chatbot supports first responders by providing voice interaction which helps limit additional steps such as browsing through multiple categories when searching for information. Using feedback from our field interviews, the voice interface has been developed to enable the first responder to focus on the immediate emergency. With less distractions, the first responder is able to engage the incident more effectively. The partial hands-free TruePAL chatbot assistant improves the accessibility to the correct guidance by an average of 1.9 seconds compared to the widely used application, NIH WISER, which requires full attention to operate. We combined this intelligent chatbot with a separate visual processing capability to produce hazardous signage analysis and generate the proper guidance for first responders. With the evolving functionality of AI tools, the use of virtual assistants in first responder technology will be an advancement, benefiting the safety of both first responders and civilians.

Chow, Edward↗
DOE OSTI2021

OPTIMADE, an API for exchanging materials data

Abstract The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗