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All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

GRC MILab Software: Quick Start Guide

This document provides detailed installation and operating instructions for the GRC MILab Excel Add-In software developed at the NASA Glenn Research Center. The software described has been implemented to facilitate the process of importing into Microsoft Excel and analyzing materials test data from a wide range of materials tests. All resulting data is then ready for automated upload to the relevant table of the GRC Materials Intelligence (MI) database. This new software represents an update to the original MILab software developed by Granta Design Ltd.—a company specializing in materials software, data, and databases—for members of the Materials Data Management Consortium (MDMC), a collaboration between Granta, ASM International, NASA Glenn, and several other materials-oriented corporations and government agencies in the aerospace and defense industries. The updated software consists of the addition of two test type modules, the Generic and Generic Cyclic modules, with both representing a generalization of the original software. The Generic module supports the import and analysis of multiaxial data from any sequence of tensile, compression, relaxation, and/or creep test stages; and the Generic Cyclic module expands the functionality to include repeated sequences during cyclic testing. During processing, all imported data and analysis results are formatted by the software so as to be ready for immediate automated upload to the MI database, ensuring minimal overhead on the part of the user and access to persistent and reliable data for all relevant personnel.

Quick Start Guide↗
NASA NTRS1996

Database of Mechanical Properties of Textile Composites

This report describes the approach followed to develop a database for mechanical properties of textile composites. The data in this database is assembled from NASA Advanced Composites Technology (ACT) programs and from data in the public domain. This database meets the data documentation requirements of MIL-HDBK-17, Section 8.1.2, which describes in detail the type and amount of information needed to completely document composite material properties. The database focuses on mechanical properties of textile composite. Properties are available for a range of parameters such as direction, fiber architecture, materials, environmental condition, and failure mode. The composite materials in the database contain innovative textile architectures such as the braided, woven, and knitted materials evaluated under the NASA ACT programs. In summary, the database contains results for approximately 3500 coupon level tests, for ten different fiber/resin combinations, and seven different textile architectures. It also includes a limited amount of prepreg tape composites data from ACT programs where side-by-side comparisons were made.

Delbrey, Jerry↗
DOE OSTI2023

The Materials Provenance Store

Abstract We present a database resulting from high throughput experimentation, primarily on metal oxide solid state materials. The central relational database, the Materials Provenance Store (MPS), manages the metadata and experimental provenance from acquisition of raw materials, through synthesis, to a broad range of materials characterization techniques. Given the primary research goal of materials discovery of solar fuels materials, many of the characterization experiments involve electrochemistry, along with optical, structural, and compositional characterizations. The MPS is populated with all information required for executing common data queries, which typically do not involve direct query of raw data. The result is a database file that can be distributed to users so that they can independently execute queries and subsequently download the data of interest. We propose this strategy as an approach to manage the highly heterogeneous and distributed data that arises from materials science experiments, as demonstrated by the management of over 30 million experiments run on over 12 million samples in the present MPS release.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A Database of Stress-Strain Properties Auto-generated from the Scientific Literature using ChemDataExtractor

Abstract There has been an ongoing need for information-rich databases in the mechanical-engineering domain to aid in data-driven materials science. To address the lack of suitable property databases, this study employs the latest version of the chemistry-aware natural-language-processing (NLP) toolkit, ChemDataExtractor, to automatically curate a comprehensive materials database of key stress-strain properties. The database contains information about materials and their cognate properties: ultimate tensile strength, yield strength, fracture strength, Young’s modulus, and ductility values. 720,308 data records were extracted from the scientific literature and organized into machine-readable databases formats. The extracted data have an overall precision, recall and F-score of 82.03%, 92.13% and 86.79%, respectively. The resulting database has been made publicly available, aiming to facilitate data-driven research and accelerate advancements within the mechanical-engineering domain.

Kumar, Pankaj↗
DOE OSTI2021

Cladding Profilometry Analysis of Experimental Breeder Reactor-II Metallic Fuel Pins with HT9, D9, and SS316 Cladding

BISON finite element method fuel performance simulations were conducted using an existing automated process that couples the Fuels Irradiation & Physics Database (FIPD) and the Integral Fast Reactor Materials Information System database by writing input files and comparing the BISON output to post-irradiation fuel pin profilometry measurements contained within the databases. The importance of this work is to demonstrate the ability to benchmark fuel performance metallic fuel models within BISON using Experimental Breeder Reactor-II fuel pin data for a number of similar pins, while building off previous modeling efforts. Changes to the generic BISON input file include implementing pin specific axial power and flux profiles, pin specific fluences, frictional contact, and irradiation-induced volumetric swelling models for cladding. A statistical analysis of irradiation-induced volumetric swelling models for HT9, D9, and SS316 was performed for experiments X421/X421A, X441/X441A, and X486. Between these three experiments, there were 174 post-irradiation examination (PIE) profilometries used for validating the swelling models presented using a standard error of the estimate (SEE) method. Implementation of the volumetric swelling models for D9 and SS316 claddings was found to have a significant impact on the BISON profilometry simulated, where HT9 clad pins had an insignificant change due to low fluence values. BISON profilometry simulated for HT9, D9, and SS316 fuel pins agreed with PIE profilometry measurements, with assembly SEE values being 4.4 × 10−3 for X421A, 2.0 × 10−3 for X441A, and 2.8 × 10−3 for X486. D9 clad pins in X421/X421A had the highest SEE values, which is due to the BISON simulated profilometry being shifted axially. While this work accomplished its purpose to demonstrate the modeling of multiple fuel pins from the databases to help validate models, the results suggest that the continued development of metallic fuel models is necessary for qualifying new metallic fuel systems to better capture some physical performance phenomena, such as the hot pressing of U-Pu-Zr and the fuel cladding chemical interaction.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1995

Overview of the LDEF MSIG databasing activities

The Long Duration Exposure Facility (LDEF) and the accompanying experiments were composed of and contained a wide variety of materials, representing the largest collection of materials flown in low earth orbit (LEO) and retrieved for ground-based analysis to date. The results and implications of the mechanical, thermal, optical, and electrical data from these materials are the foundation on which future LEO spacecraft and missions will be built. The LDEF Materials Special Investigation Group (MSIG) has been charged with establishing and developing databases to document these materials and their performance to assure not only that the data are archived for future generations but also that the data are available to the spacecraft user community in an easily accessed, user-friendly form. This paper gives an overview of the current LDEF Materials Databases, their capabilities, and availability. An overview of the philosophy and format of a developing handbook on LEO effects on materials is also described.

Funk, Joan G.↗
DOE OSTI2022

Vickers hardness prediction from machine learning methods

Abstract The search for new superhard materials is of great interest for extreme industrial applications. However, the theoretical prediction of hardness is still a challenge for the scientific community, given the difficulty of modeling plastic behavior of solids. Different hardness models have been proposed over the years. Still, they are either too complicated to use, inaccurate when extrapolating to a wide variety of solids or require coding knowledge. In this investigation, we built a successful machine learning model that implements Gradient Boosting Regressor (GBR) to predict hardness and uses the mechanical properties of a solid (bulk modulus, shear modulus, Young’s modulus, and Poisson’s ratio) as input variables. The model was trained with an experimental Vickers hardness database of 143 materials, assuring various kinds of compounds. The input properties were calculated from the theoretical elastic tensor. The Materials Project’s database was explored to search for new superhard materials, and our results are in good agreement with the experimental data available. Other alternative models to compute hardness from mechanical properties are also discussed in this work. Our results are available in a free-access easy to use online application to be further used in future studies of new materials at www.hardnesscalculator.com .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2005

Fatigue Crack Growth Database for Damage Tolerance Analysis

The objective of this project was to begin the process of developing a fatigue crack growth database (FCGD) of metallic materials for use in damage tolerance analysis of aircraft structure. For this initial effort, crack growth rate data in the NASGRO (Registered trademark) database, the United States Air Force Damage Tolerant Design Handbook, and other publicly available sources were examined and used to develop a database that characterizes crack growth behavior for specific applications (materials). The focus of this effort was on materials for general commercial aircraft applications, including large transport airplanes, small transport commuter airplanes, general aviation airplanes, and rotorcraft. The end products of this project are the FCGD software and this report. The specific goal of this effort was to present fatigue crack growth data in three usable formats: (1) NASGRO equation parameters, (2) Walker equation parameters, and (3) tabular data points. The development of this FCGD will begin the process of developing a consistent set of standard fatigue crack growth material properties. It is envisioned that the end product of the process will be a general repository for credible and well-documented fracture properties that may be used as a default standard in damage tolerance analyses.

Metallic materials↗
NASA NTRS1993

Approach to SSME health monitoring. III - Exhaust plume emission spectroscopy: Recent results and detailed analysis

Spectral data for two recent A-1 test firings, 901-717 and 901-718, obtained from an Optical Multichannel Analyzer and an Optical Plume Anomaly Detector, are presented. The spectral data encompasses the database of SSME critical components and materials and the spectral database for the SSME related elements and materials. Relatively strong and continuous emissions from Cr and Fe atomic transitions were observed starting at engine start plus 494 s and persisting until the engine shut off at engine start plus 520 s. These emissions are considered to be emanated from the SSME material AISI 440C, which is traced to high pressure turbopump bearings.

Tejwani, Gopal D.↗
DOE OSTI2025

Dynamic in-context learning with conversational models for data extraction and materials property prediction

The advent of natural language processing and large language models (LLMs) has revolutionized the extraction of data from unstructured scholarly papers. However, ensuring data trustworthiness remains a significant challenge. In this paper, we introduce PropertyExtractor, an open-source tool that leverages advanced conversational LLMs such as Google gemini-pro and OpenAI gpt-4, blends zero-shot with few-shot in-context learning, and employs engineered prompts for the dynamic refinement of structured information hierarchies—enabling autonomous, efficient, scalable, and accurate identification, extraction, and verification of material property data. Our tests on material data demonstrate precision and recall that exceed 95% with an error rate of ∼9%, highlighting the effectiveness and versatility of the toolkit. Finally, databases for 2D material thicknesses, a critical parameter for device integration, and energy bandgap values are developed using PropertyExtractor. In particular, for the thickness database, the rapid evolution of the field has outpaced both experimental measurements and computational methods, creating a significant data gap. Our work addresses this gap and showcases the potential of PropertyExtractor as a reliable and efficient tool for the autonomous generation of various material property databases, advancing the field.

Ekuma, Chinedu E. (ORCID:0000000258527556)↗
NASA NTRSDate not supplied

Diglycolamine Material Compatibility through Mechanical Testing

One promising candidate for use as a liquid sorbent in air revitalization technologies is a mixture of diglycolamine and water. This solution can react heavily with certain materials, putting a constraint on what materials can be used when designing and operating a liquid amine air revitalization system. Initial testing on materials has been done in the Air Revitalization labs at NASA Ames Research Center (ARC) through dunk testing of materials and physically checking for swelling, cracking, or any sort of corrosion of the material at hand. While this approach allowed for a quick way of material testing, a more thorough approach is favorable to allow for a database of compatible materials and their properties after contact with the solution for extended periods of time to be compiled. With this database, an air revitalization system intended for long-duration space flight can be designed and operated with confidence that the materials chosen will not degrade and need to be replaced over time.

Tiago Faria Costa↗
DOE OSTI2020

Thermochemical Data Fusion Using Graph Representation Learning

Large databases are required for “Big Data” applications in catalysis and materials science. Thermochemical databases can be created by combining data from various sources and by correcting low-fidelity datasets to higher accuracy with minimal computation. To achieve this “data fusion”, thermochemical quantities of interest, calculated at various levels of density functional theory (DFT), need to be mapped to the same, high levels of theory. In this work, a graph theoretical, statistical framework is proposed for such tasks. Subgraph frequencies are shown to provide a natural representation for learning these fusion maps. The maps are linear and are learnt with automated descriptor selection. Using a dataset of as few as ~1% from the QM9 database of 133,885 molecules, these models can predict multiple thermochemical quantities at a higher level of theory with an accuracy of 1 kcal/mol. Here, the method is explainable, generalizable, and provides a diagnostic tool for outlier identification

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

CoRE MOF DB: A curated experimental metal-organic framework database with machine-learned properties for integrated material-process screening

Here, we present an updated version of the Computation-Ready, Experimental (CoRE) Metal-Organic Framework (MOF) database, which includes a curated set of computation-ready MOF crystal structures designed for high-throughput computational materials discovery. Data collection and curation procedures were improved from the previous version to enable more frequent updates in the future. Machine-learning-predicted properties, such as stability metrics and heat capacities, are included in the dataset to streamline screening activities. An updated version of MOFid was developed to provide detailed information on metal nodes, organic linkers, and topologies of an MOF structure. DDEC6 partial atomic charges of MOFs were assigned based on a machine-learning model. Gibbs ensemble Monte Carlo simulations were used to classify the hydrophobicity of MOFs. The finalized dataset was subsequently used to perform integrated material-process screening for various carbon-capture conditions using high-fidelity temperature-swing adsorption (TSA) simulations. Our workflow identified multiple MOF candidates that are predicted to outperform CALF-20 for these applications.

CoRE MOF database↗
DOE OSTI2021

Overview of the fusion nuclear science facility, a credible break-in step on the path to fusion energy

The Fusion Nuclear Science Facility (FNSF) is examined here as part of a two step program from ITER to commercial power plants. This first step is considered mandatory to establish the materials and component database in the real fusion in-service environment before proceeding to larger electricity producing facilities. The FNSF can be shown to make tremendous advances beyond ITER, toward a power plant, particularly in plasma duration and fusion nuclear environment. A moderate FNSF is studied in detail, which does not generate net electricity, but does reach the power plant blanket operating temperatures. The full poloidal Dual Coolant Lead Lithium (DCLL) blanket is chosen, with alternates being the Helium Cooled Lead Lithium (HCLL) and Helium Cooled Ceramic Breeder/Pebble Bed (HCCB/PB). Several power plant relevant choices are made in order to follow the philosophy of targeted technologies. Any fusion core component must be qualified by fusion relevant neutron testing and highly integrated non-nuclear testing before it can be installed on the FNSF in order to avoid the high probability of constant failures in a plasma-vacuum system. A range of missions for the FNSF, or any fusion nuclear facility on the path toward fusion power plants, are established and characterized by several metrics. A conservative physics strategy is pursued to accommodate the transition to ultra-long plasma pulses, and parameters are chosen to represent the power plant regime to the extent possible. An operating space is identified, and from this, one point is chosen for further detailed analysis, with R = 4.8 m, a = 1.2 m, IP = 7.9 MA, BT = 7.5 T, βN Gr = 0.9, fBS = 0.52, q95 = 6.0, H98 ∼1.0, and Q = 4.0. The operating space is shown to be robust to parameter variations. A program is established for the FNSF to show how the missions for the facility are met, with a He/H, a DD and 5 DT phases. The facility requires ∼25 years to complete its DT operation, including 7.8 years of neutron production, and the remaining spent on inspections and maintenance. The DD phase is critical to establish the ultra-long plasma pulse lengths. The blanket testing strategy is examined, and shows that many sectors have penetrations for heating and current drive (H/CD), diagnostics, or Test Blanket Modules (TBMs). The hot cell is a critical facility element in order for the FNSF to perform its function of developing the in-service material and component database. The pre-FNSF R&D is laid out in terms of priority topics, with the FNSF phases driving the time-lines for R&D completion. A series of detailed technical assessments of the FNSF operating point are reported in this issue, showing the credibility of such a step, and more detailed emphasis on R&D items to pursue. These include nuclear analysis, thermo-mechanics and thermal-hydraulics, liquid metal thermal hydraulics, transient thermo-mechanics, tritium analysis, maintenance assessment, magnet specification and analysis, materials assessments, core and scrape-off layer (SOL)/divertor plasma examinations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Initial demonstration of automated fuel performance modeling with 1977 EBR-II metallic fuel pins using BISON code with FIPD and IMIS databases

Using the BISON fuel performance code, simulations were conducted using an automated process to read initial and operating conditions from the Fuels Irradiation and Physics Database (FIPD) and Integral Fast Reactor materials information system (IMIS) database, which contains metallic fuel data from the Experimental Breeder Reactor-II (EBR-II). This work demonstrates use of an integrated framework to access the vast majority of EBR-II experimental fuel pin data to support rapid development of fuel performance models for next-generation metallic fuel systems. With this capability, validation for fuel qualification can be performed rapidly. Between IMIS and FIPD, there is enough information to conduct 1977 unique EBR-II metallic fuel pin histories from 24 different experiments, at varying levels of detail between the two databases. Each of these histories includes a high-resolution power history, flux history, coolant channel flow rates, and coolant channel temperatures. Fission gas release (FGR), cumulative damage fraction (CDF), fuel axial swelling, cladding profilometry, and burnup were all simulated in BISON. The results were compared to post-irradiation examination (PIE) results for the initial demonstration of automated BISON modeling. BISON simulations conducted with IMIS and FIPD were in rough agreement with PIE measurements and calculations. Cladding profilometry, FGR, and fuel axial swelling were found to be in rough agreement with PIE measurements, depending on the physics used within the BISON input files. Here, the mechanical contact solver chosen was found to significantly impact axial fuel swelling and cladding strain predictions. CDF values were assessed to see whether pin failure may have been predicted (CDF ≥ 1). This work suggests that continued development of an automated tool for BISON should focus on inclusion of the Fast Flux Test Facility (FFTF) experimental data for a larger database for metallic fuel, improved physical models to better capture fuel performance, such as fuel-cladding interactions, and a more detailed comparison with available PIE data to further the BISON model development.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

A Curated Experimental Compilation Analyzed by Theory Is More than a Review

Macromolecules is an exceptional resource in the field of polymer science and now publishes more than 1000 original articles a year that set the standard for scientific rigor and creative insights. Over the years, these individual contributions have combined to build the foundation of polymer science, broadly and inclusively defined. In addition to the individual articles, many of which are being celebrated in this series of editorials, Macromolecules has published invaluable reviews and perspectives. These scholarly contributions integrate the insights and results from numerous sources into a unified whole and often recommend future directions for the field. Novices and experts alike benefit from these works that capture topics from emerging discoveries to long-pondered topics and everything in between. To explore the importance of Macromolecules’ reviews and perspectives, we considered their influence on the field and found the 1994 review by Fetters et al. entitled “Connection between Polymer Molecular Weight, Density, Chain Dimensions, and Melt Viscoelastic Properties”1 to be a singularity. This review expertly curates and compiles a trove of data to build robust correlations between molecular characteristics and macroscopic viscoelastic properties of polymer melts, in the context of the tube model of entanglements.

36 MATERIALS SCIENCE↗
DOE OSTI2021

High-throughput search for magnetic topological materials using spin-orbit spillage, machine learning, and experiments

Magnetic topological insulators and semi-metals have a variety of properties that make them attractive for applications including spintronics and quantum computation. Here, we use systematic high-throughput density functional theory calculations to identify magnetic topological materials from the ≈ 40000 three-dimensional materials in the JARVIS-DFT database. First, we screen materials with net magnetic moment > 0.5 μB and spin-orbit spillage > 0.25, resulting in 25 insulating and 564 metallic candidates. The spillage acts as a signature of spin-orbit induced band-inversion. Then, we carry out calculations of Wannier charge centers, Chern numbers, anomalous Hall conductivities, surface bandstructures, and Fermi-surfaces to determine interesting topological characteristics of the screened compounds. We also train machine learning models for predicting the spillage, bandgaps, and magnetic moments of new compounds, to further accelerate the screening process. We experimentally synthesize and characterize a few candidate materials to support our theoretical predictions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗