Learning dislocation dynamics mobility laws from large-scale MD simulations
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97 MATHEMATICS AND COMPUTING↗
SEARCH · Engineering Papers
Results for “computational materials science”
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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Enabling deeper learning on big data for materials informatics applications
Abstract The application of machine learning (ML) techniques in materials science has attracted significant attention in recent years, due to their impressive ability to efficiently extract data-driven linkages from various input materials representations to their output properties. While the application of traditional ML techniques has become quite ubiquitous, there have been limited applications of more advanced deep learning (DL) techniques, primarily because big materials datasets are relatively rare. Given the demonstrated potential and advantages of DL and the increasing availability of big materials datasets, it is attractive to go for deeper neural networks in a bid to boost model performance, but in reality, it leads to performance degradation due to the vanishing gradient problem. In this paper, we address the question of how to enable deeper learning for cases where big materials data is available. Here, we present a general deep learning framework based on Individual Residual learning (IRNet) composed of very deep neural networks that can work with any vector-based materials representation as input to build accurate property prediction models. We find that the proposed IRNet models can not only successfully alleviate the vanishing gradient problem and enable deeper learning, but also lead to significantly (up to 47%) better model accuracy as compared to plain deep neural networks and traditional ML techniques for a given input materials representation in the presence of big data.
36 MATERIALS SCIENCE↗
Data-driven analysis and prediction of stable phases for high-entropy alloy design
High-entropy alloys (HEAs) represent a promising class of materials with exceptional structural and functional properties. However, their design and optimization pose challenges due to the large composition-phase space coupled with the complex and diverse nature of the phase formation dynamics. In this study, a data-driven approach that utilizes machine learning (ML) techniques to predict HEA phases and their composition-dependent phases is proposed. By employing a comprehensive dataset comprising 5692 experimental records encompassing 50 elements and 11 phase categories, we compare the performance of various ML models. Our analysis identifies the most influential features for accurate phase prediction. Furthermore, the class imbalance is addressed by employing data augmentation methods, raising the number of records to 1500 in each category, and ensuring a balanced representation of phase categories. The results show that XGBoost and Random Forest consistently outperform the other models, achieving 86% accuracy in predicting all phases. Additionally, this work provides an extensive analysis of HEA phase formers, showing the contributions of elements and features to the presence of specific phases. We also examine the impact of including different phases on ML model accuracy and feature significance. Notably, the findings underscore the need for ML model selection based on specific applications and desired predictions, as feature importance varies across models and phases. This study significantly advances the understanding of HEA phase formation, enabling targeted alloy design and fostering progress in the field of materials science.
36 MATERIALS SCIENCE↗
Experimental discovery of structure–property relationships in ferroelectric materials via active learning
Emergent functionalities of structural and topological defects in ferroelectric materials underpin an extremely broad spectrum of applications ranging from domain wall electronics to high dielectric and electromechanical responses. Many of these functionalities have been discovered and quantified via local scanning probe microscopy methods. However, the search has until now been based on either trial and error, or using auxiliary information such as the topography or domain wall structure to identify potential objects of interest on the basis of the intuition of operator or pre-existing hypotheses, with subsequent manual exploration. Here we report the development and implementation of a machine learning framework that actively discovers relationships between local domain structure and polarization-switching characteristics in ferroelectric materials encoded in the hysteresis loop. The hysteresis loops and their scalar descriptors such as nucleation bias, coercive bias and the hysteresis loop area (or more complex functionals of hysteresis loop shape) and corresponding uncertainties are used to guide the discovery of these relationships via automated piezoresponse force microscopy and spectroscopy experiments. As such, this approach combines the power of machine learning methods to learn the correlative relationships between high-dimensional data, as well as human-based physics insights encoded into the acquisition function. For ferroelectric materials, this automated workflow demonstrates that the discovery path and sampling points of on- and off-field hysteresis loops are largely different, indicating that on- and off-field hysteresis loops are dominated by different mechanisms. Here, the proposed approach is universal and can be applied to a broad range of modern imaging and spectroscopy methods ranging from other scanning probe microscopy modalities to electron microscopy and chemical imaging.
36 MATERIALS SCIENCE↗
LTAU-FF: Loss Trajectory Analysis for Uncertainty in atomistic Force Fields
Model ensembles are effective tools for estimating prediction uncertainty in deep learning atomistic force fields. However, their widespread adoption is hindered by high computational costs and overconfident error estimates. In this work, we address these challenges by leveraging distributions of per-sample errors obtained during training and employing a distance-based similarity search in the model latent space. Our method, which we call LTAU (Loss Trajectory Analysis for Uncertainty), efficiently estimates the full probability distribution function of errors for any test point using the logged training errors, achieving speeds that are 2–3 orders of magnitudes faster than typical ensemble methods and allowing it to be used for tasks where training or evaluating multiple models would be infeasible. We apply LTAU towards estimating parametric uncertainty in atomistic force fields (LTAU-FF), demonstrating that it produces well-calibrated confidence intervals and predicts errors that correlate strongly with the true errors for data near the training domain. Furthermore, we show that the errors predicted by LTAU-FF can be used in practical applications for detecting out-of-domain data, tuning model performance, and predicting failure during simulations. We believe that LTAU will be a valuable tool for uncertainty quantification in atomistic force fields and is a promising method that should be further explored in other domains of machine learning.
97 MATHEMATICS AND COMPUTING↗
Density-functional theory of material design: fundamentals and applications-I
This article is part-I of a review of density-functional theory (DFT) that is the most widely used method for calculating electronic structure of materials. The accuracy and ease of numerical implementation of DFT methods has resulted in its extensive use for materials design and discovery and has thus ushered in the new field of computational material science. In this article, we start with an introduction to Schrödinger equation and methods of its solutions. After presenting exact results for some well-known systems, difficulties encountered in solving the equation for interacting electrons are described. How these difficulties are handled using the variational principle for the energy to obtain approximate solutions of the Schrödinger equation is discussed. The resulting Hartree and Hartree–Fock theories are presented along with results they give for atomic and solid-state systems. We then describe Thomas–Fermi theory and its extensions which were the initial attempts to formulate many-electron problem in terms of electronic density of a system. Having described these theories, we introduce modern DFT by discussing Hohenberg–Kohn theorems that form its foundations. We then go on to discuss Kohn–Sham (KS) formulation of DFT in its exact form. Next, local density approximation (LDA) is introduced and solutions of KS equation for some representative systems, obtained using the LDA, are presented. We end part-I of the review describing the contents of part-II.
36 MATERIALS SCIENCE↗
Uncertainty Quantification of Metal Additive Manufacturing Processing Conditions Through the use of Exascale Computing
Metal additive manufacturing (AM) is a disruptive manufacturing technology that opens the design space for parts outside those possible from traditional manufacturing methods. In order to accelerate industry and R&D needs to certify AM parts, the Exascale Additive Manufacturing project (ExaAM) has developed a suite of exascale-ready computational tools to model the process-to-structure-to-properties (PSP) relationship for additively manufactured metal components. One such tool is an uncertainty quantification (UQ) pipeline to quantify the effect that uncertainty in processing conditions has on local mechanical responses. We present an overview of this pipeline and its required simulation and workflow codes. Using the Oak Ridge National Laboratory’s (ORNL) exascale computer, Frontier, we utilize this pipeline to cross multiple length and time scales to predict the local mechanical response of a location within a complex AM bridge part, AMB2018-01 produced by the National Institute of Standards and Technology (NIST) as part of their 2018 AM-Bench test series. Our results are then compared to experimental mechanical tests of parts from the NIST build to quantify the error in the ExaAM UQ workflow.
Carson, Robert↗
Representations of Materials for Machine Learning
High-throughput data generation methods and machine learning (ML) algorithms have given rise to a new era of computational materials science by learning the relations between composition, structure, and properties and by exploiting such relations for design. However, to build these connections, materials data must be translated into a numerical form, called a representation, that can be processed by an ML model. Data sets in materials science vary in format (ranging from images to spectra), size, and fidelity. Predictive models vary in scope and properties of interest. Here, we review context-dependent strategies for constructing representations that enable the use of materials as inputs or outputs for ML models. Furthermore, we discuss how modern ML techniques can learn representations from data and transfer chemical and physical information between tasks. Finally, we outline high-impact questions that have not been fully resolved and thus require further investigation.
36 MATERIALS SCIENCE↗
Overview presentation to SSTAC/ARTS Review Committee
An overview of the Materials and Structures Integrated Technology Plan (ITP) is presented in outline and graphic form. The base R&T funding and the Civil Space Program work breakdown structure is given. Current programs, state-of-the-art, and technology needs are described for each of several research areas including space materials, space structures and structural dynamics, aerothermal materials and structures, computational materials science and chemistry, power and propulsion materials, space environmental effects, thermal protection systems, and space debris.
Venneri, Samuel L.↗
High Temperature Calibration Furnace System user's guide
The High Temperature Calibration Furnace System (HTCFS) was developed by Summitec Corporation. It is a high precision instrument providing a constant temperature which can be used to calibrate high temperature thermocouples. Incorporating the many recent technological advances from the fields of optical fiber thermometry, material science, computer systems interfacing, and process control, the engineers at Summitec Corporation have been able to create a system that can reach a steady operating temperature of 1700 C. The precision for the system requires the measurement of temperature to be within 1 C in two hours and within 2 C in 24 hours. As documented, the experimental result shows that this system has been able to stay within .5 C in 5 hours. No other systems commercially available have been able to achieve such high temperature precision. This manual provides an overview of the system design, instructions for instrument setup, and operation procedures. Also included are a vendor list and the source codes for the custom-designed software.
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Technical Reports: Langley Aerospace Research Summer Scholars
The Langley Aerospace Research Summer Scholars (LARSS) Program was established by Dr. Samuel E. Massenberg in 1986. The program has increased from 20 participants in 1986 to 114 participants in 1995. The program is LaRC-unique and is administered by Hampton University. The program was established for the benefit of undergraduate juniors and seniors and first-year graduate students who are pursuing degrees in aeronautical engineering, mechanical engineering, electrical engineering, material science, computer science, atmospheric science, astrophysics, physics, and chemistry. Two primary elements of the LARSS Program are: (1) a research project to be completed by each participant under the supervision of a researcher who will assume the role of a mentor for the summer, and (2) technical lectures by prominent engineers and scientists. Additional elements of this program include tours of LARC wind tunnels, computational facilities, and laboratories. Library and computer facilities will be available for use by the participants.
Schwan, Rafaela↗
Langley Aerospace Research Summer Scholars
The Langley Aerospace Research Summer Scholars (LARSS) Program was established by Dr. Samuel E. Massenberg in 1986. The program has increased from 20 participants in 1986 to 114 participants in 1995. The program is LaRC-unique and is administered by Hampton University. The program was established for the benefit of undergraduate juniors and seniors and first-year graduate students who are pursuing degrees in aeronautical engineering, mechanical engineering, electrical engineering, material science, computer science, atmospheric science, astrophysics, physics, and chemistry. Two primary elements of the LARSS Program are: (1) a research project to be completed by each participant under the supervision of a researcher who will assume the role of a mentor for the summer, and (2) technical lectures by prominent engineers and scientists. Additional elements of this program include tours of LARC wind tunnels, computational facilities, and laboratories. Library and computer facilities will be available for use by the participants.
Schwan, Rafaela↗
Ultrahigh-Temperature Ceramics
Ultrahigh temperature ceramics (UHTCs) are a class of materials that include the diborides of metals such as hafnium and zirconium. The materials are of interest to NASA for their potential utility as sharp leading edges for hypersonic vehicles. Such an application requires that the materials be capable of operating at temperatures, often in excess of 2,000 C. UHTCs are highly refractory and have high thermal conductivity, an advantage for this application. UHTCs are potentially applicable for other high-temperature processing applications, such as crucibles for molten-metal processing and high-temperature electrodes. UHTCs were first studied in the 1960 s by the U.S. Air Force. NASA s Ames Research Center concentrated on developing materials in the HfB2/SiC family for a leading-edge application. The work focused on developing a process to make uniform monolithic (2-phase) materials, and on the testing and design of these materials. Figure 1 shows arc-jet models made from UHTC materials fabricated at Ames. Figure 2 shows a cone being tested in the arc-jet. Other variations of these materials being investigated elsewhere include zirconium based materials and fiber-reinforced composites. Current UHTC work at Ames covers four broad topics: monoliths, coatings, composites, and processing. The goals include improving the fracture toughness, thermal conductivity and oxidation resistance of monolithic UHTCs and developing oxidation-resistant UHTC coatings for thermal-protection-system substrates through novel coating methods. As part of this effort, researchers are exploring compositions and processing changes that have yielded improvements in properties. Computational materials science and nanotechnology are being explored as approaches to reduce materials development time and improve and tailor properties.
Johnson, Sylvia M.↗
NASA Tech Briefs, June 1999
Topics: Imaging/Video/Cameras; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Software; Mechanics; Machinery/Automation; Computers and Peripherals.
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NASA Tech Briefs, July 1999
Topics: Test and Measurement; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Software; Mechanics; Machinery/Automation; Bio-Medical; Books and Reports; Semiconductors/ICs.
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NASA Tech Briefs, August 1999
Topics include: Graphics and Simulation; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery/Automation;
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NASA Tech Briefs, December 1999
Topics include: Imaging/Videos/Cameras; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery/Automation; Books and Reports.
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NASA Tech Briefs, September 2000
Topics include: Sensors; Test and Measurement; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery/Automation; Bio-Medical; semiconductors/ICs; Books and Reports.
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