Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “material 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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Comparative Study of Large Language Model Architectures on Frontier

Large language models (LLMs) have garnered significant attention in both the AI community and beyond. Among these, the Generative Pre-trained Transformer (GPT) has emerged as the dominant architecture, spawning numerous variants. However, these variants have undergone pre-training under diverse conditions, including variations in input data, data preprocessing, and training methodologies, resulting in a lack of controlled comparative studies. Here we meticulously examine two prominent open-sourced GPT architectures, GPT-NeoX and LLaMA, leveraging the computational power of Frontier, the world’s first Exascale supercomputer. Employing the same materials science text corpus and a comprehensive end-to-end pipeline, we conduct a comparative analysis of their training and downstream performance. Our efforts culminate in achieving state-of-the-art performance on a challenging materials science benchmark. Furthermore, we investigate the computation and energy efficiency, and propose a computationally efficient method for architecture design. To our knowledge, these pre-trained models represent the largest available for materials science. Our findings provide practical guidance for building LLMs on HPC platforms.

Yin, Junqi↗
DOE OSTI2023

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↗
NASA NTRS2000

The Electrostatic Levitation Facility at NASA's Marshall Space Flight Center

Containerless processing is an important area of research in materials science. Electrostatic levitation (ESL) represents an emerging technology which permits containerless processing in a vacuum environment. NASA's Marshall Space Flight Center (MSFC) established a levitation facility to provide a critical resource to the microgravity materials science research community to continue and enhance ground-based research in the support of the development of flight experiments during the transition to Space Station. During ESL processing, charged specimens are levitated in the electrostatic field produced by the system's electrodes. Three sets of positioning electrodes represent the heart of the MSFC system. Two dual-axis position sensitive detectors provide input for the PID control-loop computer. Sample position is maintained by adjusting the control voltages for the power supplies of the positioning electrodes. A UV source refreshes the charge on specimens during processing via the photoelectric effect. Lasers permit sample heating independent of positioning. The processing chamber typically operates under vacuum condition approximately = 10(exp -7) Torr. Electrostatic levitation provides a materials science research tool for investigations of refractory solids and melts. Topics of investigation include thermophysical properties, phase equilibria, metastable phase formation, undercooling and nucleation, time-temperature-transformation diagrams and other aspects of materials processing. Current capabilities and recent results of processing studies for metals, alloys and oxides will be reviewed.

Rogers, Jan R.↗
DOE OSTI2021

Enabling Particulate Materials Processing Science for High-Consequence, Small-Lot Precision Manufacturing

This Laboratory Directed Research and Development project developed and applied closely coupled experimental and computational tools to investigate powder compaction across multiple length scales. The primary motivation for this work is to provide connections between powder feedstock characteristics, processing conditions, and powder pellet properties in the context of powder-based energetic components manufacturing. We have focused our efforts on multicrystalline cellulose, a molecular crystalline surrogate material that is mechanically similar to several energetic materials of interest, but provides several advantages for fundamental investigations. We report extensive experimental characterization ranging in length scale from nanometers to macroscopic, bulk behavior. Experiments included nanoindentation of well-controlled, micron-scale pillar geometries milled into the surface of individual particles, single-particle crushing experiments, in-situ optical and computed tomography imaging of the compaction of multiple particles in different geometries, and bulk powder compaction. In order to capture the large plastic deformation and fracture of particles in computational models, we have advanced two distinct meshfree Lagrangian simulation techniques: 1.) bonded particle methods, which extend existing discrete element method capabilities in the Sandia-developed , open-source LAMMPS code to capture particle deformation and fracture and 2.) extensions of peridynamics for application to mesoscale powder compaction, including a novel material model that includes plasticity and creep. We have demonstrated both methods for simulations of single-particle crushing as well as mesoscale multi-particle compaction, with favorable comparisons to experimental data. We have used small-scale, mechanical characterization data to inform material models, and in-situ imaging of mesoscale particle structures to provide initial conditions for simulations. Both mesostructure porosity characteristics and overall stress-strain behavior were found to be in good agreement between simulations and experiments. We have thus demonstrated a novel multi-scale, closely coupled experimental and computational approach to the study of powder compaction. This enables a wide range of possible investigations into feedstock-process-structure relationships in powder-based materials, with immediate applications in energetic component manufacturing, as well as other particle-based components and processes.

36 MATERIALS SCIENCE↗
NASA NTRS2002

QMI: Rising to the Space Station Design Challenge

The Quench Module Insert (QMI) materials processing furnace is being designed to operate for 8000 hours over four years on the International Space Station (ISS) as part of the first Materials Science Research Rack (MSRR-1) of the Materials Science Research Facility (MSRF). The Bridgman-type furnace is being built for the directional solidification processing of metals and alloys in the microgravity environment of space. Most notably it will be used for processing aluminum and related alloys. Designing for the space station environment presents intriguing design challenges in the form of a ten-year life requirement coupled with both limited opportunities for maintenance and resource constraints in the form of limited power and space. The long life requirement has driven the design of several features in the furnace, including the design of the heater core, the selection and placement of the thermocouples, overall performance monitoring, and the design of the chill block. The power and space limitations have been addressed through a compact furnace design using efficient vacuum insulation. Details on these design features, as well as development test performance results to date, are presented.

Carswell, W. E.↗
NASA NTRS1965

Research on fiber-reinforced composites

Research in materials science - high temperature structural materials, wetting and adherence of nickel alloys to sapphire, boron carbide filaments, and ceramic and graphite fibers

MATERIALS SCIENCE↗
DOE OSTI2020

Plasmonics in argentene

Merging concepts from the fields of ab initio materials science and nanophotonics, there is now an opportunity to engineer new photonic materials whose optical, transport, and scattering properties are tailored to attain thermodynamic and quantum limits. Here we present first-principles calculations predicting that Argentene, a single-crystalline hexagonal close-packed monolayer of Ag, can dramatically surpass the optical properties and electrical conductivity of conventional plasmonic materials. In the low-frequency limit, we show that the scattering rate and resistivity reduce by a factor of 3 compared to the bulk three-dimensional metal. Most importantly, the low scattering rate extends to optical frequencies in sharp contrast to, e.g., graphene, whose scattering rate increase drastically in the near-infrared range due to optical-phonon scattering. Combined with an intrinsically high carrier density, this facilitates highly confined surface plasmons extending to visible frequencies. We evaluate Argentene across three distinct figures of merit, in each outperforming the state-of-the-art, making it a valuable addition to the two-dimensional heterostructure toolkit for quantum optoelectronics.

36 MATERIALS SCIENCE↗
DOE OSTI2025

SA-GAT-SR: self-adaptable graph attention networks with symbolic regression for high-fidelity material property prediction

Recent advances in machine learning have demonstrated an enormous utility of deep learning approaches, particularly Graph Neural Networks (GNNs) for materials science. These methods have emerged as powerful tools for high-throughput prediction of material properties, offering a compelling enhancement and alternative to traditional first-principles calculations. While the community has predominantly focused on developing increasingly complex and universal models to enhance predictive accuracy, such approaches often lack physical interpretability and insights into materials behavior. Here, we introduce a novel computational paradigm—Self-Adaptable Graph Attention Networks integrated with Symbolic Regression (SA-GAT-SR)—that synergistically combines the predictive capability of GNNs with the interpretative power of symbolic regression. Our framework employs a self-adaptable encoding algorithm that automatically identifies and adjust attention weights so as to screen critical features from an expansive 180-dimensional feature space while maintaining O(n) computational scaling. The integrated SR module subsequently distills these features into compact analytical expressions that explicitly reveal quantum-mechanically meaningful relationships, achieving 23 × acceleration compared to conventional SR implementations that heavily rely on first-principle calculations-derived features as input. This work suggests a new framework in computational materials science, bridging the gap between predictive accuracy and physical interpretability, offering valuable physical insights into material behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Resilient GaN-based Power Module with Improved Diagnostic for Future Accelerator

Los Alamos Neutron Science Center (LANSCE) and Dual-Axis Radiographic Hydrodynamic Test facility (DARHT) play a pivotal role in advancing scientific research and national security initiatives. As a premier facility for neutron science, LANSCE and DARHT provide invaluable insights into fundamental research, materials science, nuclear physics, high-energy physics, material science, and medical imaging. Existing (DC) power supply and pulse modulation technologies used in LANSCE and DARHT face limitations in terms of reliability, maintainability, and diagnostics which hinder the progress of these cutting-edge facilities. By proposing improvements to LANL accelerator facilities’ performance and addressing component failure, we aim to enhance scientific outcomes and minimize disruptions, allowing for more efficient and productive research activities while ensuring the continuity of valuable contributions to the user community. This proposal aims to revolutionize (DC) power supply and pulse power systems for future particle accelerators via the development of a resilient GaN-based power module with improved diagnostic and redundant submodules.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Polycrystalline Thin-Film Research: Copper Indium Gallium Diselenide

Copper indium gallium diselenide (CIGS) solar cells are one of the primary focuses of research by the Thin Film Material Science and Processing Group. The group develops processes and materials related to thin-film polycrystalline photovoltaic (PV) devices as well as the equipment required for routine analysis of these devices and materials. We work closely with other groups in the Materials Science Center to achieve a deeper understanding of thin-film materials and devices.

capabilities↗
DOE OSTI2021

Materials representation and transfer learning for multi-property prediction

The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements as well as the relationships among multiple properties to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates: (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 three-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data are available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with transfer learning [H-CLMP(T)] wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well suited for multi-target regression across the physical sciences.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine learning approaches for intentional materials engineering

In this article, the development of nanoporous metals and metallic composites through dealloying processes presents significant opportunities in materials engineering. However, designing multicomponent precursor alloys and establishing corresponding processing methods that yield predictable compositions and nanostructures remain a complex challenge. This article explores how machine learning (ML)-augmented computational and experimental methodologies can tackle these challenges by predicting precursor alloy compositions, final nanoporous structures, and mechanical properties, while integrating ML-enabled autonomous experimentation for material design and quantification. We highlight recent advancements in applying ML to nanostructured materials design via dealloying and discuss how techniques from other nanomaterial designs can be adapted for improved control over morphological and compositional outcomes in nanoporous and nanocomposite materials. Furthermore, we explore the role of ML in autonomous synchrotron x-ray experimentation, enabling real-time feedback between modeling and experimental setups. ML-driven approaches to microstructure characterization and mechanical property prediction are also examined, with a focus on modeling and advanced imaging techniques such as three-dimensional nanotomography. Finally, this article outlines future directions for ML-enhanced materials science, emphasizing the exploration of high-dimensional parameter spaces and the incorporation of materials kinetics into processing and property evaluation, ultimately advancing the design of nanoporous structures and materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Sandia / IBM Discussion on Machine Learning for Materials Applications [Slides]

This report includes a compilation of several slide presentations: 1) Interatomic Potentials for Materials Science and Beyond–Advances in Machine Learned Spectral Neighborhood Analysis Potentials (Wood); 2) Agile Materials Science and Advanced Manufacturing through AI/ML (de Oca Zapiain); 3) Machine Learning for DFT Calculations (Rajamanickam); 4) Structure-preserving ML discovery of a quantum-to-continuum codesign stack (Trask); and 5) IBM Overview of Accelerated Discovery Technology (Pitera)

36 MATERIALS SCIENCE↗
DOE OSTI2025

Constrained GAN-Generated X-Ray CT Data For Self-Supervised And Foundation-Model Segmentation Of Concrete Microstructures

Three-dimensional characterization of materials using X-ray computed tomography (XCT) is challenging due to the complexity of internal structures, noise, and variations in resolution. Traditional computer vision models often struggle to accurately segment these images, particularly in domain-specific applications like materials science. While supervised deep learning approaches have been developed to address the limitations of conventional algorithms, they typically require large amounts of labeled training data and often fail to generalize across different datasets. Self-supervised, few-and zero-shot learning methods have gained prominence in natural image processing and segmentation tasks, but their application to scientific imaging remains limited due to the unique structural complexity, noise, and textural artifacts present in materials science data. In this work, we investigate how domain adaptation, leveraging physics-based and GAN-generated synthetic data, impacts segmentation performance. We introduce a modified Contrastive Unpaired Translation (CUT) model designed to generate realistic labeled data, which can be used for training, pre-training, and fine-tuning segmentation models for real XCT microstructure data. We evaluate the performance of two segmentation approaches: a self-supervised network (SSL-ALPNet) and a foundation model (Segment Anything Model), assessing their improvements when pre-trained and/or fine-tuned on the synthesized data. Our results demonstrate that leveraging synthetic data significantly enhances segmentation performance, particularly in challenging materials science applications.

Ziabari, Amir [ORNL] (ORCID:000000034776457X)↗
DOE OSTI2020

Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels

Abstract A majority of experimental disciplines face the challenge of exploring large and high-dimensional parameter spaces in search of new scientific discoveries. Materials science is no exception; the wide variety of synthesis, processing, and environmental conditions that influence material properties gives rise to particularly vast parameter spaces. Recent advances have led to an increase in the efficiency of materials discovery by increasingly automating the exploration processes. Methods for autonomous experimentation have become more sophisticated recently, allowing for multi-dimensional parameter spaces to be explored efficiently and with minimal human intervention, thereby liberating the scientists to focus on interpretations and big-picture decisions. Gaussian process regression (GPR) techniques have emerged as the method of choice for steering many classes of experiments. We have recently demonstrated the positive impact of GPR-driven decision-making algorithms on autonomously-steered experiments at a synchrotron beamline. However, due to the complexity of the experiments, GPR often cannot be used in its most basic form, but rather has to be tuned to account for the special requirements of the experiments. Two requirements seem to be of particular importance, namely inhomogeneous measurement noise (input-dependent or non-i.i.d.) and anisotropic kernel functions, which are the two concepts that we tackle in this paper. Our synthetic and experimental tests demonstrate the importance of both concepts for experiments in materials science and the benefits that result from including them in the autonomous decision-making process.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Perspectives of 2D MXene Tribology

Abstract The large and rapidly growing family of 2D early transition metal carbides, nitrides, and carbonitrides (MXenes) raises significant interest in the materials science and chemistry of materials communities. Discovered a little more than a decade ago, MXenes have already demonstrated outstanding potential in various applications ranging from energy storage to biology and medicine. The past two years have witnessed increased experimental and theoretical efforts toward studying MXenes’ mechanical and tribological properties when used as lubricant additives, reinforcement phases in composites, or solid lubricant coatings. Although research on the understanding of the friction and wear performance of MXenes under dry and lubricated conditions is still in its early stages, it has experienced rapid growth due to the excellent mechanical properties and chemical reactivities offered by MXenes that make them adaptable to being combined with other materials, thus boosting their tribological performance. In this perspective, the most promising results in the area of MXene tribology are summarized, future important problems to be pursued further are outlined, and methodological recommendations that could be useful for experts as well as newcomers to MXenes research, in particular, to the emerging area of MXene tribology, are provided.

2D materials↗
NASA NTRS1988

Electronic materials high-T(sub c) superconductivity polymers and composites structural materials surface science and catalysts industry participation

The fifth year of the Center for Advanced Materials was marked primarily by the significant scientific accomplishments of the research programs. The Electronics Materials program continued its work on the growth and characterization of gallium arsenide crystals, and the development of theories to understand the nature and distribution of defects in the crystals. The High Tc Superconductivity Program continued to make significant contributions to the field in theoretical and experimental work on both bulk materials and thin films and devices. The Ceramic Processing group developed a new technique for cladding YBCO superconductors for high current applications in work with the Electric Power Research Institute. The Polymers and Composites program published a number of important studies involving atomistic simulations of polymer surfaces with excellent correlations to experimental results. The new Enzymatic Synthesis of Materials project produced its first fluorinated polymers and successfully began engineering enzymes designed for materials synthesis. The structural Materials Program continued work on novel alloys, development of processing methods for advanced ceramics, and characterization of mechanical properties of these materials, including the newly documented characterization of cyclic fatigue crack propagation behavior in toughened ceramics. Finally, the Surface Science and Catalysis program made significant contributions to the understanding of microporous catalysts and the nature of surface structures and interface compounds.

Source record↗