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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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DOE OSTI2026

Cooperative Education

Los Alamos National Laboratory (LANL) is a multidisciplinary national laboratory that conducts research and development in national security, engineering, materials science, computational modeling, and advanced manufacturing. The laboratory develops innovative technologies to address complex scientific and engineering challenges. This project focuses on the development and evaluation of high-performance absorbing structures through computational design, simulation, and engineering analysis. Absorbing structures are used in applications where damage mitigation, structural protection, and material efficiency are critical performance requirements. The increasing demand for lightweight, high-strength, and highly efficient structural systems has created a need for improved design methodologies capable of maximizing absorption while minimizing weight and material usage. The project utilizes advanced engineering software, including 3D CAD software and FEA, to generate and optimize structural concepts. Computational simulations are performed to evaluate structural behavior under loading conditions, while mathematical analyses are conducted using Python-based tools as well as established analytical equations from material and structural mechanics. The project benefits LANL by supporting the development of advanced design methodologies and improving the understanding of material and structural performance. During the internship term, a significant portion of the design development, simulation, and data analysis activities will be completed. Success of the project depends on collaboration among engineering mentors and technical staff members. Work will be conducted at Los Alamos National Laboratory using laboratory computing resources and engineering software.

42 ENGINEERING↗

NASA NTRS2001

Materials Overview

The Materials and Structures for High Performance project has made excellent progress in the development of advanced high-temperature materials and computational materials science tools to enable high-performance, hgh-efficiency, and environmentally compatible propulsion systems. Ceramic matrix composite (CMC) systems with 2700 O F temperature capability are initially being developed for low NO, combustor liners and turbine vanes. The feasibility of pushing CMC technologies to 3000 O F through revolutionary concepts is also being pursued. Achieving fuel savings of 8 to 15 percent requires higher turbine inlet temperatures as well as reductions in engine weight. An advanced disk alloy has been evaluated for application in future engines. Advanced thermal barrier coatings (TBC s) will enable 3200 O F turbine rotor inlet temperature capability. Advanced alloy development and the development of computational tools for the design of these future alloys will further enable attainment of these goals. Innovative lightweight materials, and structural and nozzle aerodynamic concepts are also being developed to reduce the weight of engine static structures to contribute toward overall fuel savings. Rig tests will demonstrate long-term durability of CMC linersand vanes and advanced turbine airfoil alloy systems, as well as fabricability of components with the highest payoff potential using the lightweight materials and structures concepts being developed.

Draper, Robert↗

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 OSTI2026

Los Alamos National Laboratory R&D Intern

This report details the responsibilities, outcomes, and project details of a summer R&D internship at Los Alamos National Laboratory (LANL). LANL is a multidisciplinary laboratory that focuses on current cutting-edge research in many fields such as national security, engineering, materials science, computational modeling, and advanced manufacturing. The goal of the internship project was to work with lab engineers and resources to develop an energy absorbing structure for high-velocity impact applications. The successful development of this technology and methodology would not only positively impact future project funding but also contribute to the laboratory's commitment to solve national security challenges through simultaneous excellence. Such devices would also support efforts surrounding the research and development of energy absorbing structures and would provide new vital information backed by experimentation. Different computational and modeling methods were used to design these structures, in addition to qualitative background information provided by past literature. The resultant designs were successfully tested, and the test results were successfully quantified. From these results, new computational methods were developed through python programming and modeling to predict ideal materialistic properties for these structures given a sufficiently defined application.

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

Advanced Materials for the Lunar Surface: Multiscale Computational Design of Refractory Alloys and Carbides

Emerging operational environments, such as the lunar surface, present novel challenges for NASA and drive the need for advanced materials in applications like fission surface power systems. To address these demands, computational materials science is rapidly evolving to augment or replace costly and hazardous empirical testing. Although materials selection at NASA remains predominantly experimentally driven, advanced simulation methodologies are being steadily integrated into the engineering lifecycle. This work details the application of multiscale simulation techniques—including first-principles calculations, CALPHAD, dislocation dynamics, and molecular dynamics—at NASA's Ames Research Center to evaluate advanced materials for extreme environments. First, we present contributions to the Space Nuclear Propulsion Project. Be-cause propellant channel coatings in nuclear thermal rockets must withstand high-pressure, high-temperature hydro-gen, optimizing these materials is critical. First-principles calculations were employed to establish a rigorous quantitative and qualitative understanding of the behavior of the refractory carbides ZrC, NbC, and their mixtures in high-enthalpy hydrogen environments. This necessitated the generation of high-fidelity thermodynamic models for both stoichiometric and carbon-depleted carbides, both with and without the presence of hydrogen. Furthermore, we highlight efforts under the Refractory Alloy Additive Manufacturing Build Optimization (RAAMBO) project, where existing and novel alloy compositions were assessed for additive manufacturing printability and subsequent performance in applications such as heat pipes and rocket nozzle extensions. This was accomplished through a comprehensive multiscale simulation framework that bridged the gap from the nanometer to the millimeter scale. Across both initiatives, rigorous validation against empirical data was prioritized. By systematically employing a verified and validated computational frame-work, we demonstrate how simulation effectively supports multidisciplinary engineering efforts, builds project-wide confidence, and drives critical materials development.

computational materials↗

DOE OSTI2025

The design space of E(3)-equivariant atom-centred interatomic potentials

Abstract Molecular dynamics simulation is an important tool in computational materials science and chemistry, and in the past decade it has been revolutionized by machine learning. This rapid progress in machine learning interatomic potentials has produced a number of new architectures in just the past few years. Particularly notable among these are the atomic cluster expansion, which unified many of the earlier ideas around atom-density-based descriptors, and Neural Equivariant Interatomic Potentials (NequIP), a message-passing neural network with equivariant features that exhibited state-of-the-art accuracy at the time. Here we construct a mathematical framework that unifies these models: atomic cluster expansion is extended and recast as one layer of a multi-layer architecture, while the linearized version of NequIP is understood as a particular sparsification of a much larger polynomial model. Our framework also provides a practical tool for systematically probing different choices in this unified design space. An ablation study of NequIP, via a set of experiments looking at in- and out-of-domain accuracy and smooth extrapolation very far from the training data, sheds some light on which design choices are critical to achieving high accuracy. A much-simplified version of NequIP, which we call BOTnet (for body-ordered tensor network), has an interpretable architecture and maintains its accuracy on benchmark datasets.

Computer Science↗

DOE OSTI2024

Learning dislocation dynamics mobility laws from large-scale MD simulations

Not Available

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

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↗

NASA NTRS1991

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.↗

NASA NTRS1994

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.

Source record↗

NASA NTRS1995

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↗

NASA NTRS1995

Langley Aerospace Research Summer Scholars

Schwan, Rafaela↗

NASA NTRS2007

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 NTRS1999

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 NTRS1999

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 NTRS1999

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 NTRS1999

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 NTRS2000

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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