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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Intelligent systems in space : the EO-1 Autonomous Sciencecraft

The Autonomous Sciencecraft Software (ASE) is currently flying onboard the Earth Observing One (EO-1) Spacecraft. This software enables the spacecraft to autonomously detect and respond to science events occurring on the Earth. The package includes software systems that perform science data analysis, deliberative planning, and runtime robust execution. Because of the deployment to the EO-1 spacecraft, the ASE software has stringent constraints of autonomy and limited computing resources. We describe these constraints and how they are reflected in our operations approach. A summary of the final results of the experiment is also included. This software has demonstrated the potential for space missions to use onboard decision-making to detect, analyze, and respond to science events, and to downlink only the highest value science data. As a result, ground-based mission planning and analysis functions have been greatly simplified, thus reducing operations cost.

Earth Observing One (EO-1) Spacecraft↗
NASA NTRS1997

Parametric Flutter Analysis of the TCA Configuration and Recommendation for FFM Design and Scaling

The current HSR Aeroelasticity plan to design, build, and test a full span, free flying transonic flutter model in the TDT has many technical obstacles that must be overcome for a successful program. One technical obstacle is the determination of a suitable configuration and point in the sky to use in setting the scaling point for the ASE models program. Determining this configuration and point in the sky requires balancing several conflicting requirements, including model buildability, tunnel test safety, and the ability of the model to represent the flutter mechanisms of interest. As will be discussed in detail in subsequent sections, the current TCA design exhibits several flutter mechanisms of interest. It has been decided that the ASE models program will focus on the low frequency symmetric flutter mechanism, and will make no attempt to investigate high frequency flutter mechanisms. There are several reasons for this choice. First, it is believed that the high frequency flutter mechanisms are similar in nature to classical wing bending/torsion flutter, and therefore there is more confidence that this mechanism can be predicted using current techniques. The low frequency mode, on the other hand, is a highly coupled mechanism involving wing, body, tail, and engine motion which may be very difficult to predict. Second, the high frequency flutter modes result in very small weight penalties (several hundred pounds), while suppression of the low frequency mechanism inside the flight envelope causes thousands of pounds to be added to the structure. In order to successfully test the low frequency flutter mode of interest, a suitable starting configuration and point in the sky must be identified. The configuration and point in the sky must result in a wind tunnel model that (1) represents the low-frequency wing/body/engine/empennage flutter mechanisms that are unique to HSCT configurations, (2) flutters at an acceptably low frequency in the tunnel, (3) flutters at an acceptably low dynamic pressure in the tunnel, (4) allows sufficient weight for model buildability without inordinately high cost, and (5) has significant separation between the target flutter mechanism and other, potentially catastrophic, flutter mechanisms.

Baker, Myles↗
NASA NTRS2010

Concepts for a NASA Applied Spaceflight Environments Office

The National Aeronautics and Space Administration (NASA) is launching a bold and ambitious new space initiative. A significant part of this new initiative includes exploration of new worlds, the development of more innovative technologies, and expansion our presence in the solar system. A common theme to this initiative is the exploration of space beyond Low Earth Orbit (LEO). As currently organized, NASA does not have an Agency-level office that provides coordination of space environment research and development. This has contributed to the formation of a gap between spaceflight environments knowledge and the application of this knowledge for multi-program use. This paper outlines a concept to establish a NASA-level Applied Spaceflight Environments (ASE) office that will provide coordination and funding for sustained multi-program support in three technical areas that have demonstrated these needs through customer requests. These technical areas are natural environments characterization and modeling, materials and systems analysis and test, and operational space environments modeling and prediction. This paper will establish the need for the ASE, discuss a concept for organizational structure and outline the scope in the three technical areas

Edwards, David L.↗
NASA NTRS2010

A Coordinated Effort to Address Space Weather and Environment Needs

The growing need for coordination of the many aspects of space environments is directly related to our increasing dependence on space assets. An obvious result is that there is a need for a coordinated effort to organize and make accessible the increasing number of space environment products that include space environment models and observations, material testing, and forecasting tools. This paper outlines a concept to establish a NASA-level Applied Spaceflight Environments (ASE) office that will provide coordination and funding for sustained multi-program support in three technical areas; (1) natural environments characterization and modeling, (2) environmental effects on materials and systems, (3) and operational and forecasting space environments modeling. Additionally the ASE office will serve as an entry point of contact for external users who wish to take advantage of data and assets associated with space environments, including space weather.

Minow, Joe↗
NASA NTRS2005

High-Power Fiber Lasers Using Photonic Band Gap Materials

High-power fiber lasers (HPFLs) would be made from photonic band gap (PBG) materials, according to the proposal. Such lasers would be scalable in the sense that a large number of fiber lasers could be arranged in an array or bundle and then operated in phase-locked condition to generate a superposition and highly directed high-power laser beam. It has been estimated that an average power level as high as 1,000 W per fiber could be achieved in such an array. Examples of potential applications for the proposed single-fiber lasers include welding and laser surgery. Additionally, the bundled fibers have applications in beaming power through free space for autonomous vehicles, laser weapons, free-space communications, and inducing photochemical reactions in large-scale industrial processes. The proposal has been inspired in part by recent improvements in the capabilities of single-mode fiber amplifiers and lasers to produce continuous high-power radiation. In particular, it has been found that the average output power of a single strand of a fiber laser can be increased by suitably changing the doping profile of active ions in its gain medium to optimize the spatial overlap of the electromagnetic field with the distribution of active ions. Such optimization minimizes pump power losses and increases the gain in the fiber laser system. The proposal would expand the basic concept of this type of optimization to incorporate exploitation of the properties (including, in some cases, nonlinearities) of PBG materials to obtain power levels and efficiencies higher than are now possible. Another element of the proposal is to enable pumping by concentrated sunlight. Somewhat more specifically, the proposal calls for exploitation of the properties of PBG materials to overcome a number of stubborn adverse phenomena that have impeded prior efforts to perfect HPFLs. The most relevant of those phenomena is amplified spontaneous emission (ASE), which causes saturation of gain and power at undesirably low levels, and scattering of light from dopants. In designing a given fiber laser for reduced ASE, care must be taken to maintain a correct fiber structure for eventual scaling to an array of many such lasers such that the interactions among all the members of the array would cause them to operate in phase lock. Hence, the problems associated with improving a single-fiber laser are not entirely separate from the bundling problem, and some designs for individual fiber lasers may be better than others if the fibers are to be incorporated into bundles. Extensive calculations, expected to take about a year, must be performed in order to determine design parameters before construction of prototype individual and fiber lasers can begin. The design effort can be expected to include calculations to optimize overlaps between the electromagnetic modes and the gain media and calculations of responses of PBG materials to electromagnetic fields. Design alternatives and physical responses that may be considered include simple PBG fibers with no intensity-dependent responses, PBG fibers with intensity- dependent band-gap shifting (see figure), and broad-band pumping made possible by use of candidate broad-band pumping media in place of the air or vacuum gaps used in prior PBG fibers.

DiDomenico, Leo↗
NASA NTRS2012

Computational Aeroelastic Analysis of the Semi-Span Super-Sonic Transport (S4T) Wind-Tunnel Model

A summary of the computational aeroelastic analysis for the Semi-Span Super-Sonic Transport (S4T) wind-tunnel model is presented. A broad range of analysis techniques, including linear, nonlinear and Reduced Order Models (ROMs) were employed in support of a series of aeroelastic (AE) and aeroservoelastic (ASE) wind-tunnel tests conducted in the Transonic Dynamics Tunnel (TDT) at NASA Langley Research Center. This research was performed in support of the ASE element in the Supersonics Program, part of NASA's Fundamental Aeronautics Program. The analysis concentrated on open-loop flutter predictions, which were in good agreement with experimental results. This paper is one in a series that comprise a special S4T technical session, which summarizes the S4T project.

Sanetrik, Mark D.↗
NASA NTRS2015

Aeroservoelastic Wind-Tunnel Test of the SUGAR Truss Braced Wing Wind-Tunnel Model

The Subsonic Ultra Green Aircraft Research (SUGAR) Truss-Braced Wing (TBW) aeroservoelastic (ASE) wind-tunnel test was conducted in the NASA Langley Transonic Dynamics Tunnel (TDT) and was completed in April, 2014. The primary goals of the test were to identify the open-loop flutter boundary and then demonstrate flutter suppression. A secondary goal was to demonstrate gust load alleviation (GLA). Open-loop flutter and limit cycle oscillation onset boundaries were identified for a range of Mach numbers and various angles of attack. Two sets of control laws were designed for the model and both sets of control laws were successful in suppressing flutter. Control laws optimized for GLA were not designed; however, the flutter suppression control laws were assessed using the TDT Airstream Oscillation System. This paper describes the experimental apparatus, procedures, and results of the TBW wind-tunnel test. Acquired system ID data used to generate ASE models is also discussed.2 study.

Scott, Robert C.↗
NASA NTRS2004

Preliminary results of the autonomous sciencecraft experiment

The Autonomous Sciencecraft Experiment (ASE) will operate onboard the Earth Orbiter 1 mission 2004. The ASE software uses onboard continuous planning, robust task and goal-based execution, and onboard machine learning and patter recognition to radically increase science return by enabling intelligent downlink selection and autnomous retargeting. In this paper we will discuss how these AI technologies are synergistically integrated in multi-layer control architecture to enable a virtual spacecraft science agent.

Rabideau, Gregg↗
NASA NTRS2004

Preliminary results of the autonomous sciencecraft experiment

The Autonomous Sciencecraft Experiment (ASE) will operate onboard the Earth Orbiter 1 mission in 2003. The ASE software uses onboard continuous planning, robust task and goal-based execution, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. In this paper we discuss how these AI technologies are synergistically integrated in multilayer control architecture to enable a virtual spacecraft science agent.

Rabideau, Gregg↗
NASA NTRS2002

The Techsat-21 autonomous sciencecraft experiment

The Autonomous Sciencecraft Experiment flight demonstration (ASE) will fly onboard the US Air Forces's TechSat-21 constellation, an unclassified mission scheduled for launch in 2004. ASE will use onboard science analysis, replanning, robust execution, and formation flying to radically increase science return by enabling intelligent downlink selection and autonomous retargeting.

autonomous↗
DOE OSTI2024

Interactions involved in the adsorption of ethylene glycol and 2-hydroxyethoxide on the Au(111) surface: a Density Functional Theory study

Abstract Context:The monolayers of ethylene glycol and 2-hydroxyethoxide on gold surfaces have been used in hybrid materials as biosensors. In this article, the adsorption of ethylene glycol and 2-hydroxyethoxide on the Au(111) surface was analyzed. For the first system, ethylene glycol on Au(111), there are Au$$\cdot \cdot \cdot $$ · · · O and Au$$\cdot \cdot \cdot $$ · · · H interactions. To the best of our knowledge, the Au$$\cdot \cdot \cdot $$ · · · H interaction has been overlooked until now. However, in this work, there is strong evidence that this interaction is important to stabilize the system. For the second system, the atomic interactions mentioned previously are also predicted, although there is an additional interaction between 2-hydroxyethoxide molecules. Such an interaction induces the link -O-H-O-, with high values of the electron density at the critical points of the corresponding bond path of the O-H interaction. These links suggest the forming of ethylene glycol chains.Methods:The calculations were performed using two exchange-correlation functionals: BEEF-vdW and C09$$_{x}$$ x -vdW; both functionals incorporate dispersion effects within the Kohn-Sham approach in Density Functional Theory as implemented in GPAW code and ASE computational packages. The contacts between the molecules considered in this article and the Au(111) surface were analyzed through the Quantum Theory of Atoms in Molecules implemented in GPUAM code.

Biochemistry & Molecular Biology↗
DOE OSTI2026

Agentic framework for programmatic crystal structure generation using a fine-tuned worker–supervisor large language model

Platinum group metals (PGMs) underpin many catalytic technologies but face severe supply constraints, motivating the search for alternative materials and computational methods to accelerate discovery. While atomistic simulation tools such as Pymatgen and ASE have streamlined structure manipulation, they require detailed inputs, limiting accessibility for experimentalists and slowing early-stage exploration. Here, in this study, we present an AI-driven agentic framework that orchestrates worker–supervisor large language models (LLMs). The worker translates natural-language prompts of varying abstraction into valid crystallographic structures using a compact LLM fine-tuned with low-rank adaptation on a curated text–code–CIF dataset, emphasizing energy-efficient training. Benchmarking against the baseline CodeGen-350M-mono model shows that fine-tuning reduces hallucination rates from 100% to as low as 5% and improves structural match accuracy to up to 82% for fully specified inputs. Accuracy declines with decreasing prompt detail but remains nontrivial even when only stoichiometry and space group are provided, underscoring the LLM’s capacity for crystallographic inference. The supervisor Claude LLM evaluates the outputs and triggers iterative refinement through the worker’s built-in structure manipulation capabilities (e.g., supercell scaling, strain, vacancy, and substitution operations). We further demonstrate use cases for technologically relevant catalysts, including IrO 2 , pyrochlore Pb 2 Ir 2 O 7 , Ni 2 FeO 4 , and Ni 3 Mo, where the framework generates physically consistent structures that can be refined via geometry optimization. This work introduces a low-energy, language-driven pathway for integrating human and machine intelligence in materials design, paving the way for AI-assisted synthesis planning and high-throughput screening of complex oxides.

AI agent↗
DOE OSTI2025

Temperature-Dependent Structural Transition in Cu-Intercalated Trigonal CuYbSe 2

Rare-earth delafossites, ARCh 2 ; A = alkali metal, R = rare-earth, Ch = chalcogen which consist of intercalated rare-earth metal dichalcogenides, host frustrated triangular lattices that are fertile ground for exotic phenomena. In most cases, the triangular rare-earth sublattice arises from R-3m (No. 166) structures with three layers of rare-earth metal dichalcogenide octahedra or P6 3 /mmc (No. 194) structures with two such layers, analogous to those found in transition metal dichalcogenides. Substituting the alkali metal with Cu + yields a distinct trigonal crystal symmetry P-3m1 (No. 164) in these structures. This symmetry change alters the coordination environment from ASe 6 octahedra in R-3m AYbSe 2 to CuSe 4 tetrahedra in CuYbSe 2 , resulting in shortened rare-earth to rare-earth separations and significantly reduced interlayer distances. Using X-ray single-crystal diffraction, powder neutron diffraction, resistance, and specific heat measurements, a structural transition slightly below room temperature (258 K) is observed. The low-temperature structure is a lower-symmetry I2/m structure, accompanied by partial Cu-site vacancy ordering. The combination of Cu disorder and the triangular lattice geometry in CuYbSe 2 provides a promising platform for investigating frustrated magnetism and unconventional transport phenomena.

Chemical structure↗
DOE OSTI2026

Discovery of tunable and soluble organic emitters for solid-state lasers with a self-driving laboratory

We have recently demonstrated the ability of using self-driving laboratories for AI-driven searches of organic emitters for solid-state lasing devices. Our past workflow featured solubility challenges for such large molecular moieties. In this next-generation study, we return to the drawing board to explore a family of com-pounds that are much solution processable and composed of a set of electronic cores that provide a broader color response. Out of 252 potential candidates,and with guidance from DFT calculations, we selectively perform a compre-hensive study exploring 51 fluorene-based A-B-A type organic laser oligomers, armed with our self-driving lab. The candidates range from simple hydrocarbon molecules to complex hetero atom-mixed molecules. As a result of this study, we highlight diketopyrrolopyrrole and benzodiazole derivatives for their largely red-shifted emissions. Furthermore, we investigate the effect of color change aris-ing from hetero atom permutation, fluorine addition, thiophene coupling, and a combination of fluorine addition and thiophene coupling. Amplified spontaneous emission (ASE) measurements in the solid state further corroborate the lasing potential of selected candidates, reinforcing their suitability for future device applications. The computational study with density functional theory confirms the experimental results.

fluorescence↗
DOE OSTI2026

Strong Effect of Nonpolar Solvent Molecular Structure on CdSe Nanoplatelet Stacking

We report a drastic difference in stacking behavior of oleic acid-stabilized 4-monolayer (4 ML) CdSe nanoplatelets (NPLs) in toluene and methylcyclohexane (MCH), two nonpolar solvents that differ in the conformational flexibility of their molecules. Using liquid cell transmission electron microscopy (TEM) and small angle scattering (SAXS) techniques, we show that NPLs form microns-long ribbons consisting of 4 ML CdSe NPLs in toluene, the solvent widely used to form stable colloidal solutions of a broad range of quasi-spherical nanoparticles. In contrast, 4 ML CdSe NPLs are well dispersed in MCH. The difference in stacking behavior of NPLs in toluene and MCH suggests that the conformational flexibility of the solvent molecules, such as the ability to adopt multiple chair conformations, modulates nanoplatelet interactions. Molecular dynamics (MD) simulations reveal that solvent molecules subtly alter the structure of the organic ligand shell. These solvent-dependent changes propagate to the inorganic core, modulating the degree of CdSe nanoplatelet (NPL) twisting and, consequently, the properties of the nanoparticles. We show that toluene better solvates oleate ligands while MCH induces a bimodal oleate span distribution, which can lead to increased solubility of CdSe NPLs. In addition, the solvent can also influence the inorganic core, which, in turn, can modify the nanoparticle properties. We demonstrate that destabilization of toluene solution containing ribbons of 4 ML CdSe NPLs without CdS shells results in the formation of NPL assemblies with amplified spontaneous emission (ASE) with a low threshold of 14 µJ cm−2 that is comparable with that of CdSe/CdS core/shell NPLs. Our results emphasize that the solvent plays a major role in mediating interactions between NPLs and hence their processability for fabrication of functional structures.

CdSe↗
DOE OSTI2026

pyRMG: A framework for high-throughput, large-cell DFT calculations on supercomputers

Exascale computing delivers the raw power to simulate ever larger and more chemically realistic systems, but realizing this potential requires codes that can efficiently use thousands of processors. Our real-space multigrid (RMG) density functional theory (DFT) code’s grid-decomposition approach scales nearly linearly with the number of graphics processing units (GPUs), even for simulations exceeding thousands of atoms. This scalability makes RMG a compelling tool for high-throughput DFT studies of materials that would otherwise be bottlenecked in other codes (for example, by global fast Fourier transforms in plane-wave DFT). However, the limited workflow infrastructure for RMG has thus far constrained its adoption to a small user community. In this work, we present pyRMG, a Python package designed to streamline the setup and execution of RMG DFT calculations. Built on the pymatgen and ASE (Atomic Simulation Environment) computational materials science Python packages, pyRMG automates input generation and convergence checking, and it integrates with modern job schedulers (e.g., Flux) on leadership-class platforms such as Frontier and Perlmutter. Here, we demonstrate pyRMG for a high-throughput study of strain effects in 2D 2L-Bi 2 Se 3 /2L-NbSe 2 heterostructures, which offers chemical insights into this system and shows that RMG-based workflows can converge with limited user intervention.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

matsim-agents v1.0

matsim-agents is a multi-agent AI framework for atomistic materials simulation and discovery. It orchestrates large language models (LLMs), machine-learned interatomic potentials (MLIPs), and DFT codes into a single agentic loop running on laptops and DOE leadership-class supercomputers. MULTI-AGENT ORCHESTRATION A LangGraph state machine with three nodes: a Planner that converts a natural-language research objective into structured tasks; an Executor that dispatches atomistic tools and loops until the queue is empty; and an Analyst that summarizes results into a human-readable report. State is checkpointed after every step and human-in-the-loop gates can be inserted at any edge. HYPOTHESIS-DRIVEN DISCOVERY CHAT An interactive REPL (matsim-agents chat) that couples LLM dialogue with atomistic simulation. Chemical formulas are automatically detected in conversation turns and trigger a full crystal-phase exploration: structure generation → relaxation → stability scoring → result injection back into the conversation, creating a closed hypothesis-refinement loop. CRYSTAL PHASE ENUMERATION Given a composition, the phase explorer enumerates prototypes by stoichiometry: elemental (fcc/bcc/hcp/sc/diamond), binary 1:1 (rocksalt/CsCl/zincblende/ wurtzite/fluorite/rutile), ternary 1:1:3 (cubic perovskite), ternary 1:2:4 (perovskite + spinel), quaternary 1:1:2:6 (Fm-3m double perovskite). 2-D prototypes (graphene, h-BN, MoS2 2H/1T) and multilayer stacking are also supported via --include-2d and --num-layers. SUPERCELL GENERATION AND SITE DECORATION Auto-tiling to a minimum atom count (--min-atoms), explicit NxNxN tiling (--supercell), symmetry-distinct site decorations (--n-orderings), and isotropic lattice-scale sweeps (--lattice-scales) for volume bracketing. MLFF RELAXATION AND STABILITY SCORING HydraGNN (multi-headed GNN) drives structure relaxation via ASE with FIRE, BFGS, or BFGSLineSearch. Stability output: delta-E/atom ranking across phases and a max-residual-force dynamical-stability proxy. Other MLIPs (MACE, NequIP, Orb) can be plugged in through the same interface. DFT BACKENDS Quantum ESPRESSO pw.x and VASP 6.6 are first-class labellers. Both have validated GPU builds and SLURM/PBS launchers for three DOE platforms: Frontier (AMD MI250X, ROCm), Aurora (Intel PVC, oneAPI), Perlmutter (NVIDIA A100, CUDA). QE produces ~100 binaries (pw.x, ph.x, epw.x, ...). VASP supports scf, relax, vc-relax, and vc-relax-shape run types. ACTIVE-LEARNING LOOP matsim-agents al run CONFIG.yaml drives an iterative HydraGNN-DFT loop: MD generates candidates → ensemble/MC-dropout uncertainty selects the most informative → DFT labels them in parallel inside one allocation → dataset grows → HydraGNN retrains → repeat. DFT backend is a single YAML toggle (dft.backend: vasp | qe). LLM-generated seed structures are supported (no curated POSCAR library needed). Config uses ${VAR}, ${VAR:-default}, ${VAR:?msg} shell-style substitution for cross-user/cross-site portability. LLM BACKENDS Ollama (local, default), vLLM (HPC multi-GPU serving), OpenAI, Anthropic, HuggingFace Transformers+Accelerate. Selected at runtime via flag or env var with no code changes. HPC PORTABILITY Same Python entry points run on Frontier (ROCm 7.2), Aurora (oneAPI), and Perlmutter (CUDA 12). DFT and ML stacks are never co-loaded in the same shell; they couple through the scheduler and filesystem. Advanced multi-node launchers (serve, discovery-chat, single-relaxation, active-learning, QE warm-start) are provided for all three platforms. CODABENCH COMPETITION BUNDLE A self-contained benchmark: 159 atomistic test structures across 11 material classes, 5 tasks (formation energy, forces, ML relaxation, AI-DFT relaxation, phase stability ranking), public/private leaderboard split (30/70), and four ready-to-run baselines: MACE-MP-0, HydraGNN, UMA, AllScAIP.

Lupo Pasini, Massimiliano [Oak Ridge National Labo↗
DOE OSTI2026

PRISMA: PARALLEL REFINEMENT AND INTEGRATION SYSTEM FOR MULTI-AZIMUTHAL ANALYSIS

The Parallel Refinement and Integration System for Multi-azimuthal Analysis (PRISMA, version 1.1.0) is a Python application for processing X-ray diffraction (XRD) image data. PRISMA wraps GSAS-II to perform azimuthally-binned peak refinement, computes per-frame strain and d-spacing from those fits, and provides three PyQt5 graphical interfaces: (1) a Recipe Builder for selecting GSAS-II control (.imctrl) files, optional mask (.immask) files or threshold-ased masking, reference and experiment image sets, peaks, zimuthal range and bin size, and an optional ceria-based auto-calibration; (2) a Batch Processor that uses Dask on local workstations and pure MPI (mpi4py.futures.MPICommExecutor) on HPC to distribute GSAS-II refinement across cores or compute nodes and write results to a 4-dimensional (peaks x frames x azimuths x measurements) Zarr dataset; and (3) a Data Analyzer that renders heatmaps of fit parameters, strain, frame-to-frame deltas, and percent-change-vs-reference, and exports user-defined subsections to CSV or Excel. The peak-refinement algorithm is deterministic. Benchmark on ALCF Crux: a 20,000-image set, single-peak fit in frame mode with 44 azimuthal bins on 128 nodes x 128 workers, 48 seconds total wall time.

Lorenzo Martin, Maria De La Cinta [Argonne Nationa↗