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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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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Dial

A key step in almost all scientific endeavors is answering the question: Given this data I already collected, what new data do I expect will yield the most useful information toward my scientific objective? The area of (sequential) experimental design has long been investigating answers to this question, but in recent years techniques from the machine learning subfield of active learning are increasingly applied. Researchers need a simple software tool for active learning applied to experimental design that can easily integrate into their existing workflows. This computer code, Dial, provides a microservice in ORNL's INTERSECT ecosystem for active learning applied to experimental design. By being part of the INTERSECT ecosystem, Dial is simple to integrate into any INTERSECT-based workflow. Dial provides multiple backend options, where a backend is an implementation of a specific active learning method. Users can select the backend that performs best for their application. Developers can also add new backends as needed. At its core, Dial receives a set of pre-existing measurements and input parameter bounds and then recommends one or more new sets of parameters to measure. Dial also includes interfaces to other microservices in the INTERSECT ecosystem so that it can be incorporated into INTERSECT campaigns. Dial provides a simple, yet powerful interface to convert automated INTERSECT workflows into autonomous workflows that adapt based on the results that are obtained. A shared microservice for active learning prevents duplicated effort by each application team implementing its own adaptive design of experiments tool.

Drane, Lance [Oak Ridge National Laboratory (ORNL)↗
DOE OSTI2024

The Rise of Intelligent Materials Science: Unleashing the Power of Machine Intelligence in Characterization

Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.

autonomous↗
DOE OSTI2025

Prototype acoustic positioning system for the Pacific Ocean Neutrino Experiment

We present the design and initial performance characterization of the prototype acoustic positioning system intended for the Pacific Ocean Neutrino Experiment. It comprises novel piezo-acoustic receivers with dedicated filtering- and amplification electronics installed in P-ONE instruments and is complemented by a commercial system comprised of cabled and autonomous acoustic pingers for sub-sea installation manufactured by Sonardyne Ltd. We performed an in-depth characterization of the acoustic receiver electronics and their acoustic sensitivity when integrated into P-ONE pressure housings. These show absolute sensitivities of up to -125 dB re V2/μPa2 in a frequency range of 10–40 kHz. We furthermore conducted a positioning measurement campaign in the ocean by deploying three autonomous acoustic pingers on the seafloor, as well as a cabled acoustic interrogator and a P-ONE prototype module deployed from a ship. Using a simple peak-finding detection algorithm, we observe high accuracy in the tracking of relative ranging times at approximately 230–280 μs at distances of up to 1600 m, which is sufficient for positioning detectors in a cubic-kilometer detector and which can be further improved with more involved detection algorithms. The tracking accuracy is further confirmed by independent ranging of the Sonardyne system and closely follows the ship's drift in the wind measured by GPS. The absolute positioning shows the same tracking accuracy with its absolute precision only limited by the large uncertainties of the deployed pinger positions on the seafloor.

Data analysis↗
DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science [Slides]

Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Towards a self-driving trigger at the LHC: adaptive response in real time

Real-time data filtering and selection—or trigger—systems at high-throughput scientific facilities such as the experiments at the Large Hadron Collider must process extremely high-rate data streams under stringent bandwidth, latency, and storage constraints. Yet these systems are typically designed as static, hand-tuned menus of selection criteria grounded in prior knowledge and simulation. In this work, we further explore the concept of a self-driving trigger, an autonomous data-filtering framework that reallocates resources and adjusts thresholds dynamically in real-time to optimize signal efficiency, rate stability, and computational cost as instrumentation and environmental conditions evolve. We introduce a benchmark ecosystem to emulate realistic collider scenarios and demonstrate real-time optimization of a menu including canonical energy sum triggers as well as modern anomaly-detection algorithms that target non-standard event topologies using machine learning. Using simulated data streams and publicly available collision data from the Compact Muon Solenoid experiment, we demonstrate the capability to dynamically and automatically optimize trigger performance under specific cost objectives without manual retuning. Our adaptive strategy shifts trigger design from static menus with heuristic tuning to intelligent, automated, data-driven control, unlocking greater flexibility and discovery potential in future high-energy physics analyses.

Emami, Shaghayegh [Michigan U.] (ORCID:00090007589↗
DOE OSTI2025

Development and flight-testing of modular autonomous cultivation systems for biological plastics upcycling aboard the ISS

Cultivation of microorganisms in space has enormous potential to enable in-situ resource utilization (ISRU) Here, we develop an autonomous payload with fully programmable serial passaging and sample preservation, termed the Modular Open Biological Platform (MOBP), and flight-test the MOBP aboard the International Space Station (ISS) by conducting enzymatic and microbial plastics upcycling experiments. The MOBP is a compact, modular bioreactor system that allows for sustained microbial growth via automated media transfers, such as those for sample collection and storage for terrestrial analyses, and precise data monitoring from integrated sensors. The MOBP was flight-tested with two experiments designed to evaluate biological upcycling of the plastic poly(ethylene terephthalate) (PET). The bioproduct βKA can be polymerized into a nylon-6,6 analog with improved properties for use in the production of a variety of materials. We posit the MOBP will aid in democratizing the execution of synthetic biology in spaceflight towards enabling ISRU.

09 BIOMASS FUELS↗
DOE OSTI2026

AutoLabs: cognitive multi-agent systems with self-correction for autonomous chemical experimentation

The automation of chemical research through self-driving laboratories (SDLs) promises to accelerate scientific discovery, yet the reliability and granular performance of the underlying AI agents remain critical, under-examined challenges. In this work, we introduce AutoLabs, a self-correcting, multi-agent architecture designed to autonomously translate natural-language instructions into executable protocols for a high-throughput liquid handler. The system engages users in dialogue, decomposes experimental goals into discrete tasks for specialized agents, performs tool-assisted stoichiometric calculations, and iteratively self-corrects its output before generating a hardware-ready file. We present a comprehensive evaluation framework featuring five benchmark experiments of increasing complexity, from simple sample preparation to multi-plate timed syntheses. Through a systematic ablation study of 20 agent configurations, we assess the impact of reasoning capacity, architectural design (single- vs. multi-agent), tool use, and self-correction mechanisms. Our results demonstrate that agent reasoning capacity is the most critical factor for success, reducing quantitative errors in chemical amounts (nRMSE) by over 85% in complex tasks. When combined with a multi-agent architecture and iterative self-correction, AutoLabs approaches expert-authored reference procedures on the benchmark (F1-score > 0.89) on challenging multi-plate syntheses. These findings establish a clear blueprint for developing robust and trustworthy AI partners for autonomous laboratories, highlighting the synergistic effects of modular design, advanced reasoning, and self-correction to ensure both performance and reliability in high-stakes scientific applications. Code: https://github.com/pnnl/autolabs

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A Behavior Tree Approach for Battery-Aware Inspection of Large Structures Using Drones

Electric multi-rotor drones have been used to inspect several structures, including large buildings and dams. In these inspections, energy consumption is a concern. To prevent the drone from running out of battery, commercial drones usually come back to their home position when the battery level reaches a minimum threshold. The pilots then need to replace the battery and use their own experience to restart the inspection mission approximately from where it ended before the drone returned home. Instead of relying on the human operator, in this paper, we automate this process using behavior trees, which is an effective way to perform autonomous mission control and supervision. By integrating battery management strategies into a behavior tree framework, this paper demonstrates the drone’s adaptive and resilient decision-making when confronted with limited power constraints. We implemented our methodology using a commercial drone and tested the proposed ideas in a photogrammetry-based inspection task.

42 ENGINEERING↗
DOE OSTI2024

Autonomous Grid Support Functionality in Variable-Speed Drive-Based End-Use Loads

High penetration of inverter-based resources into power systems causes lower inertia, and it becomes more challenging to maintain grid stability during contingency events. While conventional demand response and shedding at the load side suffer from slow response, impact by unavailability, and issues from communication, variable-speed drives (VSDs) are becoming strong candidates for fast frequency response (FFR) thanks to their fast response and reduced user impact from variable power adjustment capability. In this letter, we propose an autonomous frequency support control method for a residential refrigerator with a VSD, a promising target small end-use load for FFR. We analyze the effect of the proposed controller on grid stability, and we verify the performance through laboratory experiments with a commercially available refrigerator.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Beyond Human Vision: Exploring Materials with Machine Intelligence

Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.

artificial intelligence↗
DOE OSTI2026

Reimagining metal-organic framework discovery: Integrating experiment, computation, and artificial intelligence

The traditional development of novel metal–organic frameworks (MOFs) is often hindered by challenges such as synthetic accessibility and time- and resource-intensive experimentation. High-throughput, automated experimental and computational techniques have enabled rapid chemical space exploration and theoretical MOF design. When combined with artificial intelligence (AI), these methods can be used to lead autonomous laboratories to new frontiers for MOF discovery, where these materials can be designed for a specific application, efficiently synthesized, characterized, and evaluated. Here, this perspective highlights the role of AI in advancing automated MOF synthesis and characterization, computational MOF design and screening, and the integration of these approaches within autonomous workflows to ultimately enable the MOF laboratories of the future.

Gaidimas, Madeleine A. [Northwestern University, E↗
DOE OSTI2025

Modular Autonomous Experimentation for Biological Applications (Full Report)

The Modular Autonomous Research System (MARS) was developed to address the pressing need for faster, more reliable, and more adaptable scientific discovery. Traditional experimentation is limited by manual labor, long cycle times, and fragmented data streams, which constrain the ability to explore complex chemical and materials design spaces. To overcome these limitations, we created an integrated, modular platform that combines laboratory robotics, diverse measurement instruments, and a central data infrastructure with artificial intelligence–driven decision-making. The system links liquid handling robots, robotic arms, and optical plate readers into a closed loop where experiments are executed automatically, data is analyzed in real time, and subsequent experimental conditions are adaptively chosen to maximize information gain. Over the course of the project, MARS was validated on two primary test cases—spectroscopic metal–ligand binding assays and peptide-directed mineralization—which highlighted the system’s ability to handle uncertainty and variability in experimental measurements. To further demonstrate modularity and extensibility, we also established additional testbeds in electrochemistry for catalyst discovery and electrolyte formulation for advanced batteries. The results show that MARS can reliably conduct autonomous campaigns with minimal human intervention, adapt to distinct scientific domains, and provide a scalable model for future self-driving laboratories. This work establishes new capabilities for modular, uncertainty-aware automation and directly supports the need for advanced, data-driven research platforms capable of accelerating discovery across a wide range of scientific and national security missions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Leveraging large language models to address data scarcity in machine learning for graphene synthesis

Machine learning in experimental materials science faces significant challenges due to the scarcity of data, which are costly and time-consuming to generate, particularly when relying on in-house experiments. Literature data mining offers a potential solution but introduces issues like mixed data quality, inconsistent formats, and non-uniform reporting of synthesis parameters, resulting in partially missing and heterogeneous features across the dataset. Here, we propose data imputation and feature engineering methods that employ pre-trained large language models (LLMs) to enhance machine learning performance on scarce, heterogeneous datasets, demonstrated on graphene CVD synthesis data and the ML-HydPARK hydrogen storage dataset. GPT models perform data imputation via tailored prompting and semantic normalization of inconsistently reported features through embeddings, for example, to harmonize the complex nomenclature of CVD substrates. Beyond yielding more diverse and richer feature representations than traditional methods such as K-nearest neighbors (KNN) and Multivariate Imputation by Chained Equations (MICE), LLM-based data imputation is evaluated against dataset characteristics and prompting strategies. We vary the level of autonomy granted to the LLM, from generic prompting that leverages pre-trained knowledge for autonomous data generation to data-informed prompting that constrains outputs using target-specific information, and demonstrate which level of autonomy yields superior imputation performance across datasets and feature types. The proposed data engineering methods markedly improve downstream performance; for example, in graphene layer number classification using a support vector machine (SVM), binary accuracy increases from 39% to 65% and ternary accuracy from 52% to 72%. Fine-tuning experiments on both datasets show that combining our proposed LLM-based data imputation and feature encoding methods with numerical machine learning predictors outperforms standalone fine-tuned LLM predictors in data-scarce settings. The proposed strategies emphasize data enhancement techniques rather than refining learning architectures or regularizing loss functions, offering a broadly applicable framework for improving machine learning performance on scarce, inhomogeneous datasets.

Chemical vapor deposition↗
DOE OSTI2024

The Evolution of Airport Terminal Wayfinding Technologies

This work was presented during the Transportation Research Board (TRB) and Airport Cooperative Research Program (ACRP) webinar "Enhancing the Airport Experience with Wayfinding" on May 23, 2024. The process of navigating within airports has evolved over the years, with various technologies emerging to complement static signage. Historically, wayfinding began with static signage and airport staff members providing personal guidance, before progressing into digital signs, interactive kiosks, and displays. In recent years, wayfinding has continued along its digital path using mobile applications, indoor positioning technology, and even robot guides. With ever- larger airport facilities and a growing number of travelers, the future of wayfinding will likely be heavily linked to further digital developments such as facial recognition, augmented-reality technology, and autonomous vehicle navigation. This presentation delves into the history of interior wayfinding in airports, its present state, and the anticipated future. Key wayfinding technologies are discussed with an emphasis on emerging smartphone applications. Other considerations such as legal issues, language barriers, and human-technology interactions are included. A wayfinding framework is proposed, with static wayfinding technologies serving as the base upon which dynamic and personalized digital technologies are built. In this framework, electronic wayfinding technologies do not replace - but rather augment - traditional methods. These technologies can be integrated into existing wayfinding systems for a seamless traveler experience.

airport↗
DOE OSTI2026

Demonstration and analysis of volumetric additive manufacturing via sub-orbital spaceflight testing

Computed Axial Lithography (CAL) represents a significant advancement in the emerging field of Volumetric Additive Manufacturing (VAM). CAL addresses key limitations of traditional photopolymer additive manufacturing technologies, by eliminating the need for layering and support structures. Unlike conventional methods, CAL prints components by illuminating all points within a desired geometry simultaneously, using tomographic reconstruction to form the object in a single step. This unique approach eliminates the relative motion between the object and the precursor material, enabling faster printing speeds and reducing the waste associated with support structures. However, CAL parts require post-processing steps before they can be utilized. CAL's core attributes make it particularly suited for In-Space Manufacturing (ISM), due to its fast fabrication times, wide breadth of materials it can use, and minimized footprint. CAL has been successfully demonstrated in microgravity during parabolic flight experiments. However to fully validate and understand CAL's behaviour in microgravity, all manufacturing and post-processing steps must be integrated. In June 2024, we conducted SpaceCAL Mission 3, testing this entire workflow on a suborbital flight aboard Virgin Galactic's SpaceShipTwo. During ~140 s of microgravity, the system autonomously manufactured and post-processed four parts using PEGDA700 resin. Post-flight analysis showed that 2/4 parts were recognisable, while others were distorted due to bubble formation from residual water droplets, off-axis optical aberrations, and non-uniform solvent rinsing. Despite these limitations, this study represents the first integrated CAL workflow in space, providing an initial experimental demonstration and analysis for closed-loop in-space manufacturing.

Waddell, Taylor [Department of Mechanical Engineer↗
DOE OSTI2024

High Performance Computing Peak Shaving for Microreactor Operation

There are multiple nuclear microreactors currently under development that are designed to provide autonomous power for as many as ten or more years without refueling and are designed to power high performance computing (HPC) datacenters. But the load-follow speeds for a nuclear microreactor will be much slower than grid power and slower than the power variance typical of a HPC system. HPC datacenters experience peak power load variance driven by several factors ranging from the operation of cooling systems to remove heat from the servers to supporting a wide range of user application workflows and architectures each with different power signatures. One mechanism to support the limited load-follow of a microreactor is peak shaving where an energy storage mechanism is used to shed peak load and reduce significant power variance. This work explores peak electrical load shaving using uninterruptible power supply (UPS) systems designed for HPC support in the context of peak shaving when operating using a nuclear microreactor with a load-follow limited to 10% of load per minute. Using a self contained HPC datacenter complete with stand-alone cooling system and provisioned with an x86 cluster, an ARM cluster, and a graphics processing unit (GPU) cluster, peak shaving for microreactor operation using the UPS battery backup is explored while running two classes of typical HPC user applications. HPC architecture suitability for microreactor operation under this type of peak shaving is examined.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Design and Validation of Mechanical & Autonomous Systems in Harsh Environments [Poster]

The autonomous systems within the Mobile Hot Cell provide advanced capabilities over traditional hot cells, pushing the boundaries of these systems in harsh environments. The degraded state of some radiological devices and the variety of designs present a series of challenges that require innovative tooling and procedural solutions. By leveraging technical knowledge and experience from disposition experts at SwRi and other institutions, initial design concepts were produced using rapid prototyping techniques. These concepts were then validated and optimized in a non-hazardous test bed, resulting in iterative improvements at minimal cost.

07 - ISOTOPES AND RADIATION SOURCES↗
DOE OSTI2025

Exploring Large Language Model Agents in Cybersecurity: A Literature Review with Experiments

The accelerated development and integration of large language model (LLM) agents have led researchers and developers to explore their effectiveness in cybersecurity, specifically with penetration testing (pentesting). Recent research efforts have attempted to use LLM agents to automate the process of pentesting because of the cost and time requirements that are required to perform a manual review. However, not all of the tools perform as expected. This paper reviews some of the newest and most popular autonomous pentesting frameworks, highlighting the capabilities and limitations of each one with the goal of providing the components needed to successfully and effectively build an autonomous pentesting agent in the future.

97 MATHEMATICS AND COMPUTING↗