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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
DOE OSTI2025

Multi-agent AI collaboration for digital twin development and assessment

Developing a digital twin (DT) model involves different steps that encompass formulating requirements, model development, implementation, and assessment with respect to real applications. Human expertise is required to coordinate and implement different steps in the DT development and assessment process. However, certain parts of this process can be automated using artificial intelligence (AI) agents for efficient workflow development. In this work, we test and analyze a multiagent AI collaboration with humans in the loop to automate different elements of the DT development and assessment process. To implement the workflow for multiagent AI DT development and assessment, we use Autogen, a multiagent framework developed by Microsoft. Autogen offers a modular and flexible framework for configuring and designing task-specific multiagent workflows. In this framework, large language models (LLMs) form the core intelligence of the AI agents where the quality and performance of the automated element is governed by the inherent capabilities and knowledge base of the LLM. We use retrieval augmented generation to supplement the LLM with relevant domain-specific information for DT requirement formulation. We illustrate this multiagent workflow using a case study on a thermal energy storage system, focusing on how AI agents can collaborate with humans to expedite and optimize different elements of DT development and assessment process.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling as Catalysts for Next-Generation Breakthroughs

The Presidential Symposium (PRES) at the 2025 Fall Meeting, hosted by the President’s Office and Energy and Fuels Division, American Chemical Society (ACS) in Washington, DC, brought together a diverse group of chemists, engineers, and materials scientists working in battery materials & systems, automation and artificial intelligence from academia, industry, and national laboratories. The accelerating demand for high-performance, scalable, and sustainable energy storage has catalyzed a paradigm shift in how materials are dis-covered, devices are engineered, and systems are optimized. This Presidential Symposium, entitled “Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling Driving Next-Gen Breakthroughs”, brings together global leaders to unveil transformative strategies anchored in the AAA framework: Artificial Intelligence, Automation, and Advanced Modeling. Artificial Intelligence is redefining the frontiers of energy storage by enabling predictive design, real-time optimization, and intelligent control across diverse chemistries and architectures. Automation is streamlining the synthesis, characterization, and testing of battery materials, dramatically accelerating innovation cycles and unlocking scalable solutions for grid and mobility applications. Advanced Modeling, spanning atomic to system-level scales, provides unprecedented insight into electrochemical dynamics, degradation pathways, and thermal behavior, particularly when coupled with physics-informed machine learning and digital twin technologies. Digital twins, in turn, leverage the AAA framework by integrating real-time data, physics-based models, and AI predictions into dynamic virtual replicas, enabling proactive diagnostics, optimization, and system resilience. Together, these synergistic pillars are not only re-shaping the scientific landscape but also forging a new era of reproducible, data-driven, and resilient energy storage innovation. In conclusion, this symposium marks a pivotal moment in the convergence of computational intelligence and experimental rigor, charting the course for next-generation breakthroughs in lithium-ion, solid-state, and flow battery technologies.

Artificial Intelligence (AI)↗
DOE OSTI2025

Combining genome-wide association studies and expression quantitative trait nucleotide mapping with molecular and genetic validations to identify transcriptional networks regulating drought tolerance in Populus

Objectives: (i). To deploy a large-scale experimental drought trial for up to 1000 unique genotypes of Populus equipping the sites with controlled irrigation and drought treatments that are fully automated and monitored. FULLY COMPLETED (ii) To test the hypothesis that a suite of traits identified for drought tolerance in P. nigra can be measured in drought and control treatments in the wide germplasm collection of P. trichocarpa. FULLY COMPLETED (iii) To use established and novel GWAS model approaches to identify gene loci linked to drought tolerance traits on interest in P. trichocarpa. FULLY COMPLETED (iv) To undertake comparative analysis of GWAS results for drought tolerance traits in P. nigra and P. trichocarpa. PARTIALLY COMPLETED – remains active (v) Using RNAseq in P. trichocarpa, in droughted and control treatments to identify cis- and trans-regulated eQTN. FULLY COMPLETED (vi) Validate up to 50 cis-QTNs, from network hubs using transient protoplast assays. FULLY COMPLETED (vii) To establish Agrobacterium-based gene editing protocols in Populus. FULLY COMPLETED (viii) To utilize early leads from previous research to investigate at least 6 candidate genes for drought tolerance in Populus. FULLY COMPLETED (ix) To validate up to 20 candidate genes for drought tolerance in P. trichocarpa refined from the long-list tested in the transient assays for cis-acting hub gene targets. PARTIALLY COMPLETED- remains active.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

ASME Code change proposal to implement new universal high temperature constitutive models for Section III, Division 5

This report completes work on a universal high temperature constitutive model suitable for use with the ASME Boiler & Pressure Vessel Code Section III, Division 5 rules for the design by inelastic analysis of Class A nuclear reactor components. The goals of this work are to provide a simple model form that adequately captures the high temperature response of materials and can be applied to any future Code material. Additionally, the report describes an automated process for calibrating a model against test data. The idea is to simplify the effort required to generate a constitutive model for an arbitrary material, provided test data is available. This will accelerate the process of qualifying new Code materials in the future. In addition, the report provides calibrated models and detailed validation comparisons to test data for five currently-qualified or soon-to-be qualified materials: 316H, Grade 91, Alloy 800H, Alloy 617, and Alloy 709. The report surveys the available data for the remaining two ASME Class Materials --- 2.25Cr-1Mo and 304H --- concluding that there is enough data data to generate a model for 2.25Cr-1Mo steel provided some additional sources of non-public data can be included in the test database, but that a dedicated cyclic test campaign would be needed for 304H. Supplemental material includes the full text of an ASME Code change proposal to incorporate the models for the four currently-qualified Class A material, detailed validation comparisons to test data for the five material models, and input files for reference implementations of the constitutive models in the NEML and NEML2 modeling frameworks.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Adaptive Dynamic Digital Twin for Test Scenario Generation

Vehicle testing has been an important part in the development of both highly automated vehicles (HAV) and advanced driving assistant systems (ADAS). Obtaining a good representation of the Vehicle Under Test (VUT) is crucial for test scenario library generation (TSLG). Current vehicle testing methods often involve calibrating car-following models using vehicle trajectory data to create static representations that cannot be dynamically updated. For instance, when multiple vehicle trajectories are collected, it is difficult to automatically determine whether a new trajectory improves the model's representativeness or degrades its accuracy. In this paper, we introduce a dynamically updated digital twin modeling framework featuring an adaptive mechanism that evaluates new trajectory data. This mechanism can decide whether to incorporate newly collected data into the current model or create a separate digital twin model when the trajectory significantly differs from prior data. Vehicle location, speed, and acceleration extracted from the newly collected trajectory data are used to support the dynamic update decision. By integrating this digital twin model into the test library generation process, we demonstrate its ability to assist in generating test libraries while effectively handling newly collected data.

Chen, Hanlin [ORNL] (ORCID:0000000165087715)↗
DOE OSTI2025

RCBC Automatic Monitoring and Control Recommendations

The recompression closed Brayton cycle (RCBC) test rig at the Sandia Brayton Laboratory provides a development platform to accelerate the commercialization of key technologies for supercritical CO 2 (sCO 2 ) closed loop Brayton cycles. The test rig enables testing to gain experience and confidence with new technologies, equipment, and processes, and automating monitors and controls will enhance Sandia’s ability to perform the types and amounts of testing needed. This report identifies candidates for automatic monitoring and control to ensure the loop remains within design limits and minimize risk to equipment due to off-normal events or conditions.

42 ENGINEERING↗
DOE OSTI2025

Bayesian Optimization for Reactor Design Optimization

This study present a test case in which the Bayesian Optimization method is applied to a simulation-based reactor core design optimization problem. The test case aims to showcase the potential of an automated design optimization algorithm for reactor designs by streamlining the reactor core design workflow, given the high computational cost of simulations. The contributions of this work are threefold. First, the existing HTGR model is converted into a simulation-based design optimization test case by developing a pipeline that enables modification of key design parameters and evaluates design performance based on simulation outputs. Second, Bayesian Optimization is implemented and adapted to demonstrate the feasibility of automatic design optimization for nuclear reactor core. Proposed approach leverages Gaussian Process models to characterize the relationship between design variables and performance metrics, while incorporating novel acquisition functions that balance exploration of the design space with exploitation of promising configurations. This implementation lays the foundation for the future developments of reactor design optimization algorithms.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Yeast Transformation on Hamilton Vantage (YT Vantage) v1

Our software program is designed for the Hamilton Vantage liquid handling robot, automating the Build step in the Design-Build-Test-Learn (DBTL) cycle for Saccharomyces cerevisiae. This program minimizes human intervention, enabling rapid identification of pathway bottlenecks and genes that enhance verazine production. The program takes competent yeast and plasmid DNA as input and generates an output library of engineered strains compatible with automated colony picking, high-throughput culturing, and chemical extraction for downstream LC-MS analysis. A user-friendly interface, developed using the Hamilton Method Editor software, allows for on-demand parameter customization. By automating this process, our program streamlines the construction of Saccharomyces cerevisiae, reducing manual labor and increasing efficiency. While the manual process is well-documented, integration with robotic automation is less common, making our program a valuable tool for researchers. With this software, we achieved 2-5 fold increases in verazine production, demonstrating its potential to accelerate research in this field.

Louie, Randy [Lawrence Berkeley National Laborator↗
DOE OSTI2025

Machine Learning-Based Technique for Automated Sensor Characterization

The development of novel instrumentation requires an iterative cycle with three stages: design, prototyping, and testing. Recent advancements in simulation and nanofabrication techniques have significantly accelerated the design and prototyping phases. Nonetheless, detector characterization continues to be a major bottleneck in device development. During the testing phase, a significant time investment is required to characterize the device in different operating conditions and find optimal operating parameters. The total effort spent on characterization and parameter optimization can occupy a year or more of an expert s time. In this work, we present a novel technique for automated sensor calibration that aims to accelerate the testing stage of the development cycle. This technique leverages closed-loop Bayesian optimization (BO), using real-time measurements to guide parameter selection and identify optimal operating states. We demonstrate the method with a novel low-noise CCD, showing that the machine learning-driven tool can efficiently characterize and optimize operation of the sensor in a couple of days without supervision of a device expert.

Zepeda, Cuevas [Chicago U., KICP]↗
DOE OSTI2025

A flexible test facility for liquid xenon detector development

As liquid xenon time projection chambers scale to ever-larger sizes, so too do the engineering challenges they pose. Here, we describe a large, flexible, multipurpose test facility capable of supporting the development of a number of key aspects of liquid xenon detector systems. Example applications of this facility include characterization of large-area light and charge sensor arrays, tests of xenon purification techniques and materials compatibility, and investigations into high-voltage phenomena. This facility uses an automated and remotely monitored cryo-cooling system based on immersion of the test chamber in a liquid bath rather than conductive coupling, leading to advantages in temperature and pressure stability, as well as increasing required response times in the case of cooling-power loss. Design advantages, operational procedures, and performance of the facility are described, as well as five examples of liquid xenon test chambers that use the facility.

Dark Matter detectors↗
DOE OSTI2026

The Art of Automation: Translating Electron Microscopy Workflows Into Automated Processes

Acquiring data using a scanning transmission electron microscope (STEM) is a complex, multi-step process. The intricacy of the process depends on the type of sample, composition of the material, desired results of the experiment, resolution requirement and other experimental factors. Each experiment presents unique complications, such as sample drift and contamination, that the microscopist must consider when acquiring data. All these challenges are handled fluidly and expertly by experienced microscopists, but to reach new levels of innovation in material development, including greater reproducibility, throughput, and precision, the automation of these workflows is essential. The initial phase of this work involved translating intuition-based workflows into discrete, programmable steps. Some common key stages in STEM workflows are the initial tuning, scanning the sample for areas of interest, and then acquiring the data. Each stage can be broken further into specific parameter adjustments, such as aberration correction and dwell time optimization, depending on the experiment. When deconstructing various experiments each step was assessed for automation feasibility based on the amount of real time operator decisions. There are steps that lend themselves to automation more readily than others, such as course focusing and sample screening, but there is potential for full automation of all stages with time. As an initial step, an automated montage routine was developed, allowing for the efficient acquisition of large portions of the sample without requiring continuous intervention from the operator. The automation of this small process of the procedure demonstrates the value of this capability. A major challenge in automation arises from discrepancies between commanded, reported and actual stage movements. Using systematic tests, stage movement was quantified. This error can be corrected algorithmically for more accurate workflows in the future. Expanding automation capabilities would result in larger, more efficient data acquisition which allows for more robust statistical analysis. Additionally, this work lays the groundwork for a closed loop system where machine learning algorithms would intake automatically acquired data and make real time decisions. By progressively automating this instrument, this work establishes the foundation for fully automated experimentation in transmission electron microscopy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Quick-and-Easy Validation of Protein–Ligand Binding Models Using Fragment-Based Semiempirical Quantum Chemistry

Electronic structure calculations in enzymes converge very slowly with respect to the size of the model region that is described using quantum mechanics (QM), requiring hundreds of atoms to obtain converged results and exhibiting substantial sensitivity (at least in smaller models) to which amino acids are included in the QM region. As such, there is considerable interest in developing automated procedures to construct a QM model region based on well-defined criteria. However, testing such procedures is burdensome due to the cost of large-scale electronic structure calculations. Here, we show that semiempirical methods can be used as alternatives to density functional theory (DFT) to assess convergence in sequences of models generated by various automated protocols. The cost of these convergence tests is reduced even further by means of a many-body expansion. We use this approach to examine convergence (with respect to model size) of protein–ligand binding energies. Fragment-based semiempirical calculations afford well-converged interaction energies in a tiny fraction of the cost required for DFT calculations. Two-body interactions between the ligand and single-residue amino acid fragments afford a low-cost way to construct a “QM-informed” enzyme model of reduced size, furnishing an automatable active-site model-building procedure. This provides a streamlined, user-friendly approach for constructing ligand binding-site models that needs neither a priori information nor manual adjustments. Extension to model-building for thermochemical calculations should be straightforward.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Biofoundries: Principles, Tools, and Applications

This chapter aims to provide a broad overview of biofoundries and introduces the principles, concepts, and case studies. We first outline the underlying principles of the Design-Build-Test-Learn (DBTL) framework and the role of automation, digital integration, and standardization. The chapter then explores core biofoundry technologies including robotic liquid handlers, high-throughput analytical instruments, and digital infrastructure for data management and workflow scheduling. Case studies spanning DNA assembly, protein engineering, metabolic engineering, and mammalian cell culture demonstrate the practical applications of the biofoundries. Economic and societal impacts are assessed alongside current limitations. We discuss the emerging trends including artificial intelligence integration and cloud-based distributed facilities to highlight its potential for biotechnology and the bioeconomy.

Singh, Nilmani↗
DOE OSTI2025

Autonomous Flow Electrochemistry for Accelerated Catalyst Discovery

Our objective is to develop an Autonomous Chemical Experimentation (ACE) platform that accelerates discovery of new catalytic transformations and other energy-relevant chemical reactions and processes. We intentionally designed ACE to be highly modular, both with respect to its rapid deployment to different chemistries and experimental workflows as well as incorporation of a wide range of different AI algorithms. In addition to the development of the core software architecture, initial efforts were made to incorporate Large Language Models to provide human-interpretable reasoning of the optimizer’s actions, and to develop a user-friendly graphical interface for experimental researchers. ACE was demonstrated using a flow electrocatalysis platform containing an inline FTIR spectrometer for real-time analysis and quantification of the reaction outcome. Human-in-the-loop experiments were performed in which a human researcher conducted an experiment using electrode potentials suggested by ACE, then fed the spectral data back to ACE for decision making. After confirming the successful function of the optimizer, efforts were next directed to automation of the hardware and performed full autonomy tests using three reactions: catalytic oxidation of formate, catalytic oxidation of cyclohexanol, and oxidation of hydroquinone. These studies confirm that ACE can close the loop between reaction execution, analysis, and optimization. They also reveal that more improved product detection methods will be essential for ACE to make well-informed decisions for reactions with low conversions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Automating Sensor Characterization with Bayesian Optimization

The development of novel instrumentation requires an iterative cycle with three stages: design, prototyping, and testing. Recent advancements in simulation and nanofabrication techniques have significantly accelerated the design and prototyping phases. Nonetheless, detector characterization continues to be a major bottleneck in device development. During the testing phase, a significant time investment is required to characterize the device in different operating conditions and find optimal operating parameters. The total effort spent on characterization and parameter optimization can occupy a year or more of an expert's time. In this work, we present a novel technique for automated sensor calibration that aims to accelerate the testing stage of the development cycle. This technique leverages closed-loop Bayesian optimization (BO), using real-time measurements to guide parameter selection and identify optimal operating states. We demonstrate the method with a novel low-noise CCD, showing that the machine learning-driven tool can efficiently characterize and optimize operation of the sensor in a couple of days without supervision of a device expert.

Cuevas-Zepeda, Julian [Chicago U., KICP; Chicago U↗
DOE OSTI2025

Let’s Get Real: Are Wearable Plant Sensors Ready for Crop Monitoring?

In recent years, the number of publications describing new and exciting developments in wearable plant sensors (WPSs) has skyrocketed. These small, lightweight sensors hold promise to assist precision agriculture and may thus help reduce crop losses, increase resource use efficiency, and automate crop production. However, WPSs are often not adequately tested in environments relevant for crop growth, and the majority of experimental WPS studies reveal a glaring lack of basic knowledge of plant biology. This review aims to bridge the communication gap between WPS developers and the wider plant research community by (1) providing essential physiological and environmental background information for engineers in relation to WPS sensing capabilities, (2) offering a step-by-step guide to conduct sensor tests on plants correctly, and (3) highlighting potential challenges and suggesting WPS applications in the open field, greenhouses, and vertical farming systems. We hope this review facilitates the development of WPSs and guides them to be truly “ready for the world”.

crop monitoring↗
DOE OSTI2025

Automated qualification data tool for high temperature metallic materials

This report describes a framework for storing, processing, and displaying qualification data for high temperature mechanical properties. The framework automates the process of generating design data from mechanical test results, for example for a data qualification report for the ASME Boiler \& Pressure Vessel Code. The framework has three parts: a data storage model with common formats for several types of typical mechanical property tests, a backend based on the \pycreep Python library for correlating and extrapolating the data to generate design material properties and allowable stresses, and a demonstration user interface for displaying, sorting, and filtering the data and exploring different options for modeling the design mechanical properties. The report discusses the options available for data processing, with illustrations from real test data on Alloy 617, Alloy 709, Alloy 740H, and Laser-Powder Bed Fusion 316H. The framework is complete for ASME type data analysis and will be used to store test data generated by the Department of Energy, Office of Nuclear Energy, Advanced Materials and Manufacturing Technologies sponsored qualification programs. Future work could extend the tool to other types of material properties and/or expand the demo user interface to make it accessible across the AMMT program.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Closing the Accuracy Gap in Tandem Photovoltaic Testing: An Accessible and Efficient Spectral Tuning Method Using LED-Based Simulators for Research Laboratories and Industry

Accurate performance calibration of multijunction (MJ) solar cells is critical for advancing this technology toward large-scale terrestrial application, yet existing testing methods developed by photovoltaics calibration laboratories remain prohibitively complex and resource intensive for most research laboratories. Current approaches rely on expensive multisource simulators and/or intricate spectral tuning algorithms, limiting accessibility and hindering standardized comparisons of emerging MJ technologies such as perovskite-based tandems. This paper introduces a streamlined spectral irradiance adjustment method for light-emitting diode (LED)-based solar simulators, which are increasingly adopted in the photovoltaics community due to their versatility and cost-effectiveness. The method we present bins LED channels into groups based on the number of junctions of the MJ photovoltaic device under test (DUT) and their corresponding band gaps and incorporates an automated tuning algorithm that eliminates the need to adjust each channel’s irradiance individually. This tuning algorithm requires the relative spectral responsivities of both the DUT and a broadband reference cell, as well as a calibrated spectroradiometer. Measurement validation across various MJ devices, including III-V and perovskite/silicon tandems, demonstrates excellent agreement within 1% with well-established xenon-tungsten multisource simulators and associated tuning algorithms. By enabling precise spectral tuning with readily available equipment and a simple tuning algorithm, our approach bridges the measurement accuracy gap between research laboratories and accredited testing facilities, fostering more reliable device comparisons and accelerating the translation of MJ technologies into real-world energy systems.

14 SOLAR ENERGY↗