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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

From smart homes to smart laboratories: connected instruments for materials science

The current focus on artificial intelligence and machine learning in the scientific community has the potential to greatly speed up discovery. In this article, we explore what a “smart facility” would mean for materials science. We propose to capture meta-data at every step of an experiment, including materials synthesis, sample production and characterization, simulation, and the analysis software used to extract information. Although most of this information is captured in various institutional systems and staff logbooks, more insight could be obtained by connecting this information through a system that allows automation. AI-enabled processes built on such a system would have the potential of making experiment planning easier and minimize the time between experiment and publication.

Doucet, Mathieu↗
DOE OSTI2021

Climate Intervention Assessment and Attribution

Focal Area(s): To ensure the US is positioned to scientifically attribute noncooperative climate interventions, we propose developing a foundational analytical framework for increased confidence in the assessment and attribution of observed changes in the climate. This framework will enable probabilistic attribution of prospective climate interventions. The target is to glean insight from complex Earth system data (Focal Area 3) by using AI to develop a prediction system comprised of a hierarchy of models (Focal Area 2).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Generative Physics-Informed Neural Network Solving Multi-Scale and Multi-Phase Plasma Chemical Flow Field

Low-temperature plasmas (LTPs) are non-equilibrium systems with near-room-temperature gas and highly energetic electrons. This makes them ideal for delicate applications in biomedicine and semiconductor manufacturing, enabling processes like wound healing, sterilization, etching, and plasma-enhanced chemical vapor deposition without thermal damage. However, LTPs involve complex chemistries, with hundreds of species and thousands of reactions, complicating their diagnosis, prediction, and control. Conventional diagnostics, such as Fourier-transform infrared spectroscopy (FTIR), laser-induced fluorescence (LIF), and optical emission spectroscopy (OES), offer limited species detection, while mass spectrometry (MS) struggles with low-sensitivity species. Additionally, LTP simulations face multi-scale challenges, as macroscopic fluid dynamics and microscopic particle collisions operate on vastly different timescales. To address these issues, we developed an artificial intelligence (AI) based diagnostic system: a generative physics-informed neural network (PINN-Gen) that can predict spatially resolved species concentrations and temperatures in LTPs by integrating experimental data from planar LIF with microscopic plasma chemical kinetics and macroscopic fluid mechanics, including plasma-liquid interactions at the interface between two phases. PINN-Gen solves no equations but checks the errors of physical laws by substituting the output from neural network, and the comparison with the experimental results. Thus, it naturally avoids the multi-scale difficulty of numerical simulations and predicts the results of conventionally unsolvable multi-scale and multi-phase problems. The real-time prediction will be robust due to the physical information used in the training of such a neural network, and only very limited input of condition required due to its generative feature.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Decision Points and Practical Considerations for AI Projects

In this presentation, I will present business-relevant decisions, risks, and considerations for practical implementations of AI projects. I will use energy efficiency and renewable energy AI projects at NREL as examples and case-studies highlighting the journey from concept to implementation. First, I present challenges, questions, and trade-offs related to system inputs: the data. Next, I will examine issues with system behavior and trust, presenting examples, risks, and mitigation strategies. Finally, I will discuss challenges to effective widespread deployment of AI systems including energy, compute, and time requirements.

AI↗
DOE OSTI2026

What Is the Agent Doing? Visualizing Agentic AI Querying Workflows

We explore how visualizations can help users understand what an AI agent is doing as it builds and runs queries over data. As part of the LinkQ system, a natural language interface for querying knowledge graphs with a large language model (LLM), we designed two complementary views: A State Diagram that shows where the agent is within a larger workflow, and a Live Action Display that gives real-time updates about the agent's current task. In a study with 14 practitioners, we found that these visuals helped participants build stronger mental models of the agent's behavior while also increasing their confidence in the system. However, we also observed that users sometimes trusted incorrect outputs simply because the agent appeared to be doing the "right" thing. Our findings point to both the value and risk of visualizing agent behavior in interactive AI systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

New direction of nuclear code development: artificial intelligence

It has been a long-lasting aspiration of the nuclear power community to apply artificial intelligence (AI) technologies to nuclear power plants (NPPs), as AI is expected to substantially improve the efficiency, reliability, and continuity of NPPs. Along with the recent rise of AI featured with machining learning, there is an ever-increasing interest in utilizing the data-orientated technologies for nuclear power. In this chapter, we provide a brief summary of the history of AI and the previous efforts on introducing AI to nuclear reactor systems in 1980s. These histories suggest that although with a promising future, AI in NPPs also faces unique challenges, which are still not fully addressed and need special attention. We also introduced some recent studies that apply machine learning techniques to nuclear code development.Keywords: Nuclear power plants, code for nuclear power, artificial intelligence, expert system, machine learning

Lin, Lianshan↗
DOE OSTI2021

Interpretable Deep Learning for the Earth System with Fractal Nets

Focal Area 3: Explainable AI Our confidence in the projections made by Earth System Models (ESMs) depends on understanding them to be, in some important respects, faithful representations of the Earth system. Here we present an “explainable Artificial Intelligence (AI)” method that allows us to uncover the dynamical structure of the observed and modeled Earth system, discover hidden links across wide spatiotemporal scales, target model development efforts at poorly-represented dynamics, and optimize observed or modeled data collection to maximize predictive information. Science Challenge: Dynamical system science for the Earth system poses unique challenges given the large degree of internal climate variability. Thus, tools that help us understand how ESMs succeed and fail at representing these dynamics are crucial, particularly in relation to the observed system. Furthermore, the computational and memory constraints on ESM data output motivate in situ analysis of ESM dynamics, including automatic detection of dynamical shifts. Also, of key importance are procedures that leverage ESMs to optimize observational campaigns for improving process representation, reducing structural uncertainty and improving model skill.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

WRS Capabilities Booklet [Slides]

WRS is the digital backbone of the Weapons Program—delivering trusted data assets, cyber-assured software and systems, and AI-enabling software—that transform insights into decisive action. We empower physicists, engineers, researchers, and scientists to think faster, act strategically, and stay ahead in an ever-evolving threat landscape. Our efforts ensure critical nuclear weapons data remains secure, accessible, and usable—supporting mission-critical work, informed decision making, and scientific advancement at LANL and across the Nuclear Security Enterprise (NSE).

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2025

MSD CoP Webinar: "Generative agents: A new frontier for representing human actors and their behavior in MSD models"

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Talk #1: Behavioral Generative Agents for Energy Operations Presenter: Dr. Cong Chen (Thayer School of Engineering, Dartmouth College) Abstract: Accurately modeling consumer behavior in energy operations remains challenging due to inherent uncertainties, behavioral complexities, and limited empirical data. This talk introduces a novel approach leveraging generative agents--artificial agents powered by large language models--to realistically simulate customer decision-making in dynamic energy operations. Talk #2: Simulating multiple human perspectives in socio-ecological systems using large language models Presenter: Dr. Yongchao Zeng (Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU) of the Karlsruhe Institute of Technology in Germany) Abstract: Understanding socio-ecological systems requires insights from diverse stakeholder perspectives. This talk describes a novel simulation system called HoPeS (Human-oriented Perspective Shifting). HoPeS enables model users to not only explore simulated socio-ecological systems (SESs) from a third-person observer's perspective but also take any of the simulated stakeholder roles, like playing an RPG game. By shifting multiple perspectives, model users can reflect and integrate the situated knowledge learned through the participatory simulation, approximating a more holistic and less biased understanding of SESs. Moderators: Jim Yoon (MSD CoP Human Systems Modeling Working Group Co-Chair); Stefano Galelli (MSD CoP Using AI to Enhance MSD Research Working Group Co-Chair); Patrick M. Reed (MSD CoP Facilitation Team) This webinar was held on: November 13th, 2025 from 12-1 PM EST.

Artificial Intelligence↗
DOE OSTI2021

Explainable Artificial Intelligence in Endocrinological Medical Research

Artificial Intelligence (AI) has been a part of the medical community for decades in the form of Clinical Decision Support Systems (CDSS) to aide physicians in diagnosis and categorization of patients (1). Recent years have seen a shift from expert-derived models to the integration of machine learning (ML) algorithms to drive the output of these AI systems due to the ability of ML models to more accurately make predictions by exploiting higher dimensional and often complex data. In many cases ML models gain their advantage in accuracy by capture complex and often non-linear relationships between features being used to make the prediction. However, the hype and excitement around these methods are tempered by the clinical utility of often black-box solutions driven by skepticism of results that are difficult for practitioners to not only interpret but explain to their patients (1-3). This skepticism is not unfounded as multiple examples of black-box solutions identifying incidental correlates as the key predictors have highlighted the potential bias in a training set, or reward system, that a ML model may exploit; for example a model discerning wolves from huskies based on snow in the background rather than features of the dogs (1, 4, 5).

Endocrinological, artificial intelligence, diabete↗
DOE OSTI2025

Learning error distribution kernel‐enhanced neural network methodology for multi‐intersection signal control optimization

Traffic congestion has substantially induced significant mobility and energy inefficiency. Many research challenges are identified in traffic signal control and management associated with artificial intelligence (AI)-based models. For example, developing AI-driven dynamic traffic system models that accurately capture high-resolution traffic attributes and formulate robust control algorithms for traffic signal optimization is difficult. Additionally, uncertainties in traffic system modeling and control processes can further complicate traffic signal system controllability. To partially address these challenges, this study presents a novel, hybrid neural network model enhanced with a probability density function kernel shaping technique to formulate traffic system dynamics better and improve comprehensive traffic network modeling and control. The numerical experimental tests were conducted, and the results demonstrate that the proposed control approach outperforms the baseline control strategies and reduces overall average delays by 11.64% on average. By leveraging the capabilities of this innovative model, this study aims to address major challenges related to traffic congestion and energy inefficiency toward more effective and adaptable AI-based traffic control systems.

Wang, Hong [Oak Ridge National Laboratory (ORNL), ↗
DOE OSTI2021

Revolutionizing observations and predictability of Arctic system dynamics through next-generation dense, heterogeneous and intelligent wireless sensor networks with embedded AI

The Arctic region is a complex and dynamic system, where changes in temperature and partitioning between water in solid, liquid and vapor phase are fundamentally reshaping hydro-biogeochemical fluxes from the bedrock to the atmosphere. The increasing temperature and frequency of extreme heat, precipitation and fire events (Wang et al., 2017; Meredith et al., 2019) have critical consequences for the Arctic ecosystems and people, as well as for the global climate system. Although many observational and remote sensing activities have been on-going, detailed ecosystem processes – particularly related to controls on and impacts of permafrost thaw – are difficult to detect or measure. To improve the understanding and prediction of these extreme events and their consequences, we need a revolution in the way we collect ground-based observations from hillslope to pan-Arctic scales and couple them with satellite imagery and models to deliver rich, actionable, and scale-appropriate data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

AI-powered municipal solid waste management: a comprehensive review from generation to utilization

The accumulation of municipal solid waste (MSW) continues to rise due to burgeoning population, rapid global urbanization and economic growth, intensifying ecological concerns associated with landfills and greenhouse gas (GHG) emissions. Over the past 2 decades, global waste generation has surged by 50%, with one-third remaining uncollected and about 70% sent to landfills. This review examines the critical role of integrating emerging technologies, such as advanced sensors and artificial intelligence (AI), into end-to-end MSW management to alleviate landfill burdens. The suitability of various AI tools for different stages of MSW management is assessed, alongside the deployment of advanced sensors including hyperspectral cameras, computer vision systems, and internet of things (IoT) devices for material identification. Applications of genetic algorithms and reinforcement learning for optimizing collection routes, reducing costs, and lowering emissions are highlighted. Life cycle assessment (LCA) across all stages of MSW management is also reviewed, along with future trends in leveraging generative AI, natural language processing (NLP), and agent-based AI systems to analyze waste generation patterns and public sentiment. Efficient collection and handling can be enhanced through route optimization with geographic information systems and real-time bin-level monitoring. Furthermore, sensor-embedded, real-time object detection systems paired with robotics enable material characterization and automated sorting, thereby lowering costs and diverting waste from landfills into value-added products for diverse industrial sectors including packaging, chemicals, textiles, metals and glass, transportation, and electronics industries. Without intervention, global waste is projected to reach 4.54 billion tons by 2050, contributing direct economic costs of $\$$400 billion and roughly 2.38 billion tons of CO 2 -equivalent emissions annually. This review demonstrates how AI-driven, end-to-end solutions for MSW management can mitigate economic and environmental challenges, while directly supporting the United Nations Sustainable Development (UNDP) goals related to innovation and infrastructure (SDG 9), sustainable cities (SDG 11), responsible consumption and production (SDG 12), and climate action (SDG 13).

09 BIOMASS FUELS↗
DOE OSTI2025

Construction of approximate invariants for nonintegrable Hamiltonian systems

We present a method to construct high-order polynomial approximate invariants (AI) for nonintegrable Hamiltonian dynamical systems and apply it to a modern ring-based particle accelerator. Taking advantage of a special property of one-turn transformation maps expressed as square matrices, AIs can be constructed order by order iteratively. Evaluating AI with simulation data, we observe that AI’s fluctuation is actually a measure of chaos. Through minimizing the fluctuations, the stable region of long-term motions, i.e., the dynamic aperture of the accelerator, could be enlarged.

36 MATERIALS SCIENCE↗
DOE OSTI2026

The Perils of AI-Assisted Time Conversion Critical Errors in J2000 Time to UNIX/UTC Conversions Using AI Assistants (Technical Memorandum)

This memo addresses a critical issue encountered when using AI assistants for time system conversions, specifically converting data tagged with J2000 seconds through UNIX time to UTC. Initial AI responses to this common conversion task were fundamentally incorrect, with errors exceeding 37 seconds - enough to cause significant issues in many applications. This document demonstrates the problem, quantifies the error, and provides best practices for using AI tools safely in technical work. Both Claude 4.5 Sonnet and ChatGPT GPT-5.5 Thinking gave the same answer.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Anomalous behavior detection by an artificial intelligence-enabled system with multiple correlated sensors

Multi-metric artificial intelligence (AI)/machine learning (ML) models for detection of anomalous behavior of a machine/system are disclosed. The multi-metric AI/ML models are configured to detect anomalous behavior of systems having multiple sensors that measure correlated sensor metrics such as coolant distribution units (CDUs). The multi-metric AI/ML models perform the anomalous system behavior detection in a manner that enables both a reduction in the amount of sensor instrumentation needed to monitor the system's operational behavior as well as a corresponding reduction in the complexity of the firmware that controls the sensor instrumentation. As such, AI-enabled systems and corresponding methods for anomalous behavior detection disclosed herein offer a technical solution to the technical problem of increased failure rates of existing multi-sensor systems, which is caused by the presence of redundant sensor instrumentation that necessitates complex firmware for controlling the sensor instrumentation.

Serebryakov, Sergey↗
DOE OSTI2026

Enabling integrated AI control on DIII-D: a control system design with state-of-the-art experiments

We present the design and application of a general algorithm for Prediction And Control using MAchiNe learning (PACMAN) in DIII-D. Machine learning (ML)-based predictors and controllers have shown great promise in achieving regimes in which traditional controllers fail, such as tearing mode (TM) free scenarios, ELM-free scenarios and stable advanced tokamak conditions. The architecture presented here was deployed on DIII-D to facilitate the end-to-end implementation of advanced control experiments, from diagnostic processing to final actuation commands. This paper describes the detailed design of the algorithm and explains the motivation behind each design point. We also describe several successful ML control experiments in DIII-D using this algorithm, including a reinforcement learning controller targeting advanced non-inductive plasmas, a wide-pedestal quiescent H-mode ELM predictor, an Alfvén Eigenmode controller, a Model Predictive Control plasma profile controller and a state-machine TM predictor-controller. There is also discussion on guiding principles for real-time ML controller design and implementation.

machine learning↗