GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: CURIE  -  Conversational Unified Research Information Engine
Agency: Department of Energy - SRS - SRNL - Savannah River Site - Savannah River National Laboratory (EM)
Problem addressed: Turning unstructured questions, tasks, or ideas into structured outcomes
Purpose and benefits: increasing productivity, enhance decision-making
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: Not used for organizational outcomes or work products
US federal AI use case: Microsoft ScreenSketch
Agency: Department of Energy - EMLA - EM Los Alamos Field Site (EM)
Purpose and benefits: AI was automatically integrated into the product without an identified benefit to the organization.
Topic area: IT
Development stage: Deployed
Impact assessment: AI integrated into the software is solely used to provide a user with assistive functions.
First used: 2023-02-01
US federal AI use case: Microsoft Visual C++ Additional Runtime
Agency: Department of Energy - EMLA - EM Los Alamos Field Site (EM)
Purpose and benefits: AI was automatically integrated into the product without an identified benefit to the organization.
Topic area: IT
Development stage: Deployed
Impact assessment: AI integrated into the software is solely used to provide a user with assistive functions.
First used: 2023-02-01
US federal AI use case: A Digital Twin for In-silico Spatiotemporally-resolved Experiments
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: A Digital Twin for In-silico Spatiotemporally-resolved Experiments
Purpose and benefits: A Digital Twin for In-silico Spatiotemporally-resolved Experiments
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: Machine learning for accelerated understanding of dynamic catalysis
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: The understanding of catalytic reactions has been a long-standing challenge due to the complexity and wide range of time scales involved in their mechanisms. There remain significant gaps in the understanding of how the catalyst's atomistic structure
Purpose and benefits: The understanding of catalytic reactions has been a long-standing challenge due to the complexity and wide range of time scales involved in their mechanisms. There remain significant gaps in the understanding of how the catalyst's atomistic structure determines the activity of reactions and how it is transiently transformed under varying operating conditions. The proposed effort seeks to take on this challenge with a data-science-driven approach to computational modeling, joining it with advanced experimental methods of characterization to create new methods for capturing realistic complexity of reactions at heterogenous and disordered interfaces. As a prototype application, we will focus on the water gas shift reaction (WGSR) CO + H2O â†’ CO2 +H2, as carried out over an active oxide (ceria CeO2) supported nanoscale Pt cluster catalyst. The Pt/CeO2 system is a high activity, low temperature catalyst for WGSR in which transient catalyst reconstructions and fluxional oscillating behavior of active sites at the metal-support interface play an essential role.
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: Computer Vision for Defect Detection
Agency: Department of Energy - SRS - ESB - Savannah River Site - Enterprise System Boundary (SRFO)
Problem addressed: "To automate and enhance the detection of  visible defects in products, improving quality control and reducing production errors."
Purpose and benefits: Improved product quality, reduced production costs, less need for manual inspections, and enhanced productivity. The AI system will lead to significant cost savings and better consistency in products over time.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: The AI use case focuses on product quality control and does not significantly affect legal, material, or critical access to services rights.
First used: 2024-10-01
US federal AI use case: Microsoft Project
Agency: Department of Energy - EMLA - EM Los Alamos Field Site (EM)
Purpose and benefits: AI was automatically integrated into the product without an identified benefit to the organization.
Topic area: IT
Development stage: Deployed
Impact assessment: AI integrated into the software is solely used to provide a user with assistive functions.
First used: 2023-02-01
US federal AI use case: HeyGen
Agency: Department of Energy - PNNL - Pacific Northwest National Laboratory (SC43 OIM)
Problem addressed: Explore the generation of engaging internal training videos using AI avatars.
Purpose and benefits: Makes internal training more engaging and effective.
Topic area: IT
Development stage: Pilot
Impact assessment: Does not meet any requiremnts for high Impact use case
First used: 2024-10-01
US federal AI use case: Nuclear Safety Analysis
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Safety, risk, and reliability analysis is performed by reactor designers, developers, and plant operators to ensure safe design and operation of the reactor and the plant; and is required by the regulators as part of license application. Performing s
Purpose and benefits: The outcome of this effort will provide a tool to the safety analysis teams which will enable them to automate creating the risk models resulting in significant reduction in time spent on performing safety analysis, writing SARs, and conducting the regulatory review of safety case.
Topic area: Other
Development stage: Pilot
Risk category: High-impact
US federal AI use case: CrewAI
Agency: Department of Energy - PNNL - Pacific Northwest National Laboratory (SC43 OIM)
Problem addressed: Automate and orchestrate workflows across LLMs and cloud platforms.
Purpose and benefits: Boosts efficiency by automating complex workflows across platforms.
Topic area: IT
Development stage: Pilot
Impact assessment: No high-impact category applies
First used: 2025-01-01
US federal AI use case: AI for QA Audit
Agency: Department of Energy - ORNL - Oak Ridge National Laboratory (SC43 OIM)
Problem addressed: Improve quality assurance at different levels of the Lab.
Purpose and benefits: Improved quality assurance
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: Does not meet any requiremnts for high Impact use case
US federal AI use case: Google Chrome Generative AI
Agency: Department of Energy - EMLA - EM Los Alamos Field Site (EM)
Purpose and benefits: AI was automatically integrated into the product without an identified benefit to the organization.
Topic area: IT
Development stage: Deployed
Impact assessment: Does not meet the criteria for High-Impact. Unless explicitly deployed in a safety-critical or classified environment, it should be considered Not High Impact.
First used: 2023-12-06
US federal AI use case: PWS Builder
Agency: Department of Energy - IM-50 - Architecture Engineering Technology and Innovation (IM)
Problem addressed: Improved speed to create a performance work statement document.
Purpose and benefits: DOE employees would be able to quickly and accurately draft performance work statements for projects both net-new and in-flight. This would greatly reduce the time needed to get a project up and running.
Topic area: Admin Functions
Development stage: Deployed
Impact assessment: AI integrated into the software is solely used to provide a user with assistive functions.
First used: 2024-10-01
US federal AI use case: ServiceNow Classification Prediction
Agency: Department of Energy - LANL - Los Alamos National Lab (LAFO)
Problem addressed: Inconsistencies in classification values determined by human technicians
Purpose and benefits: Improved automation
Topic area: IT
Development stage: Deployed
Impact assessment: It does not meet the definition outlined by OMB.
First used: 2024-11-01
US federal AI use case: Safeguards Digital Twin
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: This deals with international safeguars approaches.
Purpose and benefits: Reduce the burden for inspectors by increasing synthesizing data and announcing anomalies.
Topic area: Science
Development stage: Pilot
Impact assessment: ServiceNow's AI output is NOT a principal driver of a government decision that would meaningfully affects people's rights, safety, critical access, or strategic assets. AI in ServiceNow is strictly used to help manage IT resources for LANL.
US federal AI use case: Use AI/ML ML to Optimize Data and Experiments at National Synchrotron Light Source II (NSLS-II) and the Accelerator Test Facility (ATF)
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: Three projects: use ML for denoising in scientific images; use ML to mine large quantities of data for automated evaluation of data quality and predictive analysis;  develop AI/ML infrastructure to tune / align / optimize instruments/beamlines at NSL
Purpose and benefits: Three projects: use ML for denoising in scientific images; use ML to mine large quantities of data for automated evaluation of data quality and predictive analysis;  develop AI/ML infrastructure to tune / align / optimize instruments/beamlines at NSLS-II and ATF
Topic area: Science
Development stage: Pre-deployment
Impact assessment: This deals with international safeguars approaches.
US federal AI use case: Tabnine AI Pair Programmer
Agency: Department of Energy - KCNSC - Kansas City National Security Campus (KCFO)
Problem addressed: Accelerate and simplify software development across our entire site. There is no definite 'problem' being solved, just capitalizing on an opportunity to leverage industry investment in generative AI for software development
Purpose and benefits: This use case will boost engineering velocity, code quality, and developer happiness by automating the coding workflow through AI tools customized to our teams.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: AI provides structured recommendations only; human reviewers retain authority for evaluation decisions
First used: 2025-07-16
US federal AI use case: AI-Based Chat Bot
Agency: Department of Energy - IM-60 - IM Enterprise Operations and Shared Services (IM)
Development stage: Retired
Impact assessment: Output does not serve as a principal basis for decisions or actions that have a legal, material, binding, or significant effect on rights or safety. Assists in developing software code that may accept product, but this AI will not make those decision
US federal AI use case: Ask Alan
Agency: Department of Energy - SRS - ESB - Savannah River Site - Enterprise System Boundary (SRFO)
Problem addressed: GenAI/AI interactive learning
Purpose and benefits: Productivity Tool
Topic area: IT
Development stage: Pre-deployment
Impact assessment: End User Productivity Tool
First used: 2025-09-01
US federal AI use case: Elastic Stack Technology (ELK)
Agency: Department of Energy - Y-12 - Consolidated Nuclear Security Y-12 (YFO)
Problem addressed: Increase searchability of documents for information pertinent to the mission
Purpose and benefits: Enable intelligent media cataloging to assist with data discovery
Topic area: IT
Development stage: Deployed
Impact assessment: No high-impact category applies
First used: 2022-11-01
US federal AI use case: Data-Science Enabled, Robust and Rapid MeV Ultrafast Electron Diffraction System to Characterize Materials Including for Quantum and Energy Applications
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: Data-Science Enabled, Robust and apid MeV Ultrafast Electron Diffraction System to Characterize Materials Including for Quantum and Energy Applications
Purpose and benefits: Data-Science Enabled, Robust and apid MeV Ultrafast Electron Diffraction System to Characterize Materials Including for Quantum and Energy Applications
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: GitHub Co-Pilot
Agency: Department of Energy - RL - Hanford - Richland Operations Office - Hanford (EM)
Problem addressed: quicker, more complete, and safer code development
Purpose and benefits: quicker, more complete, and safer code development
Topic area: IT
Development stage: Deployed
Impact assessment: This AI system does not provide a principle basis for decisions or actions with legal, material binding, or have any significat effects of those items listed in OMB-25-21 in the definition of High Impact.
First used: 2025-08-25
US federal AI use case: Use ML as part of an integrated strategy for  forecasting renewable energy resources
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: This is an inderdisciplinary project aiming to develop a novel system that transforms the forecasting of renewal energy resources by seamlessly integrating a numerical weather prediction model, ML, and measurements.
Purpose and benefits: This is an inderdisciplinary project aiming to develop a novel system that transforms the forecasting of renewal energy resources by seamlessly integrating a numerical weather prediction model, ML, and measurements.
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: DNA-P Use Cases Leaverging Artificial Intellegence (Pre-Development)
Agency: Department of Energy - NA-IM (Enterprise) - Office of the Associate Administrator for Information Management and Chief Information Officer (HQ-Enterprise) (NNSA HQ)
Problem addressed: "- identify data clusters/trend analysis - identify data discrepencies/data enrichment - generate suggestions (including generating reports, data linkages, and courses of action) - generate graphical and natural language analyses"
Purpose and benefits: "-save time for DOE DNA-P users
- improve DOE/NNSA data quality
- improve DOE/NNSA safety operations"
Topic area: Other
Development stage: Pre-deployment
Impact assessment: This use case is determined to be not high-impact base on definitoin in Section 5 of the OMB Memorandum M-25-21.
US federal AI use case: M365 Copilot
Agency: Department of Energy - LANL - Los Alamos National Lab (LAFO)
Problem addressed: Collaboration and Productivity improvements
Purpose and benefits: Automation of routine tasks such as email drafting, meeting summaries, and document generation.  Improved efficiency in the use of mundane tasks.
Topic area: Other
Development stage: Pilot
Risk category: Presumed high-impact (determined not high)
Impact assessment: The M365 Copilot and it's variants are integrated into all LANL collaboration and productivity services within M365 including email and Teams.
First used: 2025-07-01
US federal AI use case: EDMS Admin
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Machine-readable information is difficult to quickly summarize by humans. By enabling summaries of information in our electronic document management system we have a previously-unavailable capability that addresses this problem.
Purpose and benefits: Reduces administrative burden through intelligent automation of routine tasks.
Topic area: Admin Functions
Development stage: Pilot
Impact assessment: Used for records managment functions.
First used: 2025-09-02
US federal AI use case: EnerGPT Canvas
Agency: Department of Energy - IM-50 - Architecture Engineering Technology and Innovation (IM)
Problem addressed: Improves the speed and accuracy DOE employees are able to draft, edit, and produce content.
Purpose and benefits: Allowing DOE users to edit their projects using AI in one single platform.
Topic area: Admin Functions
Development stage: Deployed
Impact assessment: It does not meet the definition defined by OMB.
First used: 2025-08-06
US federal AI use case: Microsoft Copilot
Agency: Department of Energy - EMLA - EM Los Alamos Field Site (EM)
Purpose and benefits: AI was automatically integrated into the product without an identified benefit to the organization.
Topic area: IT
Development stage: Deployed
Impact assessment: AI integrated into the software is solely used to provide a user with assistive functions.
First used: 2023-02-01
US federal AI use case: Project Optimus - Prime Contract
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Not available
Purpose and benefits: Improves contract lifecycle efficiency and compliance tracking.
Topic area: Other
Development stage: Pilot
Impact assessment: Used as a general/business chat agent for genAI.
US federal AI use case: MR-DT
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Aid safeguards analysts for determining if a reactor is being used in a non-declared way.
Purpose and benefits: Reduce the burden for inspectors by increasing synthesizing data and announcing anomalies.
Topic area: Science
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: This deals with international safeguars approaches.
US federal AI use case: Raytheon Multimedia Monitoring System (M3S)
Agency: Department of Energy - Y-12 - Consolidated Nuclear Security Y-12 (YFO)
Problem addressed: Video transcript generation
Purpose and benefits: Enable off-cloud transcription of pre-recorded video media to improve data discoverability
Topic area: IT
Development stage: Deployed
Impact assessment: No high-impact category applies
First used: 2024-01-18
US federal AI use case: Development of a Planning, Operation, and Control Framework for Hybrid Energy Storage and Renewable Generation Systems
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: One project: will develop the initial framework for planning, operation, and control of these 'hybrid' energy systems containing high penetrations of renewables together with energy storage, non-wires alternatives, and conventional generating resourc
Purpose and benefits: One project: will develop the initial framework for planning, operation, and control of these 'hybrid' energy systems containing high penetrations of renewables together with energy storage, non-wires alternatives, and conventional generating resources by leveraging BNL's expertise in energy storage technologies, probabilistic-based planning and control solutions, and machine learning techniques.
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: GitHub Copilot
Agency: Department of Energy - SLAC - SLAC National Accelerator Laboratory (SC43 OIM)
Problem addressed: Enhancement of developer productivity
Purpose and benefits: Cost savings and efficiency
Topic area: IT
Development stage: Pilot
Impact assessment: This AI system does not meet the criteria of any of the six pillars that make up High Impact AI in Memorandum M-25-21
First used: 2025-07-01
US federal AI use case: Accelerated Nanomaterial Discovery
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: Historically the discovery and development of new materials has followed an iterative process of synthesis, measurement, and modeling, suitable integration of advanced characterization, robotics, and machine-learning provides an opportunity for radic
Purpose and benefits: Historically the discovery and development of new materials has followed an iterative process of synthesis, measurement, and modeling, suitable integration of advanced characterization, robotics, and machine-learning provides an opportunity for radically accelerating the material design process. The CFN has an established record of discovering nanomaterials by applying new materials synthesis strategies, advanced characterization, and machine-learning. Integrating these efforts will enable autonomous platforms for iteratively exploring material parameter spaces, which have potential to revolutionize materials science by uncovering fundamental links between synthetic pathways, material structure, and functional properties.
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: GitHub Copilot
Agency: Department of Energy - WAPA - Western Area Power Administration (PMA)
Problem addressed: Improve the quality and speed of code development.
Purpose and benefits: Speed the delivery of code development, edits and troublshooting.
Topic area: IT
Development stage: Deployed
Impact assessment: AI output does not serve as a principal basis for decisions or action with legal, material, binding or signficant effect on high impact areas.
First used: 2024-03-31
US federal AI use case: DIRECTIVES
Agency: Department of Energy - SRS - ESB - Savannah River Site - Enterprise System Boundary (SRFO)
Problem addressed: GenAI-RAG on DOE/NNSA content
Purpose and benefits: Productivity Tool
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: End User Productivity Tool
First used: 2024-10-01
US federal AI use case: AI-Assisted Strategies and Solutions for Environmental Technology (AI-ASSET)
Agency: Department of Energy - SRS - SRNL - Savannah River Site - Savannah River National Laboratory (EM)
Problem addressed: Enable rapid technology transfer of the ALTEMIS AI approaches to new sites and new systems through an automated data analysis and knowledge management toolkit
Purpose and benefits: AI-assisted monitoring system design, generalized AIML contaminant forecasting framework, development of end state recommendations that account for site-specific environmental/technological/regulatory constraints, EM knowledge management
Topic area: Energy and Environment
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: In development stage and impact has not been determined
US federal AI use case: Google Agentspace / NotebookLM
Agency: Department of Energy - PNNL - Pacific Northwest National Laboratory (SC43 OIM)
Problem addressed: Unify enterprise data and enable large-scale agent deployment with Google Agentspace.
Purpose and benefits: Improves enterprise knowledge use and team productivity at scale.
Topic area: Science
Development stage: Pilot
Impact assessment: Does not meet any requiremnts for high Impact use case
First used: 2025-07-01
US federal AI use case: Cognitive Prescreen Tool (CPT)
Agency: Department of Energy - Y-12 - Consolidated Nuclear Security Y-12 (YFO)
Problem addressed: Classification recommendations to assist the Derivative Classifier in making  a document's overall classification determination.
Purpose and benefits: Serve as a recommender to Derivative Classifier to assist with document review to improve process accuracy and efficiency, in that order.
Topic area: IT
Development stage: Deployed
Impact assessment: No high-impact category applies
First used: 2019-12-18
US federal AI use case: SpyglassGPT Chat Assistant
Agency: Department of Energy - NR HQ - Naval Reactors (NR)
Problem addressed: Aid in routine administrative tasks
Purpose and benefits: Increased efficiency for routine tasks
Topic area: Admin Functions
Development stage: Deployed
Impact assessment: Output of generative AI is administratively prohibited from being used as principal responses in the various scenarios considered high impact.
First used: 2025-08-18
US federal AI use case: Custom GenAI for Advanced Reactor Development (LotusAI)
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Engineers spending extensive time manually iterating through documents during the development of the LOTUS Test Bed, and have to frequently answer user questions.
Purpose and benefits: Engineers spending extensive time manually iterating through documents during the development of the LOTUS Test Bed, and have to frequently answer user questions.
Topic area: Other
Development stage: Pilot
Risk category: High-impact
First used: 2025-08-04
US federal AI use case: Use AI/ML for Climate Prediction
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: Two projects: leverage AI/ML tools to synthesize uncertainty quantification-targeted, complex, multi-scale, multi-domain observations into high-resolution processmodels to characterize 4D variability in aerosol dynamics and evolution, which accounts
Purpose and benefits: Two projects: leverage AI/ML tools to synthesize uncertainty quantification-targeted, complex, multi-scale, multi-domain observations into high-resolution processmodels to characterize 4D variability in aerosol dynamics and evolution, which accounts for large uncertainties in climate models; partial differential equation solving using machine learning for simulating the aerosol-cloud-precipitation system that is recognized to be the key in forecasting weather and climate change
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: Microsoft CoPilot
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Generative AI tools like this solve the problem of time-consuming knowledge work by instantly providing expert-level assistance with writing, coding, research, and analysis. They automate repetitive tasks that require human expertise, allowing people
Purpose and benefits: Boost employee productivity by helping to create content, assist in coding, and complex problem-solving tasks, while democratizing access to sophisticated capabilities like writing and design. This technology promises to accelerate innovation by serving as an intelligent collaborator, freeing humans to focus on higher-level strategic and creative work
Topic area: Admin Functions
Development stage: Deployed
Impact assessment: Used for general purpose generative AI within a COTS product.
First used: 2025-07-03
US federal AI use case: M&O Program Trimester Reporting Modernization
Agency: Department of Energy - PNNL - Pacific Northwest National Laboratory (SC43 OIM)
Problem addressed: Modernize and automate trimester reporting to improve clarity and consistency.
Purpose and benefits: Increases transparency and efficiency in program reporting.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: Does not meet any requiremnts for high Impact use case
First used: 2024-10-01
US federal AI use case: AI and natural-language powered search
Agency: Department of Energy - EE HQ - EE Headquarters (EE)
Problem addressed: Solve records retrieval issues.
Purpose and benefits: Efficienct retrieval of records.
Topic area: IT
Development stage: Deployed
Impact assessment: Does not fall within the requirements for high impact.
First used: 2019-01-02
US federal AI use case: Microsoft Visual C++ Redistributable
Agency: Department of Energy - EMLA - EM Los Alamos Field Site (EM)
Purpose and benefits: AI was automatically integrated into the product without an identified benefit to the organization.
Topic area: IT
Development stage: Deployed
Impact assessment: AI integrated into the software is solely used to provide a user with assistive functions.
First used: 2023-02-01
US federal AI use case: Offshore AIIM Dashboard
Agency: Department of Energy - NETL - National Energy Technology Laboratory (FECM)
Problem addressed: Evaluate the integrity of offshore energy infrastructure (e.g., pipelines, platforms) in the U.S Gulf Region.
Purpose and benefits: Evaluate integrity of infrastructure.
Topic area: Energy and Environment
Development stage: Pre-deployment
Impact assessment: Limited to a specific area of research
US federal AI use case: EDX-ClaiMM
Agency: Department of Energy - NETL - National Energy Technology Laboratory (FECM)
Problem addressed: Address fundamental knowledge gaps and foster the innovation of new techniques for enhanced characterization and recovery of critical minerals and materials (CMMs) within the US.
Purpose and benefits: Address fundamental knowledge gaps.
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Limited to a specific area of research
US federal AI use case: Microsoft Teams
Agency: Department of Energy - EMLA - EM Los Alamos Field Site (EM)
Purpose and benefits: AI was automatically integrated into the product without an identified benefit to the organization.
Topic area: IT
Development stage: Deployed
Impact assessment: AI integrated into the software is solely used to provide a user with assistive functions.
First used: 2023-02-01
US federal AI use case: Machine Learning for Linac Improved Performance
Agency: Department of Energy - FNAL - Fermi National Accelerator (SC43 OIM)
Problem addressed: In Linacs at FNAL and J-PARC, the current emittance optimization procedure is limited to manual adjustments of a few parameters; using a larger number is not practically feasible for a human operator. Using machine learning (ML) techniques allows li
Purpose and benefits: Daily fluctuations in the Ion Source conditions as well af the effect of environmental changes to RF systems and cavities affect Linac beam. Results include increased beam loss resulting in increased beamline component irradiation, decreased beam intensity to downstream machines affecting Accelerator Complex deliverables, drifts in Linac beam energy directly affecting Booster losses. These drifts are not easily predictable since we do not have environmental control on the RF gallery, not enough instrumentaion in the Ion Source or Linac proper. To counter these effects, we are developing AI-based optimization and modeling, including Bayesian Optimization and surrogate model-based optimization, with the ultimate goal of (near) real-time RF compensation.
Topic area: Science
Development stage: Pilot
Impact assessment: The use case does not have an effect on civil rights/liberties/privacy, access to education/housing/insurance/credit/employment, access to critical government resources/services, human health/safety, critical infrastructure/public safety
First used: 2025-09-25
US federal AI use case: Storage usage effectiveness and data placement optimization at Data Center
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: The goal of this project is to take data management for data centers to the next level by implementing Artificial Intelligence (AI) and Machine Learning (ML) to create a precise data use prediction model to aid important business and operational deci
Purpose and benefits: The goal of this project is to take data management for data centers to the next level by implementing Artificial Intelligence (AI) and Machine Learning (ML) to create a precise data use prediction model to aid important business and operational decisions
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: xAI Grok Pilot
Agency: Department of Energy - LLNL - Lawrence Livermore National Laboratory (LFO)
Problem addressed: Quick access to general knowledge.
Purpose and benefits: Ability to quickly find and access general knowledge and business productivity
Topic area: IT
Development stage: Pilot
Impact assessment: General chatbot knowledge and capabilities
First used: 2025-06-30
US federal AI use case: Microsoft Search in Bing
Agency: Department of Energy - EMLA - EM Los Alamos Field Site (EM)
Purpose and benefits: AI was automatically integrated into the product without an identified benefit to the organization.
Topic area: IT
Development stage: Deployed
Impact assessment: AI integrated into the software is solely used to provide a user with assistive functions.
First used: 2023-02-01
US federal AI use case: Microsoft Copilot (Pilot)
Agency: Department of Energy - WAPA - Western Area Power Administration (PMA)
Problem addressed: Improve general office productivity
Purpose and benefits: Content creation and drafting, data analysis and summerization, personalized learning and research
Topic area: Other
Development stage: Pilot
Impact assessment: AI output does not serve as a principal basis for decisions or action with legal, material, binding or signficant effect on high impact areas.
First used: 2025-08-01
US federal AI use case: FindMATID
Agency: Department of Energy - SRS - ESB - Savannah River Site - Enterprise System Boundary (SRFO)
Problem addressed: Search for corporate material IDs
Purpose and benefits: Maxmize use of Strategic Agreements
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
Impact assessment: End User Productivity Tool
First used: 2024-10-01
US federal AI use case: AI Enabled Code Review
Agency: Department of Energy - ORNL - Oak Ridge National Laboratory (SC43 OIM)
Problem addressed: Enhance application development lifecycle capabilities
Purpose and benefits: Expidited code production
Topic area: IT
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Microsoft Teams Classic
Agency: Department of Energy - EMLA - EM Los Alamos Field Site (EM)
Purpose and benefits: AI was automatically integrated into the product without an identified benefit to the organization.
Topic area: IT
Development stage: Deployed
Impact assessment: AI integrated into the software is solely used to provide a user with assistive functions.
First used: 2023-02-01
US federal AI use case: Custom GenAI for Advanced Reactor Development (RickoverAI)
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Reactor engineers spending extensive time manually iterating through documents during the reactor engineering process
Purpose and benefits: Reactor engineers save time by using the AI to quickly find answers and relevant source documents.
Topic area: Other
Development stage: Pilot
Risk category: High-impact
First used: 2025-08-04
US federal AI use case: AI Denoising
Agency: Department of Energy - FNAL - Fermi National Accelerator (SC43 OIM)
Development stage: Retired
Impact assessment: The use case does not have an effect on civil rights/liberties/privacy, access to education/housing/insurance/credit/employment, access to critical government resources/services, human health/safety, critical infrastructure/public safety, stratey,.
US federal AI use case: Report Assistant
Agency: Department of Energy - SRS - ESB - Savannah River Site - Enterprise System Boundary (SRFO)
Problem addressed: Management spends excessive hours generating and reformatting reports for various customers/audiences this will reduce the learing curve and effort to translate information into the various formats, saving consideratble time and effort.
Purpose and benefits: This will reduce the time and effort required to generate reports, allowing management to focus on more critical tasks. It is expected to halve the time spent on report generation in the first year.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: This use case does not significantly affect legal, material, or binding rights or critical access to services.
First used: 2024-10-01
US federal AI use case: AI-Tailored Learning Management Solutions
Agency: Department of Energy - SRS - ESB - Savannah River Site - Enterprise System Boundary (SRFO)
Problem addressed: To improve the quality, relevance, and delivery of training materials, ensuring they are tailored and effective for users.
Purpose and benefits: Improved training materials, dynamic learning tailored to user needs, real-time feedback, comprehensive evaluation, and higher staff effectiveness and readiness.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: This AI use case focuses on enhancing training materials and does not significantly affect legal, material, or binding rights or critical access to services.
First used: 2024-10-01
US federal AI use case: Objective-Driven Data Reduction for Scientific Workflows
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: This project aims to develop theories and algorithms for objective-driven reduction of scientific data in workflows that are composed of various models, including data-driven AI models
Purpose and benefits: This project aims to develop theories and algorithms for objective-driven reduction of scientific data in workflows that are composed of various models, including data-driven AI models
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: LivChat
Agency: Department of Energy - LLNL - Lawrence Livermore National Laboratory (LFO)
Problem addressed: Quick access to general policy and process knowledge.
Purpose and benefits: Ability to quickly find and access internal process and procedure knowledge, and and business productivity
Topic area: IT
Development stage: Deployed
Impact assessment: General business process and policy knowledge.
First used: 2025-06-30
US federal AI use case: Autonomous, real-time guiding of BCP film synthesis
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: Adaptive synthesis and manufacturing processes will be realized by combining AI/ML methods for autonomous on-the-fly control of the deposition and processing of self-assembling polymer films.
Purpose and benefits: Adaptive synthesis and manufacturing processes will be realized by combining AI/ML methods for autonomous on-the-fly control of the deposition and processing of self-assembling polymer films.
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: Ask IT
Agency: Department of Energy - SRS - ESB - Savannah River Site - Enterprise System Boundary (SRFO)
Problem addressed: GenAI-RAG on IT content
Purpose and benefits: Productivity Tool
Topic area: IT
Development stage: Pre-deployment
Impact assessment: End User Productivity Tool
First used: 2024-10-01
US federal AI use case: ServiceNow Now Assist (AskIT)
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Service Desk operations
Purpose and benefits: Improve the timeliness and quality of transactional service desk requests through improved incident search, resolution, feedback as well as AI-assisted coding of  workflows.
Topic area: Other
Development stage: Deployed
Impact assessment: Used for Help Desk Function
US federal AI use case: Low Dose Radiation Biology
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: Predicting the effects of low dose radiation
Purpose and benefits: Predicting the effects of low dose radiation
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: Claude Anthropic Enterprise
Agency: Department of Energy - PNNL - Pacific Northwest National Laboratory (SC43 OIM)
Problem addressed: Deliver secure enterprise AI with Claude's large-context models and code generation.
Purpose and benefits: Offers secure, large-context AI tools for research and analysis.
Topic area: Science
Development stage: Pilot
Impact assessment: Does not meet any requiremnts for high Impact use case
First used: 2025-08-01
US federal AI use case: KBase: An Integrated Knowledgebase for Predictive Biology and Environmental Research
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: Building knowledgebase for systems biology and enabling the predictive analysis using web interface
Purpose and benefits: Building knowledgebase for systems biology and enabling the predictive analysis using web interface
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: Next-Generation Beam Cooling and Control with Optical Stochastic Cooling
Agency: Department of Energy - FNAL - Fermi National Accelerator (SC43 OIM)
Problem addressed: This program leverages the physics and technology of optical stochastic cooling (OSC) to explore new possibilities in beam control and sensing.  The planned architecture and performance of a new OSC system at IOTA should enable turn-by-turn programma
Purpose and benefits: This effort focuses on enhanced real-time control of the structure of circulating particle beams. The additional performance and capabilities provided may enable substantially greater operational flexibility and science reach at current and future DOE accelerator facilities.
Topic area: Science
Development stage: Pre-deployment
Impact assessment: The use case does not have an effect on civil rights/liberties/privacy, access to education/housing/insurance/credit/employment, access to critical government resources/services, human health/safety, critical infrastructure/public  safety
US federal AI use case: AI Video Creation
Agency: Department of Energy - ORNL - Oak Ridge National Laboratory (SC43 OIM)
Problem addressed: Enhance internal communications and streamline Learning and Development
Purpose and benefits: Tool that lab staff can use to facilitate creation of generative AI enabled media
Topic area: Other
Development stage: Pre-deployment
Impact assessment: Doesn't meet the criteria.
US federal AI use case: ChatGPT
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Generative AI tools like this solve the problem of time-consuming knowledge work by instantly providing expert-level assistance with writing, coding, research, and analysis. They automate repetitive tasks that require human expertise, allowing people
Purpose and benefits: Boost employee productivity by helping to create content, assist in coding, and complex problem-solving tasks, while democratizing access to sophisticated capabilities like writing and design. This technology promises to accelerate innovation by serving as an intelligent collaborator, freeing humans to focus on higher-level strategic and creative work
Topic area: IT
Development stage: Pilot
Impact assessment: Used for general purpose generative AI within a COTS product.
First used: 2025-06-25
US federal AI use case: Enhancing the circularity: Cost effective battery de-energization, disassembly, and pre-processing (CEBDDP)
Agency: Department of Energy - EE HQ - EE Headquarters (EE)
Problem addressed: Predict state of health and state of function of spent batteries
Purpose and benefits: Improved ability for prediction to enable potential reuse of batteries, towards reducing costs of batteries
Topic area: Energy and Environment
Development stage: Pre-deployment
Impact assessment: Will serve as basis for decisions on use of end-of-first life batteries
US federal AI use case: Microsoft MSPaint
Agency: Department of Energy - EMLA - EM Los Alamos Field Site (EM)
Purpose and benefits: AI was automatically integrated into the product without an identified benefit to the organization.
Topic area: IT
Development stage: Deployed
Impact assessment: AI integrated into the software is solely used to provide a user with assistive functions.
First used: 2023-02-01
US federal AI use case: AGN-201 DT
Agency: Department of Energy - NE INL - NE Idaho National Laboratory (NE)
Problem addressed: Aid safeguards analysts for determining if a reactor is being used in a non-declared way.
Purpose and benefits: Reduce the burden for inspectors by increasing synthesizing data and announcing anomalies.
Topic area: Science
Development stage: Pilot
Impact assessment: This deals with international safeguards approaches.
US federal AI use case: Intrabot
Agency: Department of Energy - Pantex - PanTeXas Deterrence Pantex (PFO)
Problem addressed: The AI  is intended to improve efficiency by providing plant personnel with a faster way to access information on the Pantex Intranet.
Purpose and benefits: The expected benefit is increased effiency for plant personnel.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: The AI output is not a basis for decisions or actions. It is for information retrieval.
US federal AI use case: AI Drafting of Operational Procedures and Training Materials
Agency: Department of Energy - SRS - ESB - Savannah River Site - Enterprise System Boundary (SRFO)
Problem addressed: "To automate and enhance the drafting of operational  procedures and training materials, reducing time, effort, and errors."
Purpose and benefits: "Reduced development time, reduced errors, and rework, 
better context understanding, and improved standardization and formatting, cutting time spent on document creation by half."
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: This AI use case does not significantly affect legal, material, or binding rights or critical access to services.
First used: 2024-10-01
US federal AI use case: lntegrated Platform for Multimodal Data Capture, Exploration and Discovery Driven by Al Tools
Agency: Department of Energy - BNL - Brookhaven National Laboratory (SC43 OIM)
Problem addressed: This project will enable and accelerate scientific discovery by leveraging large complex multimodal datasets generated at BES user facilities, develop shared transferrable infrastructure to store, curate, analyze and disseminate the data.  Additional
Purpose and benefits: This project will enable and accelerate scientific discovery by leveraging large complex multimodal datasets generated at BES user facilities, develop shared transferrable infrastructure to store, curate, analyze and disseminate the data.  Additionally, we will build data analysis tools that reveal correlations in multimodal data and apply Machine Learning (ML) methods and train artificial Intelligence (AI) models that efficiently extract synergistic physical information and embed such models in new workflows for rapid scientific discovery.
Topic area: Science
Development stage: Pre-deployment
Impact assessment: Does not meet definition
US federal AI use case: Improve Scout Search results
Agency: Department of Energy - PNNL - Pacific Northwest National Laboratory (SC43 OIM)
Problem addressed: Improve enterprise search with natural language prompts and personalization.
Purpose and benefits: Provides faster, more accurate access to enterprise knowledge.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: Does not meet any requiremnts for high Impact use case
First used: 2024-10-01
US federal AI use case: Consolidated Nuclear Waste Glass Database
Agency: Department of Energy - SRS - SRNL - Savannah River Site - Savannah River National Laboratory (EM)
Problem addressed: Incorporation of several physics-driven machine learning models to predict the properties of nuclear waste glass compositions – in addition, bootstrap other glass computational science models such as GlassPy and GlassNet to the database
Purpose and benefits: Develop an opensource, online database consisting of property information for nuclear waste glass data generated by various national laboratories over several decades
Topic area: Energy and Environment
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: In development stage and impact has not been determined