Federal AI Intelligence · U.S. Government AI Observatory

Custom inventory report

Filter: agency = DOE; AI type = classical_ml; sourcing = unknown

Generated 2026-10-12 14:04 UTCDataset: 2025 Federal Agency AI Use Case Inventory (individually reported use cases), Office of Management and Budget (OMB). Version 2025-inventory@62d5baa65fa3, processed 2026-10-11, rules 2026.10.1. Static snapshot.Applied filters: agency = DOE; AI type = classical_ml; sourcing = unknown · 64 records

Key findings

64 use-case records from 1 agencies: 0 deployed (0%), 0 pilots, 64 in development or acquisition, 0 retired, 0 with no stage reported.
6 records are designated high-impact; 0 of them (0%) answered at least one minimum-practice question.
Sourcing is reported for 0% of use cases. Where reported, n/a involve in-house development.

Lifecycle

Stage	Records	Share
Pre-deployment	64	100%

AI technology

Classification	Records	Share	Deployed
Classical / predictive ML	64	100%	0

Agencies

Agency	Records	Deployed	High-impact	Completeness
DOE · Department of Energy	64	0	6	61%

High-impact designation

Designation	Records
High-impact	6
Presumed high-impact, determined not	3
Not high-impact	55

Governance evidence: use cases designated high-impact

Practice	No answer	In scope
Pre-deployment testing	6	6
AI impact assessment	6	6
Independent review	6	6
Ongoing monitoring	6	6
Operator training	6	6
Fail-safe	6	6
Appeal process	6	6
User and public consultation	6	6

"In place" means the agency reported the practice as established. "No answer" means the field was blank; it says nothing about whether the practice exists.

Method

All figures are computed directly from the normalised inventory records. Category labels are normalised from the source answer options; original values are kept on each record. Governance answers are classified as in place, in progress, not in place, not applicable, precluded, waived, other or no answer, and blanks are never read as "no". See the Methodology page for full rules.

Limitations

Figures are what agencies reported in the 2025 inventory. They are not verified, and registration of use cases is not complete for every agency.
A blank answer is missing evidence, not evidence that a practice or safeguard is absent.
Counts are use-case records. A larger portfolio does not indicate better performance or greater AI maturity, and agencies differ in how they split systems into use cases.
The inventory has no budget, cost, accuracy or outcome data. None is shown or inferred here.
This is a disclosure analysis. It is not an audit, a compliance determination or an official government assessment.

Source records

First 40 of 64 records, deployed first. The full list is available as CSV from the explorer with the same filters.

Record	Agency ID	Agency	Use case	Stage
R3272	DOE-118	DOE	Natural Language Processing	Pre-deployment
R3273	DOE-120	DOE	DOE AI Data Infrastructure System	Pre-deployment
R3274	DOE-125	DOE	Creation of polymer datasets and inverse design of polymers with targeted backbones having High CO2 permeability and high CO2/N2 selectivity.	Pre-deployment
R3275	DOE-150	DOE	To use AI to calibrate the simulation model by matching simulation data with production history data.	Pre-deployment
R3276	DOE-179	DOE	Data discovery, processing, and generation using machine learning for a range of CCS data and information	Pre-deployment
R3282	DOE-331	DOE	ORNL: AI for Transportation and Mobility	Pre-deployment
R3283	DOE-333	DOE	ORNL: AI for Energy Generation and Distribution	Pre-deployment
R3284	DOE-334	DOE	ORNL: AI for Advanced Manufacturing	Pre-deployment
R3287	DOE-338	DOE	ORNL: AI for Neutron Science	Pre-deployment
R3289	DOE-340	DOE	ORNL: AI for Fusion Energy	Pre-deployment
R3348	DOE-485	DOE	Unleashing AI Transformer Models on FPGAs for Accelerating LHC and Particle Physics	Pre-deployment
R3366	DOE-506	DOE	Advanced Peer to Peer Transactive Energy Platform with Predictive Optimization	Pre-deployment
R3368	DOE-508	DOE	ServiceNow Predictive Intelligence	Pre-deployment
R3372	DOE-513	DOE	Nanoparticle growth kinetics and mechanism	Pre-deployment
R3374	DOE-516	DOE	Center for Mesoscale Transport Properties	Pre-deployment
R3375	DOE-517	DOE	Develop a Machine Learning Framework for Optimal Computational Campaigns for Complex Uncertain Systems	Pre-deployment
R3381	DOE-523	DOE	AI/ML in High Energy Physics Research	Pre-deployment
R3388	DOE-531	DOE	IDAES-PSE	Pre-deployment
R3389	DOE-532	DOE	NRAP-Open-IAM	Pre-deployment
R3390	DOE-533	DOE	SMMM	Pre-deployment
R3409	DOE-553	DOE	A Digital Twin for In-silico Spatiotemporally-resolved Experiments	Pre-deployment
R3410	DOE-554	DOE	Machine learning for accelerated understanding of dynamic catalysis	Pre-deployment
R3421	DOE-565	DOE	Use AI/ML ML to Optimize Data and Experiments at National Synchrotron Light Source II (NSLS-II) and the Accelerator Test Facility (ATF)	Pre-deployment
R3426	DOE-570	DOE	Data-Science Enabled, Robust and Rapid MeV Ultrafast Electron Diffraction System to Characterize Materials Including for Quantum and Energy Applications	Pre-deployment
R3428	DOE-572	DOE	Use ML as part of an integrated strategy for forecasting renewable energy resources	Pre-deployment
R3435	DOE-579	DOE	MR-DT	Pre-deployment
R3437	DOE-582	DOE	Development of a Planning, Operation, and Control Framework for Hybrid Energy Storage and Renewable Generation Systems	Pre-deployment
R3439	DOE-585	DOE	Accelerated Nanomaterial Discovery	Pre-deployment
R3447	DOE-592	DOE	Use AI/ML for Climate Prediction	Pre-deployment
R3452	DOE-597	DOE	Offshore AIIM Dashboard	Pre-deployment
R3456	DOE-600	DOE	Storage usage effectiveness and data placement optimization at Data Center	Pre-deployment
R3467	DOE-612	DOE	Objective-Driven Data Reduction for Scientific Workflows	Pre-deployment
R3469	DOE-614	DOE	Autonomous, real-time guiding of BCP film synthesis	Pre-deployment
R3472	DOE-617	DOE	Low Dose Radiation Biology	Pre-deployment
R3474	DOE-619	DOE	KBase: An Integrated Knowledgebase for Predictive Biology and Environmental Research	Pre-deployment
R3478	DOE-622	DOE	Enhancing the circularity: Cost effective battery de-energization, disassembly, and pre-processing (CEBDDP)	Pre-deployment
R3483	DOE-627	DOE	lntegrated Platform for Multimodal Data Capture, Exploration and Discovery Driven by Al Tools	Pre-deployment
R3485	DOE-629	DOE	Consolidated Nuclear Waste Glass Database	Pre-deployment
R3486	DOE-63	DOE	In-storage computing for multi-messenger astronomy in neutrino experiments and cosmological surveys	Pre-deployment
R3500	DOE-644	DOE	Accelerating HEP Science: Inference and Machine Learning at Extreme Scales	Pre-deployment

Prepared with Federal AI Intelligence by Irakli Petriashvili. Source: https://github.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory