Department of Energy

The AI is intended to improve efficiency by providing plant personnel with a faster way to access information on the Pantex Intranet.

The AI is intended to improve the efficiency of plant personnel by giving them an easy way to access and search cybersecurity documents.

The AI is intended to solve the inefficiency and potential for error in manually developing and updating preventive maintenance procedures.

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 infrastr...

Developed natural language processing (NLP) algorithms will be used to help categorize and understand various energy storage efforts in the R&D communities.

Leveraging generative AI and cloud-enabled data infrastructure to improve carbon capture and storage user experience

Creation of polymer datasets and inverse design of polymers with targeted backbones having High CO2 permeability and high CO2/N2 selectivity.

To use AI to calibrate the simulation model by matching simulation data with production history data.

Data discovery, processing, and generation using machine learning for a range of carbon capture and storage data and information

To help automate data discovery and preparations to support a range of CS models, tools, and products

accelerate scientific discovery

accelerate scientific discovery

accelerate scientific discovery

accelerate scientific discovery

accelerate scientific discovery

accelerate scientific discovery

accelerate scientific discovery

accelerate scientific discovery

Offers extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems.

Enables quantification of containment effectiveness and leakage risk at carbon storage sites in the context of system uncertainties and variability.

AI/ML is being used to evaluate measurements in real-time during simultaneous experiments on two beamlines and then drive subsequent data collection on both of the beamlines to maximize the scientific value generated...

Content analysis across domains and structured/unstructured content for SCRM

Enhance and automate PDF scraping for large sets of PDF files.

Turning unstructured questions, tasks, or ideas into structured outcomes

A Digital Twin for In-silico Spatiotemporally-resolved Experiments

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.

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...

Improve quality assurance at different levels of the Lab.

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 / optim...

Data-Science Enabled, Robust and apid MeV Ultrafast Electron Diffraction System to Characterize Materials Including for Quantum and Energy Applications

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.

"- 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 langu...

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, an...

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...

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 dynami...

Evaluate the integrity of offshore energy infrastructure (e.g., pipelines, platforms) in the U.S Gulf Region.

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.

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 import...

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

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.

Predicting the effects of low dose radiation

Building knowledgebase for systems biology and enabling the predictive analysis using web interface

Aid safeguards analysts for determining if a reactor is being used in a non-declared way.

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...

Developing Galaxy Image deblending with the gravitational lensing effects and scaling AI/ML algorithms

AI will make discoveries of unknown combinations of rare earth elements and ligands used in critical materials that are currently impossible to separate

Seeking a continuous local structure motif that correlates to X-ray spectral signatures

Enhance the capability to find structured and unstructured data within databases and datalakes.

BNL will work alongside SLAC, to implement ML algorithm(s) into NSLS-II Operations to interpret accelerator data more intelligently.

One project: Current deep neural network (DNN)-based Artificial Intelligence (AI) algorithms have already been successfully applied to particle physics applications.

Develop state-of-the-art cycle-consistent GANs to bridge the gap between simulations and experimental data; develop real-time particle tracking with deep learning on field programmable gate arrays; explore the challen...

A SciDAC computer science institute and BNL is co-leading AI team

Three projects: use expertise in ML as one element in building an integrated multiscale bioimaging capability at BNL; use AI/ML tools use to accelerate the analysis of protein structure and function; develop a high-re...

Making sure vendors are in compliance with regulations.

'Coherent X-rays are routinely provided today by the latest Synchrotron and X-ray Free-electron Laser Sources.

Mitigate workplace ncidents

Use AI Agents for cybersecurity review of documents and structured data to improve processes and to help identify areas of risk.

New advances in scientific applied machine learning (ML) offer an opportunity to leverage the commonalities, scientific insights and collected experience of the larger scientific user facility community across differe...

Applying AI/ML methods to discover better solid state battery interface material using HPC

We propose to develop an end-to-end, 5G-enabled, reliable, and decentralized IoT framework that improves data collection and communication among edge computing devices for science applications.

This program leverages the physics and technology of optical stochastic cooling (OSC) to explore new possibilities in beam control and sensing.

This project aims to address the big-data challenges and stringent time constraints facing multi-messenger astronomy (MMA) in neutrino experiments and cosomological surveys.

his project develops real-time algorithms for event filtering with tracking detectors for nuclear physics collider experiments.

This project will develop AI-based tools to enable critical sectors for near-future cosmic applications.

This project will develop and use simulation-based inference to estimate cosmological parameters related to cosmic acceleration in the early and late universe - via the cosmic microwave background and strong gravitati...

This projects develops AI algorithms and tools for near-sensor data reduction in custom hardware.

accelerate scientific discovery

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.

Enhance application development lifecycle capabilities

Enhance internal communications and streamline Learning and Development

Capturing tenured employees experience based knowledge.

Manual helpdesk tickets consume staff time and delay issue resolution.

Improve day-to-day business functionality

Identify potential routes or evaluate existing corridors for carbon transport based on current legislation, best construction practices, and more.

Copilot Studio will help NNL build custom AI agents that automate repetitive tasks, answer common questions, and streamline workflows across departments, all without requiring deep coding expertise.

Automate code assignment at requisition generation time

Modern enterprise search among Microsoft 365 content, AI integration into daily business applications, improvemed management of email and messaging.

Make processes in Technology Deployment more effective and efficient

Aid safeguards analysts for determining if a reactor is being used in a non-declared way.

Enable rapid technology transfer of the ALTEMIS AI approaches to new sites and new systems through an automated data analysis and knowledge management toolkit

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 Glass...

With data from SWPF instrumentation, train neural networks to consider process parameters and chemical speciation data from incoming salt batches to predict filtration rate performance.

This deals with international safeguars approaches.