Artificial Intelligence (AI)


Image courtesy of Oak Ridge Leadership Computing Facility

Researchers used supercomputing and deep learning tools to predict protein structure, which has eluded experimental methods such as crystallography.

 

Advanced Scientific Computing Research (ASCR) supports research in many Artificial Intelligence (AI) areas, including: scientific machine learning (SciML) for complex systems; generative AI, large language models (LLMs), and foundation models; interpretable and explainable AI; privacy-preserving AI and federated learning; AI for visualization; and co-designed hardware and neuromorphic computing to accelerate scientific discovery.

These investments have the potential to transform science and energy research by harnessing DOE investments in massive data from scientific user facilities, software for predictive models and algorithms, high-performance computing platforms, and the national workforce.

ASCR has held several workshops on AI. Some of the priority research directions include:

  • Domain-Aware Scientific Machine Learning
  • Interpretable Scientific Machine Learning.
  • Robust Scientific Machine Learning
  • Data-Intensive Scientific Machine Learning.
  • Machine Learning-Enhanced Modeling and Simulation
  • Intelligent Automation and Decision Support.

By supporting such research directions, ASCR hopes to make advances that accelerate scientific discovery.

ASCR Funding

Award abstracts and information about awards made prior to FY2018 can be found here.

ASCR Workshops and Reports

Press Releases

Other Notable Reports


Artificial Intelligence (AI) Program Manager Contacts:

Steven Lee
Privacy-Preserving AI
Steven.Lee@science.doe.gov

Margaret Lentz
Data and Visualization
Margaret.Lentz@science.doe.gov

Kalyan Perumalla
AI Systems
Kalyan.Perumalla@science.doe.gov

Robinson Pino
Neuromorphic Computing
Robinson.Pino@science.doe.gov

Bill Spotz
AI for Complex Systems
William.Spotz@science.doe.gov