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) basic research in Scientific Machine Learning – a core part of Artificial Intelligence and computational technology to augment or automate human skill. Scientific Machine Learning (SciML) has 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. In a January 2018 Basic Needs Workshop, six Priority Research Directions for SciML were identified:

• 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.

Press Releases and Award Lists

• Department of Energy Invests $1 Million in Artificial Intelligence Research for Privacy-Sensitive Datasets (September 2021)
• DOE Announces $29 Million for Ultramodern Data Analysis Tools (March 2021)
• U.S. Department of Energy to Provide $16 Million for Machine Learning and Artificial Intelligence Research
• Department of Energy Announces $8.5 Million for FAIR Data to Advance Artificial Intelligence for Science 
• The White House Office of Technology Policy, National Science Foundation and Department of Energy announce over $1 billion in awards for Artificial Intelligence Institutes and Quantum Information Science Research Centers.(August 2020)
• Department of Energy Announces $13 Million for Artificial Intelligence Research (October 2019)

ASCR Funding Opportunity Announcements & Awards Lists

• Bridge2AI and Privacy Preventing Artificial Intelligence Research
• Data-Intensive Scientific Machine Learning and Analysis
• Scientific Machine Learning for Modeling and Simulation
• Artificial Intelligence and Decision Support for Complex Systems
• Scientific Machine Learning and Artificial Intelligence: Uncertainty Quantification
• Artificial Intelligence, Machine Learning, and Data Analytics Co-Design
• FAIR Data and Models for Artificial Intelligence and Machine Learning

Past Funding Opportunities

• Bridge2AI and Privacy Preserving Artificial Intelligence Research
• Data-Intensive Scientific Machine Learning and Analysis
• Randomized Algorithms for Extreme-Scale Science
• Scientific Machine Learning for Modeling and Simulation
• Artificial Intelligence and Decision Support for Complex Systems
• FAIR Data and Models for Artificial Intelligence and Machine Learning
• Scientific Machine Learning and Artificial Intelligence: Uncertainty Quantification
• Artificial Intelligence, Machine Learning, and Data Analytics Co-Design

ASCR Workshops & Reports

• Opportunities and Challenges from Artificial Intelligence and Machine Learning for the Advancement of Science, Technology, and the Office of Science Missions (September 2020)
• AI for Science: Report on the Department of Energy Town Halls on Artificial Intelligence for Science (February 2020)
• Data and Models: A Framework for Advancing AI in Science(December 2019)
• Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence(January 2019)
• Machine Learning and Understanding for Scientific Discovery(January 2015)

Contacts

Steven Lee
Program Manager in Applied Mathematics
Basic Math, Algorithms, Models and Data
steven.lee@science.doe.gov