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


ASCR Funding Opportunity Announcements & Awards Lists


Past Funding Opportunities


ASCR Workshops & Reports



Steven Lee
Program Manager in Applied Mathematics
Basic Math, Algorithms, Models and Data