Argonne Leadership Computing Facility

ALCF Resources
- Computing Resources
  
  Aurora
  
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  Facility Expertise
Leadership Computing Resources

The ALCF provides users with access to supercomputing resources that are significantly more powerful than systems typically used for open scientific research.

Featured: Aurora
Science and Engineering
- Output
  
  Projects
  
  Publications
  
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- Allocation Programs
  
  INCITE Program
  
  ALCC Program
  
  Director’s Discretionary
  
  Early Science Program
  
  NAIRR Program
Computational Science

The ALCF is accelerating scientific discoveries in many disciplines, ranging from chemistry and engineering to physics and materials science.

Featured: Engineering
Community and Outreach
- Partnerships
  
  Industry
  
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- Educational Outreach
  
  Women in STEM
  
  Student Programs
- Community
  
  NAIRR Pilot
  
  ALCF Lighthouse Initiative
  
  Exascale Computing Roundtable
Growing the HPC Community

The ALCF is committed to providing training and outreach opportunities that prepare researchers to efficiently use its leadership computing systems, while also cultivating a diverse and skilled HPC workforce for the future.
About
- Get to Know More
  
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Accelerating Science

The Argonne Leadership Computing Facility enables breakthroughs in science and engineering by providing supercomputing resources and expertise to the research community.
Support
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Support Center

The ALCF Support Center assists users with support requests related to their ALCF projects.

Help Desk
Hours: 9:00am-5:00pm CT M-F
Email: support@alcf.anl.gov
Guides
Featured: Get Started

Featured: MyALCF

Murali Krishna Emani

Person

Victor Anisimov

Person

Unit Commitment with Gas Network Awareness

Events 12/13/2019

Robust Optimization with Variable Uncertainty

Events 12/10/2019

Autotuning under Tight Budget Constraints: A Transparent Experimental Design Approach

Events 12/10/2019

Distributionally Robust Transmission Grid under Geomagnetic Disturbances

Events 12/12/2019

Leveraging Physical Models in Machine Learning

Events 12/12/2019

Aditya Tanikanti

Person

Kyle Felker

Person

Deep neural network learning of complex binary sorption equilibria from molecular simulation data

Publications Chemical Science

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Leadership Computing Resources

Featured: Aurora

Computational Science

Featured: Engineering

Growing the HPC Community

Accelerating Science

Support Center

Featured: Get Started

Featured: MyALCF

Murali Krishna Emani

Victor Anisimov

Unit Commitment with Gas Network Awareness

Robust Optimization with Variable Uncertainty

Autotuning under Tight Budget Constraints: A Transparent Experimental Design Approach

Distributionally Robust Transmission Grid under Geomagnetic Disturbances

Leveraging Physical Models in Machine Learning

Aditya Tanikanti

Kyle Felker

Deep neural network learning of complex binary sorption equilibria from molecular simulation data