| Argonne Leadership Computing Facility

Argonne Leadership Computing Facility

ALCF Resources
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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
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Computational Science

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

Featured: Engineering
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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.
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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: [email protected]
Guides
Featured: Get Started

Featured: MyALCF

Trillion Parameter Consortium 2026

Events

40th IEEE International Parallel & Distributed Processing Symposium

Events

Machine-Learning Predictions of Photoluminescence in Molecules Exhibiting Thermally Activated Delayed Fluorescence with Implicit Experimental Validation

Publications Machine Learning and Deep Learning

Energy Exascale Earth System Model

Research Project Earth Science

Regionally refined E3SM model run at sub-kilometer resolution

DAOS Deep Dive

Events

Separable Character of Ab Initio No-Core Shell Model One-Body Densities

Publications Physical Review C

Quantum-Informed Machine Learning for Predicting Spatiotemporal Chaos with Practical Quantum Advantage

Publications Science Advances

Building SOTA Foundation Models for Precision and Preventive Medicine

Research Project Biological Sciences

COMbining Deep-Learning with Physics-Based AffinIty EstimatiOn 4 (COMPBIO4)”

Research Project Biological Sciences

The IMPECCABLE workflow integrates machine learning (ML) and physics-based (PB) methods to accelerate drug discovery. By incorporating multiple ML and PB approaches, it leverages their strengths while mitigating individual limitations. The workflow is being scaled on exascale machines to efficiently identify potential drug candidates for various target proteins.

FRAME-IDP: Framework for Reasoning Analytics & Modeling of Emergent biologics targeting Intrinsically Disordered Proteins

Research Project Biological Sciences

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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

Trillion Parameter Consortium 2026

40th IEEE International Parallel & Distributed Processing Symposium

Machine-Learning Predictions of Photoluminescence in Molecules Exhibiting Thermally Activated Delayed Fluorescence with Implicit Experimental Validation

Energy Exascale Earth System Model

DAOS Deep Dive

Separable Character of Ab Initio No-Core Shell Model One-Body Densities

Quantum-Informed Machine Learning for Predicting Spatiotemporal Chaos with Practical Quantum Advantage

Building SOTA Foundation Models for Precision and Preventive Medicine

COMbining Deep-Learning with Physics-Based AffinIty EstimatiOn 4 (COMPBIO4)”

FRAME-IDP: Framework for Reasoning Analytics & Modeling of Emergent biologics targeting Intrinsically Disordered Proteins