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Argonne Leadership Computing Facility
an Office of Science user facility
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Staving Off Global Climate Change with CO2 Capture

INCITE researcher, William Lester, Jr. will use 4 million core-hour award that may aid in the development of clean energy systems.

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Argonne and Jülich sign MOU

The MOU was signed to promote scientific and technological cooperation in the field of supercomputing, especially in exascale supercomputing

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INCITE Researchers Explore How Aircraft Contrails Can Impact Climate

Researchers at CERFACS and Laboratoire d'Aerologie received an INCITE award of 20 million hours at the ALCF to study contrail-to-cirrus transition.

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The ALCF is Getting Q’d Up: First Racks of Blue Gene/Q Arrive

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New Groundbreaking Global Climate Visualization Debuts at 92nd American Meteorological Society Meeting

NSF/DOE Community Atmosphere Model (CAM5) Total Precipitable Water – August through September

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In Focus

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Mission Mira: a photo library and latest updates on our Blue Gene/Q installation

Engineers are hard at work building the infrastructure that will support Mira. The first two racks of the new system—named Cetus and Vesta—will be used for science application development, debugging and system software development and testing.

Live Status

Science on IntrepidCoresCore Hours
Multiscale Blood Flow Simulations3276892333.5628125
Towards breakthroughs in protein structure calculation and design (INCITE); Computational Protein Structure and Protein Design32768138977.62752387
Towards breakthroughs in protein structure calculation and design (INCITE); Computational Protein Structure and Protein Design32768176599.47854601
Enabling Green Energy and Propulsion Systems via Direct Noise Computation1638432498.06531684

Featured Visualization

NSF/DOE Community Atmosphere Model(CAM5) Total Precipitatable Water – August through September
PI: Warren Washington

In the News

Molecules from Scratch without the Fiendish Physics
New Scientist
Ten million billion and counting
The Economist
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Physical Review Letters
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