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Exploring the High-Pressure Materials Genome

Researchers at Northwestern University developed a novel computational framework that runs on NERSC’s Cori supercomputer to rapidly explore apparent paradox of material phases that exist in nature that should be unstable according to calculations that assume idealized conditions of zero pressure and zero temperature. Using this method, the stability of these material phases is better understood, and the team has identified several new intermetallic high-pressure phases that can be realized with existing experimental techniques. Read More »

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DOE Models Simulate Antarctic Ice Sheet Evolution

Ice sheet models developed under a DOE SciDAC project and run at NERSC have contributed to three international model intercomparison projects focused on assessing the future evolution of the Antarctic ice sheet. Read More »

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ATLAS Experiment: Scaling High Throughput Workflows

Researchers from the ATLAS experiment at the Large Hadron Collider (LHC ) used NERSC’s Cori supercomputer to simulate over 250 million proton collisions in support of the experiment’s data analysis efforts. This campaign also paves the way for even larger analysis that will be needed following a 2025 LHC upgrade. Read More »

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Amazon Soil Moisture Impact on Carbon Dioxide Emissions

Soil moisture variability intensifies and prolongs eastern Amazon temperature and carbon cycle response to El Niño-Southern Oscillation The Science During El Niño events, atmospheric teleconnections with sea surface temperature (SST) anomalies in the equatorial Pacific cause higher temperatures and reduced rainfall in the Amazon, leading to increased CO2​ emissions. While some of the temperature increase results directly from the SST-atmosphere teleconnection, drier soil resulting from… Read More »

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Machine-Learned Impurity Prediction in Semiconductors

Argonne National Laboratory researchers ran high-throughput simulations on NERSC supercomputers and generated comprehensive datasets of impurity properties in two classes of semiconductors: lead-based hybrid perovskites and cadmium-based chalcogenides. These datasets led to machine learned models that enable accelerated prediction and design for the entire chemical space of materials and impurities in these semiconductor classes. Read More »