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  • Molecular structure of the layered hybrid perovskite.

Supercomputer predicts optical and thermal properties of complex hybrid materials

This article was originally published by Duke University.

October 18, 2018
  • Big Data and Visualization Camp

The graphic nature of data

This summer, ALCF Director Michael Papka and several ALCF colleagues helped lead Argonne's first Big Data and Visualization Camp, which taught high school students how to gain insights from vast amounts of data.

October 16, 2018
  • Theta

Argonne to advance high-performance computing in manufacturing

The High Performance Computing for Manufacturing (HPC4Mfg) Program, operated by the U.S. Department of Energy’s (DOE) Advanced Manufacturing Office within the Office of Energy Efficiency and Renewable Energy (EERE), leverages world-class technical expertise with high performance computing to tackle manufacturing challenges uniquely solved by computer modeling. DOE’s Argonne National Laboratory and industry partners were recently awarded funding for four of the 13 projects under the program.

October 15, 2018

ALCF selects new data science projects

The Argonne Leadership Computing Facility (ALCF), a U.S. Department of Energy Office of Science User Facility, has selected four new projects to continue its ALCF Data Science Program (ADSP). These projects will utilize machine learning, deep learning, and other artificial intelligence (AI) methods to enable data-driven discoveries across scientific disciplines.

October 03, 2018
  • Turbulent envelope of a luminous blue variable star

Superstars’ secrets

Although massive stars are important to understanding astrophysics, the largest ones – at least 20 times the sun’s mass – are rare and highly variable. A team of astrophysicts is using ALCF computing resources to model the variability in three dimensions across an entire massive star. They’ve published the initial results from these large-scale simulations – linking brightness changes in massive stars with temperature fluctuations on their surfaces – in the Sept. 27 issue of the journal Nature.

October 01, 2018
  • Simulated data modeled for the ATLAS detector

Argonne team brings leadership computing to CERN’s Large Hadron Collider

A team of collaborators from the U.S. Department of Energy’s (DOE) Argonne National Laboratory is exploring the use of ALCF supercomputers to help meet the growing computing demands of the ATLAS experiment at CERN’s Large Hadron Collider. The team has used ALCF computing resources for multiple projects aimed at expediting the processing and simulation of data produced by the ATLAS experiment.

September 25, 2018

ALCF simulations shed light on technique to alter materials properties

Researchers from the University of Southern California are using ALCF computing resources to observe how changes in the atomic structure of a semiconductor material might convert it to metal. The ability to do so could drastically reduce the time and cost required to manufacture semiconductors—key components in most electronic devices—and extend their use to more novel applications, like flexible electronics.

September 12, 2018

Fine-tuning physics

Argonne physicists are using ALCF supercomputers to boost precision in particle predictions for CERN's Large Hadron Collider.

August 23, 2018
  • CodeGirls@Argonne camp

Teaching the programmers of tomorrow

The CodeGirls@Argonne camp is designed to immerse young girls in computer science before they enter high school and introduce them to potential career paths in science, technology, engineering, and mathematics (STEM). Staff members from the Argonne Leadership Computing Facility (ALCF) are among the volunteers who help the CodeGirls event fulfill its mission of bringing computer science to a population that’s often underrepresented in the field.

August 02, 2018

The high-tech evolution of scientific computing

To leverage emerging computing capabilities and prepare for future exascale systems, the Argonne Leadership Computing Facility, a DOE Office of Science User Facility, is expanding its scope beyond traditional simulation-based research to include data science and machine learning approaches.

July 30, 2018

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