Web Articles

  • Scientists use machine learning to identify high-performing solar materials

Scientists use machine learning to identify high-performing solar materials

In a new paper featured on the cover of Advanced Energy Materials, an ALCF Data Science Program team, led by Jacqueline Cole of the University of Cambridge, details a novel “design to device” approach that allowed them to identify promising dye-sensitized solar cell (DSSC) materials for fabrication and testing. The multi-institution team, which included ALCF computational scientist Álvaro Vázquez-Mayagoitia, combined simulation, data mining, machine learning and experimental validation methods to pinpoint five high-performing, low-cost DSSC materials from a pool of more than 9,400 candidates.

March 04, 2019
  • Argonne and Convergent Science join forces for better engines

Argonne and Convergent Science join forces for better engines

The dynamics of an engine’s spark ignition are complex and often take months for scientists to simulate accurately. Now scientists at the U.S. Department of Energy’s (DOE) Argonne National Laboratory can study these dynamics much more quickly with the help of a new software model. Researchers at Argonne are incorporating this new model into a computational fluid dynamics software package used by industry to simulate the highly complex processes in internal combustion engines.

March 01, 2019

Argonne addresses obstacles to clean water for all

“There’s essentially nothing you can make without water,” notes Seth Darling.

February 13, 2019
  • This plot shows the number of events ATLAS events simulated (solid lines) with and without containerization. Linear scaling is shown (dotted lines) for reference.

Software stack in a snapshot

Scaling code for massively parallel architectures is a common challenge the scientific community faces.

February 04, 2019

Argonne Training Program on Extreme-Scale Computing scheduled for July 28-August 9, 2019

Computational scientists now have the opportunity to apply for the upcoming Argonne Training Program on Extreme-Scale Computing (ATPESC), to take place from July 28-August 9, 2019.

January 09, 2019
  • Improving engine modeling and simulation

Machine learning award powers Argonne leadership in engine design

Argonne researchers are partnering with Convergent Science and Parallel Works to use machine learning algorithms and artificial intelligence to optimize simulations to better capture the real-world behavior of combustion engines. Funded by a Technology Commercialization Fund (TCF) award from the DOE, the team will use ALCF supercomputing resources as part of this engine modeling research project.

January 02, 2019
  • Fully compressible Rayleigh-Taylor simulation

ALCF supercomputers enable groundbreaking research in 2018

The Argonne Leadership Computing Facility (ALCF), a U.S. Department of Energy (DOE) Office of Science User Facility, had another fruitful year. ALCF users employed complex simulations, data science techniques, and machine learning to accelerate discoveries across scientific disciplines. As 2019 approaches, we take a look back at some of the remarkable advances ALCF supercomputing resources enabled this past year.

December 20, 2018
  • Proteins designed on computer and tested in the lab look a lot like DNA

Scientists program proteins to pair exactly

This article was originally published by UW Medicine Newsroom.

December 19, 2018
  • Tangled magnetic fields power cosmic particle accelerators

Tangled magnetic fields power cosmic particle accelerators

SLAC researchers performed simulations on the ALCF's Mira supercomputer to suggest a new mechanism to explain how a black hole’s plasma jets boost particles to the highest energies observed in the universe. The results could also prove useful for fusion and accelerator research on Earth.

December 17, 2018
  • Convection models of the sun

Astronomical magnetism

An INCITE research team used the ALCF’s Mira supercomputer to explore the internal dynamics of the sun, Jupiter, and Earth at an unprecedented level of detail.

December 13, 2018