Web Articles

sort descending

Lithium ions flow through solid material

A research team from Argonne, Purdue University and Rutgers University, have merged materials science and condensed matter physics in a study of samarium nickelate, a promising material that conducts lithium ions. The transport of ions, or charged atoms, through materials plays a crucial role in many electrical systems — from batteries to brains. As part of the study, the team used the ALCF’s Mira supercomputer to model the dynamics of the system to predict what pathways the lithium ions could take through nickelate.

March 20, 2019
  • Aurora exascale supercomputer

U.S. Department of Energy and Intel to deliver first exascale supercomputer

Targeted for 2021 delivery, Argonne National Laboratory's next-generation supercomputer will enable high-performance computing and artificial intelligence at exascale.

March 18, 2019
  • Jini Ramprakash

Opportunities abound for ascending women scientists

Women have always been critical to the core mission of the Department of Energy’s (DOE) Argonne National Laboratory. But never before have they occupied as many leadership positions as they do today. From the environmental sciences to supercomputers, they are shaping high-profile and innovative research across the laboratory.

March 12, 2019
  • Caterpillar-Argonne partnership

Caterpillar-Argonne team to pursue improved diesel engine combustion systems

Caterpillar Inc. and the U.S. Department of Energy’s (DOE) Argonne National Laboratory are joining forces to research heavy-duty diesel engines. This project is funded by the Department’s Advanced Manufacturing Office (AMO) and Vehicle Technologies Office (VTO) and is one of seven public-private partnerships recently selected under the DOE’s High Performance Computing for Manufacturing (HPC4Mfg) program.

March 08, 2019
  • 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