Articles in the Science Category
Using the Titan supercomputer, researchers from Oak Ridge National Laboratory (ORNL) and the Oak Ridge Leadership Computing Facility (OLCF) collaborated with the University of California, Los Angeles (UCLA), Lawrence Berkeley National Laboratory (Berkeley Lab), and other institutions to simulate—for the first time—atomic-level magnetic properties in regions of a real nanoparticle based on experimental data. The results have been published in Nature.
As part of her team’s research into matter’s tendency to self-organize, Sharon Glotzer of the University of Michigan ran a series of hard particle simulations to study melting in two-dimensional (2-D) systems.
A team led by the California Institute of Technology’s (Caltech’s) Thomas Miller used the OLCF’s Titan to identify potential electrolyte materials and predict which ones could enhance the performance of lithium-ion batteries.
In an effort to study the complex fluid dynamics and chemical reactions occurring inside a jet engine combustor, researchers from United Technologies Research Center have teamed up with experts at the OLCF to develop more comprehensive modeling methods.
A research team led by Jefferson Lab’s Robert Edwards has been using computation to inform GlueX experiments at Jefferson Lab as well as corroborate experimental findings.
Using the Titan supercomputer, a team at Oak Ridge National Laboratory is developing automated data tools for cancer research by employing deep learning techniques.
In a project led by Barmak Mostofian, a CMB postdoctoral researcher, Jeremy Smith’s team created models of up to 330,000 atoms and ran simulations on Titan earlier this year that led to the discovery of a THF-water cosolvent phase separation on the faces of crystalline cellulose fiber.
A multi-institution team led by University of Illinois at Urbana-Champaign (UIUC) professor David Ceperley is using high-performance computing resources at OLCF to compare and corroborate experimental findings pertaining to a variety of novel materials.
In an effort to modernize computational fluid dynamics, a group of Imperial College researchers has developed a highly accurate and flexible code that utilizes GPU accelerators.
In April 2014, a team used its INCITE allocation to simulate galaxy formation over billions of years using one trillion particles in a simulation called ds14_a (ds stands for Dark Sky).