Alumni Receive INCITE Computing Allocations

Ames, IA

Three Department of Energy Computational Science Graduate Fellowship (DOE CSGF) alumni will use DOE supercomputers for their science and engineering research, thanks to 2023 allocations from the department’s INCITE (Innovative and Novel Computational Impact on Theory and Experiment) program.

Matthew Norman and researchers from Oak Ridge, Sandia, Lawrence Livermore, Los Alamos, Pacific Northwest and Argonne national laboratories will use 450,000 node-hours on Oak Ridge’s Summit supercomputer to develop a next-generation Earth system model tailored to machines at the DOE’s Leadership Computing Facilities. The Energy Exascale Earth Systems Model, or E3SM, will investigate the climate’s sensitivity to elevated greenhouse gases.

Amanda Randles, a Duke University biomedical sciences professor, is principal investigator for a project using 800,000 Summit node-hours to create digital models of microfluidic devices designed to measure blood cell properties. The team’s models will help capture cellular-scale resolution over large areas and enable “digital twins” for microvascular and microfluidic applications.

Brenda Rubenstein and a team of university and national laboratory researchers will develop exascale simulations of quantum materials, aiming to help reduce energy use, realize new technologies and identify optimum compounds for new applications such as sensors and low-power electronics. With Oak Ridge leading the project, the team will tap 100,000 node-hours on Argonne’s Polaris supercomputer, using quantum Monte Carlo methods to predict material properties, revealing underlying trends and serving more reliably as a reference for the theoretical quantum materials community.

Norman, a fellow from 2008 to 2011 and is a computational climate scientist at Oak Ridge. Randles was a fellow from 2010 to 2013. Rubenstein, an assistant professor of chemistry at Brown University, was a fellow from 2008 to 2012.

See the complete list of award-winning projects here.

Randles image credit: Duke University

Rubenstein image credit: Brown University