Slaven Peles, PNNL computational scientist and leader of a national high-performance computing project for power grid analysis, spoke about the project with the host of the Let’s Talk Exascale podcast.
A research project that brings together mathematicians and atmospheric scientists has developed into a deep collaboration for improving atmospheric models.
With quantum chemistry, researchers led by PNNL computational scientist Simone Raugei are discovering how enzymes such as nitrogenase serve as natural catalysts that efficiently break apart molecular bonds to control energy and matter.
A team of researchers from 10 national laboratories and eight universities is conducting hydraulic shearing tests to explore the potential for geothermal energy at the Sanford Underground Research Facility (SURF).
PNNL highlights four researchers whose joint appointments are creating new and diverse opportunities for expanding knowledge and scientific impact across institutions.
A research team from Pacific Northwest National Laboratory developed an apparatus that evaluates the performance of high-temperature fluids in hydraulic fracturing for enhanced geothermal systems.
Using public data from the entire 1,500-square-mile Los Angeles metropolitan area, PNNL researchers reduced the time needed to create a traffic congestion model by an order of magnitude, from hours to minutes.
PNNL researchers have shown an improved binarized neural network can deliver a low-cost and low-energy computation to help the performance of smart devices and the power grid.
Pacific Northwest National Laboratory researchers developed a graphical processing unit (GPU)-centered quantum computer simulator that can be 10 times faster than any other quantum computer simulator.
PNNL’s new Smart Power Grid Simulator, or Smart-PGSim, combines high-performance computing and artificial intelligence to optimize power grid simulations without sacrificing accuracy.
The MIT-sponsored competition encourages community approaches to developing new solutions for analyzing graphs and sparse data; PNNL has placed a winner in each year.
Tracking down nefarious users is just one example of work at PNNL’s Center for Advanced Technology Evaluation, a computing proving ground supported by DOE’s Advanced Scientific Computing Research program.
Pacific Northwest National Laboratory researchers used machine learning to explore the largest water clusters database, identifying—with the most accurate neural network—important information about this life-essential molecule.
Five PNNL technologies were recently awarded six R&D 100 honors. The R&D 100 Awards, now in its 58th year, recognize pioneers in science and technology from industry, the federal government, and academia.
