Research at PNNL and the University of Texas at El Paso are addressing computational challenges of thinking beyond the list and developing bioagent-agnostic signatures to assess threats.
PNNL computing experts Robert Rallo and Court Corley contribute their knowledge to a recent DOE report on applications of AI to energy, materials, and the power grid.
A breakthrough in electron microscopy based on deep learning can automatically visualize and identify areas of interest, helping to speed advances in materials science.
The next-generation ShAPE machine has arrived at PNNL, where it will help prove the mettle of the ShAPE extrusion technique. ShAPE 2 is designed to allow researchers to produce larger, more complex extrusions.
The SHASTA program is doing a deep dive on subsurface hydrogen storage in underground caverns, helping to lay the foundation for a robust hydrogen economy.
In 2006, battery research was practically non-existent at PNNL. Today, the lab is lauded for its battery research. How did PNNL go from a new player to a leader in state-of-the-art storage for EVs and the grid?
PNNL chief scientist and joint appointee Auroop Ganguly was recently appointed a Distinguished Member of the Association for Computing Machinery, a high honor from the world's largest computer science society.
Identifying how curvature affects the doping and hydrogen binding energies of carbon-based materials provides a framework for designing hydrogen storage materials.
Visual Sample Plan, a free software tool developed at PNNL that boosts statistics-based planning, has been recognized with a 2024 Federal Laboratory Consortium Award.
Brett Jefferson, data scientist, was recently recognized for his determination and success in his research space with an Early Career Award from Indiana University Bloomington in the Psychological and Brain Sciences Department.
The convergence of artificial intelligence, cloud, and high-performance computing to accelerate scientific discovery is the focus of a multi-year collaboration between Microsoft and PNNL.