Center Leverages PNNL Science and Technology for Chemical Security
PNNL is supporting the Department of Homeland Security Science and Technology Directorate's Chemical Security Analysis Center in improving capabilities to enhance detection and analysis of chemical threats.
Drgoňa Featured at the Frontiers of Engineering Symposium
Ján Drgoňa invited as a speaker at the Grainger Foundation Frontiers of Engineering 2024 Symposium of the National Academy of Engineering.
AI Ups Response Time when the Grid Goes Down
Trouble on the electric grid might start with something relatively small: a downed power line, or a lightning strike at a substation. What happens next?
PNNL, Sandia, and Georgia Tech Join Forces in AI Effort
Three powerhouses in the realm of artificial intelligence have become partners in a new research center created by the U.S. Department of Energy.
Teams of Rivals: PNNL and LAS Collaborate on Machine Learning
Twenty-four analysts from U.S. intelligence organizations met in August for a machine learning activity with PNNL researchers Nicole Nichols, Jeremiah Rounds, Lawrence Phillips, and Brian Kritzstein.
Using Deep Learning as a Searchlight for Dark Matter
Scientists at PNNL are bringing artificial intelligence into the quest to see whether computers can help humans sift through a sea of experimental data.
PNNL Garners R&D 100 Awards
The U.S. Department of Energy’s Pacific Northwest National Laboratory (PNNL) is the recipient of two R&D 100 awards and one gold medal.
New AI Model Tries to Synthesize Patient Data Like Doctors Do
A new approach developed by PNNL scientists improves the accuracy of patient diagnosis up to 20 percent when compared to other embedding approaches.
Science for the Front Line: Svitlana Volkova
PNNL is highlighting scientific and technical experts in the national security domain who were recently promoted to scientist and engineer Level 5, one of PNNL’s most senior research roles.
3D-Scaffold, a Deep Learning Approach to Identify Novel Molecules for Therapeutics
A multi-institutional team has developed a deep learning framework to identify novel molecules as drug candidates.