Panelists Explore AI for Emergency Management
A series of conference panels recently explored how new and evolving technologies can support emergency managers’ responsibilities.
The Youth Are Our Future: Mentor Mariefel Olarte Invests in Interns
PNNL chemical engineer Mariefel Olarte shares passion for mentoring students and the next-generation workforce.
Novel Computing Tool Learns the Language of Chemistry
ChemReasoner combines decades of chemistry knowledge with large language models to propose strategies for effective new catalysts.
Identifying the Dark Matter of the Molecular World
Artificial intelligence helps researchers identify metabolites, the small molecules that underlie life.
Solving Scientific Problems with Institutional Collaborations
Brown University Applied Mathematics and Engineering Professor George Karniadakis has driven solutions for science and engineering problems for over ten years with a joint appointment at PNNL.
Tracking the Neural Network’s Nuclear Clues
A 2011 earthquake and tsunami in Japan that knocked out a nuclear power plant helped inspire PNNL computational scientists looking for clues of future nuclear reactor mishaps by tracking radioactive iodine.
The Center for AI @ PNNL Helps Keep U.S. at Forefront of AI for Science, Energy, and Security
Scientists at PNNL harnessing advances in deep learning, deep reinforcement learning and generative AI to change how science is conducted and achieve original scientific results and breakthroughs.
PNNL’s Wendy Shaw Named Associate Lab Director, Physical and Computational Sciences
Chemist Wendy Shaw, a nationally recognized scientific leader, has been chosen to serve as the associate laboratory director for PNNL's Physical and Computational Sciences Directorate.
Adventures of Women in Science
At the center of many research teams are women — scientists and engineers who chase mystery and replace it with discoveries.
Physics-Informed Machine Learning for Energy and Environment
Physics-informed machine learning (PIML) is a modeling approach that harnesses the power of machine learning and big data to improve the understanding of coupled, dynamic systems.