PNNL’s data-infused approach to electron microscopes’ use in scientific experimentation will help researchers and industry interpret large data streams and drive down costs.
The U.S. Department of Energy has selected the Scalable Predictive Methods for Excitations and Correlated Phenomena project to receive funding to develop software for chemical research.
The Washington State Academy of Sciences consists of more than 300 elected members who are nationally recognized for their scientific and technical expertise.
Bojana Ginovska leads a physical biosciences research team headed for PNNL's new Energy Sciences Center. She uses the transformative power of molecular catalysis and enzymes to explore scientific principles.
PNNL computational scientist Diana Bacon’s role as carbon storage associate editor uses her expertise in subsurface modeling and quantitative risk assessment.
PNNL combines AI and cloud computing with damage assessment tool to predict path of wildfires and quickly evaluate the impact of natural disasters, giving first responders an upper hand.
Machine learning techniques are accelerating the development of stronger alloys for power plants, which will yield efficiency, cost, and decarbonization benefits.
A research project that brings together mathematicians and atmospheric scientists has developed into a deep collaboration for improving atmospheric models.
Svitlana Volkova, chief scientist for decision intelligence and analytics at PNNL, was invited as a panelist at the SIAM International Conference on Data Mining
The DOE Early Career Research Program supports exceptional researchers during the crucial early years of their careers and helps advance scientific discovery in fundamental sciences
