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.
Policy changes in power, energy, buildings, and more could help slow global temperature rise, according to a new report with co-authors from PNNL’s Joint Global Change Research Institute.
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.
Data scientist at PNNL receives the Environmental and Engineering Geophysical Society and Geonics Limited Early Career Award for work with geophysical modeling and subsurface inversion codes.
Study explores Exploration of Coastal Hydrobiogeochemistry Across a Network of Gradients and Experiments, a consortium of scientists interested in the exchange between water and land in coastal systems.
This study demonstrates a new model that integrates complex organic matter (OM) chemistry and multiple electron acceptors to predict kinetic rates of OM oxidation.
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.
