The diversity and function of organic matter in rivers at a large scale are influenced by factors, such as the types of vegetation covering the land, the energy characteristics, and the breakdown potential of the molecules.
Scientists at PNNL were awarded nearly $12 million to better understand pathogens, how they spread, and how to prepare the nation against future outbreaks.
Researchers from Pacific Northwest National Laboratory created and embedded a physics-informed deep neural network that can learn as it processes data.
PNNL-Sequim scientists will spend the next year testing a new technology that could allow the ocean to soak up more carbon dioxide without contributing to ocean acidification.
Bradley Crowell with the U.S. Nuclear Regulatory Commission sees advanced materials integrity, radiological measurement, and environmental capabilities on his first visit to PNNL.
Randomly constructed neural networks can learn how to represent light interacting with atmospheric aerosols accurately at a low computational cost and improve climate modeling capabilities.
A success story of applying convergence testing to detect and address issues of numerical discretization in nonlinear representations of turbulence and clouds.
A new open-source feature tracking package is now available to facilitate advanced model evaluation, model development efforts, and scientific discovery.
PNNL researchers demonstrated a simple method to create stable, identical nanoparticles of PdTe2-like composition, which is known to be superconducting, on a WTe2 TMD support.
