Harnessing machine learning to enhance plant coexistence in a vegetation demographic model.

Glenn Fugate was selected to serve on the American Chemical Society's Science Committee.

From Mexico and Ethiopia to a U.S. national lab, interns are ready to contribute to a sustainable energy future.

Corinne Fuller has been named the new co-director of the Bioproducts Institute, a research collaboration between Washington State University and PNNL, as of July 2023.

PNNL’s Grid Storage Launchpad delivers tomorrow’s energy storage solutions today.

Researchers from Pacific Northwest National Laboratory created and embedded a physics-informed deep neural network that can learn as it processes data.

The Department of Energy’s Vehicle Technologies Office recently issued two awards to researchers at PNNL for their contributions to areas that are crucial for the expansion of electric vehicles.

Summer is for science! PNNL’s interns are diving into science and technology and getting a front-row view of the research and development of a national laboratory.

David Barajas-Solano and Aaron Luttman worked with students to create models to evaluate remediation strategies at the Hanford Site.

Different sources of low-level moisture perturbation can result in differences in the timing and location of a large, modeled storm.

At the Nonproliferation, Counterproliferation, and Disarmament Science Gordon Research Conference, researchers from PNNL shared research and scientific approaches for countering diverse threats.

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.

Regional wind data helps utilities save money, plan for windy days.

PNNL energy storage experts express need for continued investment in developing and deploying long-duration energy storage.

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 holistic evaluation of the uncertainty sources that arise from configuring the model to processing the output for global variable-resolution models.

Researchers found a 150-day period in Antarctic circulation, something that many current climate models do not represent accurately.

Four PNNL researchers received highly competitive DOE Early Career Research Program awards, providing five continuous years of funding for their projects.

A new open-source feature tracking package is now available to facilitate advanced model evaluation, model development efforts, and scientific discovery.