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.

DOE Basic Energy Sciences 2023 “Poetry of Science Art Contest” aims to educate and inspire; voting for the People’s Choice Award closes September 15.

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.

Venky Prabhakaran joined the editorial board of Scientific Reports, an open-access journal in the Nature portfolio.

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.

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

A new review describes the insights that may be gained into important electrochemical processes using ion soft landing.

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.

A crystalline silicate framework forms through a non-classical, multi-step nucleation mechanism based on structurally precise clusters.

Chun-Long Chen honored for his outstanding contributions in the field of bio-inspired nanomaterials.

Two teams were recognized for technologies to help the International Atomic Energy Agency verify peaceful use of nuclear material.

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.