Wendy Shaw is a new Fellow of the Royal Society of Chemistry, honored for her leadership role and impacts on the chemical sciences.

Six PNNL projects were selected as part of the Department of Energy’s Funding for Accelerated, Inclusive Research.

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

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.

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

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

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

The Rapid Assessment of Platform Technologies to Expedite Response project aims to prepare against future pandemics.

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

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.

The award was given at the 29th International Conference on Information, Communication and Automation Technologies.

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

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

Zhijie (Jay) Xu will apply his expertise in multiscale modeling to this editorial role.

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

PNNL researchers present challenges and opportunities for fusing graph neural networks with deep reinforcement learning.

ACMD staff contributed to 30 years of atmospheric data collection for the Department of Energy’s Atmospheric Radiation Measurement user facility.

IDREAM research shows that keeping only the most important two- and three-body terms in reactive force fields can decrease computational cost by one order of magnitude, while preserving satisfactory accuracy.