A new study uses direct numerical simulations to develop a near-surface turbulence model for thermal convection using interpretable and physics-aware neural networks, broadening the applications of numerical simulations.

PNNL scientists have put forth a new approach to protect the electric grid, creating an approach that sorts and prioritizes cyber threats on the fly.

A PNNL study developed a water management module for Xanthos that distinguishes between the operational characteristics of hydropower, irrigation, and flood control reservoirs.

The Earth System Model Aerosol–Cloud Diagnostics package version 2 uses aircraft, ship, ground, and satellite measurements to evaluate detailed physical processes in aerosols, clouds, and aerosol–cloud interactions.

ChatGrid™ is a practical application of the Department of Energy’s exascale computing efforts and offers a new experience in easy, intuitive, and interactive data interaction.

The physics-informed machine learning optimization framework is now PNNL’s most popular open-source repository on GitHub.

PNNL researchers use chemometric analysis to model chemistry in molten salt.

With the help of the U.S. State Department, PNNL climate scientists assisted counterparts in Malaysia and Thailand in exploring carbon neutral goals.

Ang participated in a White House-hosted CHIPS R&D event and roundtable discussion with senior leaders from industry, academia and key government agencies.

PNNL helps deliver efficiency-related rules and requirements that steadily improve performance of America’s buildings, saving energy and costs and reducing carbon emissions.

Results of the PR100 study outline near- and short-term strategies for Puerto Rico to achieve a renewable power grid by 2050.

Pacific Northwest may face increase in summer heat-dome-like stationary waves.

Two PNNL professionals have been selected to join the 2024-26 New Voices cohort at the National Academies of Sciences, Engineering, and Medicine.

Identifying how curvature affects the doping and hydrogen binding energies of carbon-based materials provides a framework for designing hydrogen storage materials.

A paper co-authored by four PNNL-affiliated scientists has been awarded the 2023 UCAR Outstanding Accomplishment Award for Publication.

Machine learning methods and data science can uncover hidden patterns in nanoparticle synthesis conditions and property outcomes.

A 19-person, multi-institutional national laboratory team received the inaugural Gordon Bell Prize for Climate Modeling from the Association for Computing Machinery for their work on more accurately modeling deep convective clouds.

New research shows how cloud shapes affect the process of cloud evolution, resulting in better understanding of how clouds behave, improving weather forecasts, and enhancing comprehension of climate systems.

At the GovAI Summit 2023, Ryan Eddy joined the Department of Homeland Security Science and Technology Directorate on a panel highlighting artificial intelligence impacts and opportunities in the field of emergency management.