PNNL shared recent developments in artificial intelligence at the inaugural Special Competitive Studies Project AI for National Competitiveness Expo.

Researchers are leveraging the ocean’s natural processes to advance approaches to remove carbon dioxide from the atmosphere and store it in the ocean.

Research at PNNL and the University of Texas at El Paso are addressing computational challenges of thinking beyond the list and developing bioagent-agnostic signatures to assess threats.

PNNL computing experts Robert Rallo and Court Corley contribute their knowledge to a recent DOE report on applications of AI to energy, materials, and the power grid.

A breakthrough in electron microscopy based on deep learning can automatically visualize and identify areas of interest, helping to speed advances in materials science.

A set of papers combines surface science measurements on tiny particles with uncrewed aerial systems to better understand and model aerosols.

Data reveals mechanisms in our bodies that can help us fight viral pathogenesis.

New apps help researchers with statistical analyses of data.

Researchers seek to bring down costs, address potential environmental risks and maximize the benefits of harnessing wind energy above the deep waters of the Pacific.

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

PNNL’s Climate Network with MSIs and HBCUs aims to bring opportunities to diverse populations.

PNNL study explored multiple factors that could impact future snowpack—a critical source of water for communities, irrigation and energy generation.

Earth Scientist Laura Fierce mentors Department of Energy Science Graduate Student Research program awardees.

From energy storage to cybersecurity, this 2023 wrap-up offers a glimpse of PNNL innovations with a seasonal twist.

An initiative from Washington State University and Snohomish County leaders is aiming to make Paine Field a nexus for testing and improving sustainable aviation fuels made from non-petroleum materials.

As electricity grids incorporate renewables, several factors will constrain future output, according to a new paper.

PNNL researchers comment on the “Adversarial Machine Learning” white paper from the National Institute of Standards and Technology.

Scientists at PNNL have published a new article that focuses on understanding the composition, dynamics, and deployment of beneficial soil microbiomes to get the most out of soil.

PNNL is one of the founding partners of the Trillion Parameter Consortium, which was formed to create reliable generative AI models.

Researchers use models to represent relationships between climate and socio-economic processes, helping inform decisions for slowing climate change and enhancing resilience.