Scientists have taken a key step toward harnessing a form of AI known as deep reinforcement learning to protect computer networks.

The work by the team at PNNL takes a critical step in leveraging ML to accelerate advanced manufacturing R&D, specifically for manufacturing techniques without access to efficient, first-principles simulations.

Radha Kishan Motkuri lends skill and knowledge to co-lead special edition journal.

Harnessing the sea for decarbonized energy is the focus of a new collaboration between PNNL and the University of Guam.

PNNL mathematician Aaron Luttman contributed to the organizing committee for workshop exploring robust machine learning and artificial intelligence systems for the U.S. Army.

Researchers developed a machine learning model that can analyze chemical reactions as they happen in an electron microscope.

PNNL scientists Daniel Gaspar and Wei Wang were among nine scientists featured in journal article recognizing 2021-2022 ACS award honorees.

Research published in Journal of Manufacturing Processes demonstrates innovative single-step method to manufacture oxide dispersion strengthened copper materials from powder.

Steven Spurgeon’s research is featured in the cover of the MRS Bulletin along with his team’s invited perspective on the future of machine learning for electron and scanning probe microscopy.

To thwart pathogens, researchers in the epidemiology field of infectious disease (ID) prediction are continuously trying to forecast when, where, and how an ID event will occur.

A PNNL team developed and used a model framework to understand the performance and structural reliability of a state-of-the-art solid oxide electrolysis cell design.

As leaders in AI and machine learning, PNNL experts are sharing their latest findings at the 36th annual Neural Information Processing Systems (NeurIPS) Conference, Nov. 28–Dec. 9, 2022.

Multidisciplinary teams from research and industry are brought together using management systems developed by a team of software engineers at PNNL.

Tailoring hydrogen-based energy storage solutions to specific real-world applications.

Direct visualization of metal atoms during shear deformation has broad applications from battery design to vehicle lightweighting.

PNNL researchers use machine learning and data analytics to assist with detection of nuclear proliferation and nuclear material trafficking.

PNNL joint appointee Auroop Ganguly uses data science to study the impacts of climate change.

A new web-based tool provides easy-to-understand progress metrics and other data about groundwater cleanup sites overseen by the DOE Office of Environmental Management.

Scientists are pioneering approaches in the branch of artificial intelligence known as machine learning to design and train computer software programs that guide the development of new manufacturing processes.

Lauren Charles joins the CABI One Health Editorial Board as an associate editor.