Machine learning models help identify important environmental properties that influence how often extreme rain events occur with critical intensity and duration.

PNNL researchers combined deep operator networks with spectral methods to efficiently and accurately solve complex equations.

Data-driven autonomous technology to rapidly design and deliver antiviral interventions targeting SARS-CoV-2 to reduce drug discovery timeline and advance bio preparedness capabilities.

The Building and Environment journal named Drgoňa’s co-authored work one of 2022’s best papers.

Data scientist Jung Lee accepted the position of review editor for Frontiers in Computational Neuroscience after serving as a guest editor for a special issue.

Kathleen Doty recently shared legal insight on the challenge of space debris in her presentation “Regulating Space Junk” as part of the University of Georgia School of Law’s Spring 2023 Space Law Speaker Series.

Karen Taylor was an invited speaker at the Closing the Knowledge Gaps event and spoke during a panel, titled Bioeconomy Workforce Demands.

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

PNNL physicist in the Signature Sciences and Technology Division was officially announced as a member of the FRIB User Organization Executive Committee.

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.

PNNL research analysist specializing in biological and chemical weapons nonproliferation invited to join editorial board for Frontiers in Microbiology.

PNNL is the newest member of the CONsortium on Nuclear sECurity Technologies (CONNECT).

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.

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.

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.

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

Kate Doty was invited to lend her expertise as guest editor for the International Journal of Nuclear Security on a special issue on women in nuclear security.

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.

In honor of National Intern Day, PNNL and intern Jonathan Mills were named among the Top 100 in the nation by early-career recruiting firm WayUp.