Artificial intelligence may help humans and computers work together to monitor materials that could be used to make weapons.

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.

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

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.

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.

A new approach broadens the reach of quantum computers to previously challenging systems.

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.

New PNNL research makes it easier to differentiate between chemical and nuclear explosions.

PNNL’s contributions over the last year span scientific discovery, sustainable energy and national security.

Manish Sharma, a physicist at PNNL, has been nominated to serve on the Radiation Protection and Shielding Division Executive Committee.

Researchers at PNNL leveraged their experiences to connect with attendees at the 2022 SACNAS National Diversity in STEM Conference, October 27–29, 2022, in San Juan, Puerto Rico.

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