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.

In 2006, battery research was practically non-existent at PNNL. Today, the lab is lauded for its battery research. How did PNNL go from a new player to a leader in state-of-the-art storage for EVs and the grid?

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

Scientists stop the motion of atoms to watch electrons move in liquid water.

The convergence of artificial intelligence, cloud, and high-performance computing to accelerate scientific discovery is the focus of a multi-year collaboration between Microsoft and PNNL.

The U.S. Nuclear Regulatory Commission approved the construction permit for Kairos Power’s Hermes molten salt test reactor project in Tennessee.

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

PNNL has created the Center for AI @PNNL to coordinate the pioneering research of hundreds of scientists working on a range of projects in artificial intelligence.

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

PNNL has joined Gender Champions in Nuclear Policy, a leadership network that brings together leaders of organizations working in nuclear policy who are committed to breaking down gender barriers.

Resolving how nanoparticles come together is important for industry and environmental remediation. New work predicts nanoparticle aggregation behavior across a wide range of scales for the first time.

Input from humans helps when deciding whether to trust the recommendations or decisions of a machine-learning system.

PNNL’s DataLife tool analyzes and identifies bottlenecks in scientific workflows.

The use of disciplines in pure mathematics can increase the reliability and explainability of machine learning models that “transcend human intuition,” according to PNNL scientists.

NRC Chair Christopher Hanson tours the nondestructive examination laboratories and learns more about PNNL’s broad support to the NRC mission.

A poem inspired by radioactive tank waste—“Can a Scientist Dream it Alone?”—was awarded first place in the Department of Energy’s Poetry of Science Art Contest.

Pasco School District teachers join PNNL for an immersive teaching experience.

Researchers unveil user-friendly software to keep complex projects on budget and on schedule.