A decade-long evolution in making omics data easier to analyze earns widespread recognition.

Research provided definitive spectroscopic characterization, reactivity, and deactivation of a reactive monomeric diphosphine-copper hydride intermediate.

Developed a method for selectively anchoring diverse divalent metal cations in zeolites, important for precise control over catalytic materials.

Developed a simple kinetic framework for evaluating catalyst hydride transfer ability.

PNNL has formed a partnership with the Orlando-based firm Refinity, which has licensed a key process from PNNL that will help the company convert mixed plastic waste into valuable industrial products like chemicals, plastics, and fuels.

The Department of Energy’s Transportation Technologies Office has issued two 2026 Annual Merit Review Awards to researchers from PNNL. Both awards recognize PNNL researchers whose work is reducing the need for critical materials.

PNNL’s Robert Rallo and Nathan Hodas speak to students about the future of AI.

PNNL cyber defenders are using the Laboratory’s own artificial intelligence research to recognize and address threats quickly.

Emilie Purvine, a chief data scientist at PNNL, was elected as a council member at large of the American Mathematical Society.

Yong Wang will lead the Institute for Integrated Catalysis, advancing the science and technology of catalysis to address global challenges in energy resilience.

A low-temperature, single-stage catalytic process converts PVC and polyolefin into valuable alkanes.

An award-winning computing method called poly-streaming accelerates the analysis of massive datasets while using limited memory.

A comprehensive investigation provides quantitative data on the interaction between zeolite pores and linear alcohols, with hydroxyl group interactions playing the largest role.

This achievement earned DIMPLES a Best Paper award at the ACM International Conference on Supercomputing 2025.

As Laboratory Director Steve Ashby pens his last monthly column, he highlights PNNL’s accomplishments with pride and gratitude.

Three research teams led by PNNL organizers achieve journal publications following the Mathematics Research Communities program.

PNNL showcased its expertise in AI, machine learning, and related domains at the International Conference on Machine Learning.

After isolating copper hydride monomers, researchers identified the mechanistic details of catalyst aggregation and cluster formation.

Expanding the nation’s largest wind power plant time series database; helping grid planners navigate a resilient energy future with better data.

Experiments focus on catalysts for off-road vehicles that optimize the use of critical minerals while lasting longer and costing less.