A new invention from PNNL could soon help manufacturers more easily adopt the Lab’s patented Shear Assisted Processing and Extrusion (ShAPE™) technology, enabling faster manufacturing of superior extrusion products.

PNNL will build an AI-powered modeling platform to help geothermal energy operators maximize electricity generation.

PNNL convenes leaders from the Department of Energy, national laboratories, computing industry and academia for a two-day workshop to advance quantum computing for computational chemistry.

The comprehensive guide can help companies build AI data centers that keep energy costs low and maintain grid stability.

Mechanical engineer Scott Whalen is named the Department of Energy’s Innovator of the Year for turning the patented metal extrusion invention ShAPE™ into an industrially relevant extruded metal product juggernaut.

Battery researchers are ready to work with private companies who want to test their own chemistries in a prismatic cell format.

Critical minerals could be sourced from industrial waste at scale thanks to an AI-guided separation workflow developed by researchers at PNNL.

PNNL helps strengthen domestic supply of critical mineral needed for new cutting-edge cancer therapy.

PNNL has named Douglas Mans and Daniel Stephens as associate laboratory directors to lead science and national security strategies.

Stephen Hernandez and Kaley Walker are among the 72 fellows who have the distinction of being part of the 30th class of the NNSA Graduate Fellowship Program.

Integrating multiple structural proteomics techniques helps unravel microbial responses to changing environments—key to developing next-generation biotechnology solutions.

Working with DOE and PNNL, Google has created a new badge that will help consumers find highly skilled home energy specialists.

PNNL researchers will deploy advanced AI systems to study how to best secure the future electric grid.

High-Velocity Joining is a new joining technology that could reduce energy use and cost for manufacturers.

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

Scientists equipped research subjects with wristbands to measure many factors that contribute to their exposome.

A newly published custom collection of glass formulas for nuclear waste vitrification — developed and validated by PNNL over the past three years — was made possible in that timeframe with AI tools, including a PNNL machine learning model.

Scientists are optimizing microbes to harvest critical minerals and produce bioproducts using AI and autonomous lab equipment.

An international research team deployed AI-powered computing to solve important chemical structures for energy applications using NVIDIA hardware.

PNNL has chosen three proven leaders to serve in key roles guiding lab research and operations.