The Battery Manufacturing Accelerator will integrate Super Intelligence and advanced manufacturing to speed energy storage innovation from lab to market.

Scientists are using agentic agents to speed up the process of toughening materials for their future in space or in nuclear reactors.

The Microbial Molecular Phenotyping Capability has been designed to transform how we could put microbes to work for American manufacturing competitiveness and human health advances, among other benefits.

The PermitAI™ team at PNNL has issued two new upgrades to its public resources for searching federal environmental permitting data.

A new interactive map gives state policymakers a birds-eye-view of wildfire-related legislation around the United States.

PNNL and Amazon will collaborate to help grid operators respond to emergencies and threats.

Genesis Mission awards to PNNL will advance scientific discovery, next-generation energy systems and advanced manufacturing, among others.

Coding agents repurposed to draft complex environmental impact statement sections through PermitAI public-private partnership model.

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

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.

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.

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.

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

PNNL's Building Energy Model AI is an open-source bot that can help energy modelers save time and money.

From exploring the development of transformative materials to strengthening biotechnology, six up-and-coming PNNL scientists were recognized for their scientific vision and prowess.