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

At a geospatial intelligence conference, senior data scientist Adam Attarian participated in a panel discussing how national laboratories are delivering science, technology, and partnerships to enhance modeling, analysis, and innovation.

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

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

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.

At the summit, PNNL engaged with partners and investors, highlighting seven projects that accelerate the deployment of new energy technologies.

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

David Wunschel, a chemist and Laboratory Fellow, participated on a panel focused on how emerging technologies are transforming CBRN defense, detection, and response across military and government operations.

PNNL chemist Sarah Saslow received the ACS Rising Star Award for her research supporting environmental cleanup, including work on legacy waste and subsurface contamination.

A suite of new modeling methods gives researchers better tools to predict molecular behavior inside cavities.

The underlying crystal structure of carbonate surfaces plays a key role in the formation of cobalt mineral structures on these surfaces.

PNNL has developed a next-generation electrical resistivity tomography system for DOE that uses E4D software and AI-enhanced modeling to produce real-time subsurface images that help guide environmental remediation decisions.

Margaret Cheung elected to premier scientific society.

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

Replacing commercial acid with acidic waste enables researchers to improve nickel extraction efficiency, lower projected costs, and improve process economics.

Custom AI research assistant allows users to ask questions and get answers from hundreds of wildfire mitigation plans.

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

Automated liquid handling, combined with data analysis and AI, optimizes microbiological growth conditions at PNNL.

PNNL set to create a practical path for near-term hybrid classical-quantum computers with NVIDIA accelerated computing.