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.

Computer scientist Antonino Tumeo is guest editing a special issue of IEEE’s Computer publication focused on democratizing microelectronics.

New partnership agreement to focus on AI, robotics, and advanced energy systems.

A new report highlights a public workshop hosted by the National Academies Forum on Microbial Threats and features PNNL's Lauren Charles and the AI-Driven One Health Security program.

A research team from Pacific Northwest National Laboratory used machine reasoning to schedule scientific workflows while guaranteeing quality of service (QoS).

PNNL researchers highlighted advances in AI for scientific discovery at the AI+ Expo, showcasing generative AI, autonomous research workflows, and scalable AI systems supporting energy, security, and national competitiveness.

Researchers at PNNL have developed an interpretable, lightweight AI model that can easily predict weld microstructure features using only basic machine sensor inputs.

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.

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.

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.

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.