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

PNNL researchers are advancing AI-assisted nuclear fuel recycling to tap more than 90% of the energy potential left in used fuel, reduce waste and support secure, cost-effective next-generation reactors.

Researchers showed that spiking neural networks can be accurate for regression problems, opening opportunities for efficient neuromorphic computing.

PNNL scientists were among the specialists keeping the crowds safe at the recent FIFA World Cup competition.

PNNL Launches Center for Robotics and Autonomy to advance trustworthy autonomous science at speed and scale.

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

PNNL mathematician and AI researcher Henry Kvinge bridges math and AI, finding connections between the two fields and studying ways where they can foster mutual progress.

PNNL’s nondestructive evaluation capabilities support advancements in nuclear energy, including research for the U.S. Nuclear Regulatory Commission to preserve the nation’s existing nuclear fleet.

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

AutoLabs is a new agentic AI system that translates experimental goals into robot-specific language.

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.

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.