Artificial intelligence helps analyze images and reduce uncertainty for a key measure of watershed function.

Dynamic organic matter traits tied to reactions can surpass static descriptors in explaining decomposition rates and responses to environmental change.

Using an AI approach to combine outputs from microbial models and apply them as inputs to reactive-transport models reduces computational time by several orders of magnitude.

Global exercise advances nuclear forensic science and investigative capabilities.

PNNL experts trained Armenian engineers in ultrasonic testing, advancing local expertise for nuclear power plant inspections.

Integrating experiments and modeling show how a fungal exudate-inspired substrate mixture elicits an emergent growth phenotype and metabolic responses in Pseudomonas putida.

By selectively stabilizing either a right- or left-handed helical pose of its substrate, an enzyme tunes the final product.

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

Uncovered how the choice of carbon source influences the stress tolerance, redox balance, and regulatory responses of yeast, which can help provide resilience against scale-up challenges.

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

Five PNNL innovations earned 2026 R&D 100 Awards for advances in biotechnology, manufacturing, critical minerals, and nuclear security.

A team from PNNL joined the Department of Homeland Security Science and Technology Directorate and first responders from across the country to evaluate commercially available unmanned ground vehicles for emergency response.

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).

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

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.

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.

As space exploration flourishes, a new governance working group has been established; Kate Doty will co-lead the effort.

PNNL has administered NGFP on behalf of NNSA since 2002. Looking toward the future, NGFP has signed a partnership agreement with INMM with the goal of expanding its reach to more future leaders with a passion to serve.