The AI-Driven One Health Security activities at PNNL are focused on disrupting health threats and their impacts.

PNNL partners with the Department of Homeland Security, industry, and the first-responder community to improve the effectiveness and safety during biodetection responses.

Pacific Northwest National Laboratory (PNNL) is advancing AI that unlocks new scientific discoveries, ensures reliable energy, and strengthens national security through the Center for AI @PNNL and its partnership with the Department of Ener

PNNL and ORNL are working together on Digital Twins to modernize the U.S. hydropower plant fleet, which will reduce operating costs, improve reliability, reduce downtime, enhance grid resiliency, and reduce environmental impacts.

PNNL is launching a new foundational capability to address broad, complex energy security challenges our country faces.

Usable and explainable models for global-scale, cross-lingual proliferation expertise identification and forecasting.

Pacific Northwest National Laboratory’s (PNNL) Generative AI (GenAI) investment aims to harness this transformative technology to drive innovation across the science, energy, and security research domains.

Pacific Northwest National Laboratory (PNNL) is a key partner in the Genesis Mission, the Department of Energy’s (DOE’s) integrated ecosystem to accelerate discovery and strengthen U.S. leadership in AI-powered science.

PNNL deploys advanced drone systems to tackle the nation’s most pressing challenges in energy, environmental remediation, and national security.

PNNL is heavily engaged in the development and use of mass spectrometry technology across its science, energy, and security missions, from fundamental research through mature operational capabilities.

Pacific Northwest National Laboratory supports innovations in data analytics, instrumentation, and experimental techniques for the Northwest (NW) Biopreparedness Research Virtual Environment (BRaVE) Initiative.

Pacific Northwest National Laboratory has pioneered the use of observational research for evaluating energy efficient technologies in the built environment.

Physics-informed machine learning (PIML) is a modeling approach that harnesses the power of machine learning and big data to improve the understanding of coupled, dynamic systems.