PNNL is leading the nation with research addressing urgent needs for reimagining U.S. critical infrastructure against the realities of software-speed attacks and hazards.

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

Pacific Northwest National Laboratory is partnering with the U.S. Department of Homeland Security’s Cybersecurity and Infrastructure Security Agency to develop and operate the Control Environment Laboratory Resource

The Data-Model Convergence (DMC) Initiative is a multidisciplinary effort to create the next generation of scientific computing capability through a software and hardware co-design methodology.

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.

PNNL’s MAC Privacy Linux Kernel Module project aims to interpret and integrate the IEEE standard (802.1AEdk-2023) within the Linux Kernel environment.

The Mathematics for Artificial Reasoning in Science (MARS) initiative seeks to complement machine learning and deep learning to advance science.

Mega AI seeks to develop massive-scale, self-supervised, multimodal foundation models of scientific knowledge capable of general-purpose inferences to enable reasoning with existing knowledge and discovery of new knowledge.