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.

GeoBOSS is a software library that combines the data-handling capabilities of Spark and the user-friendliness of Python to simplify geospatial analytics and the transition between small-scale research and large-scale operational projects.

The Ion Dynamics in Radioactive Environments and Materials (IDREAM) Energy Frontier Research Center (EFRC) conducts fundamental science to support innovations in retrieving and processing high-level radioactive waste.

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

The Institute for Integrated Catalysis (IIC) at Pacific Northwest National Laboratory explores and develops the chemistry and technology of catalyzed processes that enable a carbon-neutral future.

PNNL is one of five national laboratory JCESR -- a bold effort to transform the way we store energy to power vehicles and the electric grid.

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.

Advancing the understanding and monitoring of nuclear material processing to accelerate development and qualification of new material systems for national security and nuclear energy.

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

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

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.