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.

FEMP's operations and maintenance (O&M) resources offer federal agencies technology- and management-focused guidance to improve energy and water efficiency and ensure safer and more reliable operations.

Cyber, physical, and blended cyber-physical threats are real, ubiquitous, and expensive to deal with. Private companies, government institutions, and critical infrastructures struggle to implement viable solutions as technology evolves.

From global issues such as melting permafrost and the creation of alternate biofuels to matters affecting microbiomes and micro-sized life, PNNL research is featured in news publications worldwide.

PNNL is a leader in the integration of aberration-corrected electron microscopy, in-situ techniques, and atom probe tomography to address challenges in nuclear materials, environmental remediation, energy storage, and national security.

PNNL partners with agencies and industry to identify and engage historically disadvantaged populations in regulatory decision-making, environmental assessment, and impact estimation of the consequences of complex polices and projects.

By improving the Weather Research and Forecasting (WRF)-Solar model, this project aims to reduce forecast errors, improve sub-grid scale variability estimates, and more accurately estimate forecast uncertainty.

The U.S. Department of Energy-sponsored Internet of Things Upgradeable Lighting Challenge is designed to encourage the widespread adoption of IoT-Upgraded Lighting.

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.

The National Response Framework Policy Landscape Analysis Tool interactively captures and visualizes intricacies of the National Response Framework, a federal guide to national response to all types of disasters and emergencies.

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.

PNNL data scientists and engineers will be presenting at NeurIPS, the Thirty Fourth Conference on Neural Information Processing Systems, and the co-located Women in Machine Learning workshop, WiML.

PNNL computer scientists are presenting the latest research in HPC at SC20.

STOMP is a suite of numerical simulators for solving problems involving coupled flow and transport processes in the subsurface. The suite of STOMP simulators is distinguished by application areas and solved mathematical equations.