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.

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.

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.

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.

PNNL combines AI and cloud computing with damage assessment tools to predict the path of wildfires and quickly evaluate the impact of natural disasters, giving first responders an upper hand.

Poorly insulated walls in residential buildings waste an estimated quadrillion+ Btus of energy per year. Upgrading windows and insulation during re-siding projects is a unique, cost-effective opportunity to improve efficiency and comfort.

PNNL is working on behalf of the U.S. Department of Energy to create a prototype system that enables homes to help provide services to the power grid while delivering economic benefits to residents.

PNNL’s Space Science, Technology, and Policy activities draw on diverse technical expertise and research capabilities from across the Laboratory.

A software suite for working with neutron activation rates measured in a nuclear fission reactor, an accelerator-based neutron source, or any neutron field to determine the neutron flux spectrum using a generalized least-squares approach.

PNNL has developed a tool suite of interactive analytics that can be rapidly integrated into analyst workflows to empirically analyze and gain qualitative understanding of AI model performance jointly across dimensions.

The Water Cycle and Climate Extremes Modeling (WACCEM) Scientific Focus Area advances predictive understanding of water cycle variability and change through foundational research using models, observations, and novel numerical experiments.