PNNL is working with national laboratories and academia to provide electric vehicle manufacturers with batteries that are more reliable, high-performing, safe, and less expensive.
A multi-institution research team led by PNNL is addressing curb usage management challenges in large urban areas by developing a city-scale dynamic curb use simulation tool and an open-source curb management platform.
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.
PNNL administers two research buoys for the U.S. Department of Energy that allows collection of wind meteorological and oceanographic data off the nation's coasts.
PNNL is leading a consortium that provides funding opportunities to the automotive industry for accelerating new lightweight technologies in on-highway vehicles.
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 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.
The Salish Sea Model (SSM) is a predictive coastal ocean model for estuarine research, restoration planning, water-quality management, and climate change response assessment.
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 creates immersive software experiences to meet a variety of challenges. One such challenge in science, technology, engineering, and mathematics (STEM) education is providing quality computer science education for all students.