Five staff members from PNNL received awards from the Department of Energy’s Federal Energy Management Program for contributions to projects for the U.S. Army.
A PNNL team developed and used a model framework to understand the performance and structural reliability of a state-of-the-art solid oxide electrolysis cell design.
As leaders in AI and machine learning, PNNL experts are sharing their latest findings at the 36th annual Neural Information Processing Systems (NeurIPS) Conference, Nov. 28–Dec. 9, 2022.
PNNL’s fall Pathways to Excellence award ceremony celebrated nearly 50 staff for their contributions across science, engineering, operations, and STEM education.
The Department of Energy has issued updated energy conservation standards for manufactured homes. The effort to establish the standards, supported by PNNL, is expected to result in a range of benefits for the manufactured housing sector.
A new version of the Department of Energy’s Technical Resilience Navigator allows users to prioritize resilience solutions based on both risk reduction and emissions impact.
Scientists are pioneering approaches in the branch of artificial intelligence known as machine learning to design and train computer software programs that guide the development of new manufacturing processes.
PNNL’s Ján Drgoňa and Draguna Vrabie are part of an international team that authored a most-cited paper on Model Predictive Control, an approach for improving operations, energy efficiency, and comfort in buildings.
The American Chemical Society Richland Section has been recognized by its national organization with the Best Overall Section Minority Affairs award for 2022.
PNNL’s Reid Hart and Bing Liu have earned individual Champions of Energy Efficiency in Buildings awards from the American Council for an Energy-Efficient Economy.