New research aids the design of more efficient water purification technologies.

Marcel Baer is a computational scientist working in PNNL’s Physical Sciences Division with a prominent effort in materials science and physical bioscience.

With quantum chemistry, researchers led by PNNL computational scientist Simone Raugei are discovering how enzymes such as nitrogenase serve as natural catalysts that efficiently break apart molecular bonds to control energy and matter.

Connected moments math shortcut shaves time and cost of quantum calculations while maintaining accuracy.

PNNL’s longstanding grid and buildings capabilities are driving two projects that test transactive energy concepts on a grand scale and lay the groundwork for a more efficient U.S. energy system.

The project received an Innovative and Novel Computational Impact on Theory and Experiment (INCITE) award, a highly competitive U.S. Department of Energy Office of Science program.

PNNL data scientists Ján Drgoňa and Draguna Vrabie are working to reduce deployment costs of advanced energy control systems for buildings.

Pacific Northwest National Laboratory researchers used machine learning to explore the largest water clusters database, identifying—with the most accurate neural network—important information about this life-essential molecule.

The PNNL-developed VOLTTRON™ software platform’s advancement has benefited from a community-driven approach. The technology has been used in buildings nationwide, including most recently on a university campus.

PNNL engineer Srinivas Katipamula was recognized by the American Council for an Energy-Efficient Economy with a 2020 Champion of Energy Efficiency Award.

Contributions from researchers across Pacific Northwest National Laboratory (PNNL) were recently recognized in the preliminary findings of a Secretary of Energy Advisory Board (SEAB) report.

Four researchers from PNNL were recently honored for contributing to two U.S. Department of Energy Office of Energy Efficiency and Renewable Energy initiatives that support the blue economy and building-grid integration.

PNNL employs deep learning strategies to advance Model Predictive Control (MPC), a highly promising solution for intelligent building operations.

PNNL’s Srinivas Katipamula and Nora Wang have received a Northwest Energy Efficiency Alliance award for contributing to the success of Seattle’s Building Tune-Up Accelerator Program.

PNNL’s Intelligent Load Control technology manages and adjusts electricity use in buildings when there’s peak demand on the power grid.

A PNNL technology enables automated Economic Dispatch, which coordinates the use of energy in a manner that enhances distributed generation, efficiency, renewables, and grid reliability.

PNNL researchers Jianming Lian, Karanjit Kalsi, joint appointee Wei Zhang, and former PNNL intern Sen Li recently received a patent for a market mechanism consisting of novel bidding and clearing strategies.

A PNNL researcher peers into the future and sees homes and neighborhoods seamlessly working together to coordinate energy use.