The Simple Building Calculator, developed at PNNL, meets a need for a quick, interactive, and economic method to evaluate energy use—and potential savings from efficiency measures—in simple commercial buildings.

Researchers developed a machine learning model that can analyze chemical reactions as they happen in an electron microscope.

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.

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.

Research organizations work toward compelling market-ready solutions for building operations.

PNNL researchers teamed up with SambaNova to enhance artificial intelligence for scientific computing.

Researchers studied water cycling to assess the achievability of net-zero water usage at military installations in the Pacific Islands.

PNNL researchers design an artificial intelligence-guided electron microscope in a Laboratory Directed Research and Development study.

The PNNL-developed Eclipse VOLTTRON™ software platform continues to support new applications in energy efficiency and clean energy.

PNNL will provide technical resources and support to a national coalition of states and cities focused on implementing building performance standards to improve energy efficiency and reduce carbon emissions.

A multi-institutional team has developed a deep learning framework to identify novel molecules as drug candidates.

Researchers developed a new tool that combines artificial intelligence and microscopy data to rapidly test new materials for fusion reactor use.

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.