Machine learning models help identify important environmental properties that influence how often extreme rain events occur with critical intensity and duration.
Data-driven autonomous technology to rapidly design and deliver antiviral interventions targeting SARS-CoV-2 to reduce drug discovery timeline and advance bio preparedness capabilities.
The work by the team at PNNL takes a critical step in leveraging ML to accelerate advanced manufacturing R&D, specifically for manufacturing techniques without access to efficient, first-principles simulations.
Staff at PNNL recently completed a report highlighting commercial products enabled through projects funded by the Department of Energy’s Building Technologies Office.
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.
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.
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.
PNNL worked with the Department of Energy on the Commercial Packaged Boiler rule, which will help reduce energy use, enhance the environment, and save dollars.
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.
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.
