PNNL computer scientist Ang Li serves as one of the principal investigators for a project selected to receive DOE funding.

Slaven Peles, PNNL computational scientist and leader of a national high-performance computing project for power grid analysis, spoke about the project with the host of the Let’s Talk Exascale podcast.

PNNL researchers deploying and testing novel building technologies and energy-management approaches.

Researchers to design their own computer accelerators for faster analysis of large datasets.

The first customized resource of its kind, H-BEST analyzes the indoor environmental quality profile for buildings and helps its users identify the costs and benefits of improvements.

Modeling study suggests vigorous ventilation can cause spike in viral concentrations

OpenCGRA expedites the design-prototype cycle of computer architecture development.

A research project that brings together mathematicians and atmospheric scientists has developed into a deep collaboration for improving atmospheric models.

U.S. Secretary of Energy Jennifer M. Granholm virtually visits PNNL, one of several national lab visits she is conducting.

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.

Vigorous and rapid air exchanges might not always be a good thing when it comes to levels of coronavirus particles in a multiroom building, according to a new modeling study.

PNNL highlights four researchers whose joint appointments are creating new and diverse opportunities for expanding knowledge and scientific impact across institutions.

George Karniadakis receives award highlighting 30 years of work developing computational methods to solve real-world problems.

New approach allows nonexperts to optimize control of a building's energy systems without adding computing power or proprietary software.

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

Using public data from the entire 1,500-square-mile Los Angeles metropolitan area, PNNL researchers reduced the time needed to create a traffic congestion model by an order of magnitude, from hours to minutes.

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.

PNNL researchers have shown an improved binarized neural network can deliver a low-cost and low-energy computation to help the performance of smart devices and the power grid.

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

PNNL and Microsoft Quantum partner to link quantum circuits to powerful government supercomputers.