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.

To thwart pathogens, researchers in the epidemiology field of infectious disease (ID) prediction are continuously trying to forecast when, where, and how an ID event will occur.

PNNL researchers use machine learning and data analytics to assist with detection of nuclear proliferation and nuclear material trafficking.

PNNL joint appointee Auroop Ganguly uses data science to study the impacts of climate change.

A new web-based tool provides easy-to-understand progress metrics and other data about groundwater cleanup sites overseen by the DOE Office of Environmental Management.

Lauren Charles joins the CABI One Health Editorial Board as an associate editor.

SEA-CROGS is one of four Mathematical Multifaceted Integrated Capability Centers selected for support by the Department of Energy.

The IAC will advise the Secretary of Commerce on the science and technology needs of the microelectronics industry.

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.

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

New research makes molecular-level calculations of water faster and easier.

Teams at PNNL welcomed their first two interns this summer from the competition-based program.

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.

James Ang, John Feo, and Marco Minutoli organized a high performance computing workshop at the prestigious ISC High Performance 2022 conference.

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

TransMED analyzes patient data from similar diseases across multiple sources to understand COVID-19 patient outcome risk factors.

Business, industry, and government careers were a focus of the PNNL-led Mathematics Research Communities conference.

PNNL researchers developed a hybrid quantum-classical approach for coupled-cluster Green’s function theory that maintains accuracy while cutting computational costs.

Parallel graph neural networks identify protein-ligand 3-D structural interactions to aid in protein function prediction and drug discovery.

Srinivas Katipamula and Ron Underhill were invited to share their expertise in energy efficient building operations.