In soil, microbes produce and consume methane. Using a technique called pool dilution, researchers can separate the rate of methane production and consumption from the net rate.

Visual Sample Plan, a free software tool developed at PNNL that boosts statistics-based planning, has been recognized with a 2024 Federal Laboratory Consortium Award.

Researchers created a methodology to design custom network topologies that improve the execution time for scientific applications.

Tumeo will lead the ChemComp project, which was awarded for Exploratory Research in Extreme Scale Science

Stinis will serve as a principal investigator on two of the four projects selected for funding by the Advanced Scientific Computing Research program.

Researchers unveil user-friendly software to keep complex projects on budget and on schedule.

The Rapid Assessment of Platform Technologies to Expedite Response project aims to prepare against future pandemics.

The award was given at the 29th International Conference on Information, Communication and Automation Technologies.

Zhijie (Jay) Xu will apply his expertise in multiscale modeling to this editorial role.

PNNL researchers present challenges and opportunities for fusing graph neural networks with deep reinforcement learning.

ACMD staff contributed to 30 years of atmospheric data collection for the Department of Energy’s Atmospheric Radiation Measurement user facility.

PNNL researchers outline open problems in different scientific areas in the Journal of Combinatorics.

Adam Attarian led a new panel session, titled National Labs and the GEOINT Mission, at the GEOINT 2023 Symposium, held May 21–24, 2023.

PNNL and The University of Texas at El Paso expand their premier partnership to include the Cyber Halo Innovation Research Program.

To overcome high-performance computing bottlenecks, a research team at PNNL proposed using graph theory, a mathematical field that explores relationships and connections between a number, or cluster, of points in a space.

Methods review suggests ways to use artificial intelligence and machine learning to handle missing data when integrating two or more omics approaches.

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.