Input from humans helps when deciding whether to trust the recommendations or decisions of a machine-learning system.

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

Researchers from PNNL, CSET, and Google DeepMind delved into the potential risks of artificial intelligence in a roundtable discussion.

Robert Rallo from Pacific Northwest National Laboratory will direct a machine learning thrust for a new Department of Energy-funded project led by SLAC National Accelerator Laboratory.

The results of this study are consistent with the idea that the stress of chronic salinity exposure changes tree leaf shape and function, weakening their physiology and setting in motion processes that lead to death.

PNNL’s software tool brings together artificial intelligence and satellite imagery to help responders assess damage and take action.

Newly developed ultra-low radiation cables reduce background noise for neutrino and dark matter detectors.

PNNL is collaborating with Microsoft, Micron and other partners to make computational chemistry broadly available.

PNNL’s Nick Ward has been honored with the American Geophysical Union’s Thomas Hilker Early Career Award.

Pasco School District teachers join PNNL for an immersive teaching experience.

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

The diversity and function of organic matter in rivers at a large scale are influenced by factors, such as the types of vegetation covering the land, the energy characteristics, and the breakdown potential of the molecules.

PNNL chemists investigate the performance of ion exchange resins for subsurface remediation of uranium, technetium, and chromium.

Six PNNL projects were selected as part of the Department of Energy’s Funding for Accelerated, Inclusive Research.

The Human Factors Symposium took place at Discovery Hall at PNNL in May 2023. Fifty-seven attendees participated in the three-day event representing 15 different institutions.

David Barajas-Solano and Aaron Luttman worked with students to create models to evaluate remediation strategies at the Hanford Site.

At the Nonproliferation, Counterproliferation, and Disarmament Science Gordon Research Conference, researchers from PNNL shared research and scientific approaches for countering diverse threats.

Brown University Applied Mathematics and Engineering Professor George Karniadakis has driven solutions for science and engineering problems for over ten years with a joint appointment at PNNL.

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

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