Predicting how organisms’ characteristics respond to not only their genes, but also their environments (a nascent field called predictive phenomics), is extraordinarily challenging. Researchers at PNNL are using AI to tackle that challenge.

Americans produce enough waste every year to replace up to a quarter of the country’s jet fuel with sustainable alternatives, new research shows.

PNNL scientists developed a new method to map exactly how a fungus works with leafcutter ants in a complex microbial community to degrade plant material at the molecular level. The team’s insights are important for biofuels development.

A new discovery by PNNL researchers has illuminated a previously unknown key mechanism that could inform the development of new, more effective catalysts for abating NOx emissions from combustion-engines burning diesel or low carbon fuel.

Focused research studies of advanced algorithms and materials are setting the stage for the next generation of quantum computers.

Risk analysis on the plutonium-fueled power system that supplies electricity to the Mars rover answered the “what if” nuclear safety questions for NASA.

PNNL researchers are using explainable artificial intelligence to develop new ways to understand and interpret data for national security.

Explainable artificial intelligence—understanding and explaining the reasoning behind AI decisions—is a necessity for national security.

Explainable AI helps scientists understand and untangle the threads of information that the system uses to make choices or recommendations.

Quantum computing experts gather in the Pacific Northwest to discuss challenges and progress.

Researchers apply numerical simulations to understand more about a sturdy material and how its basic structure responds to and resists radiation. The outcomes could help guide development of the resilient materials of the future.