Researchers at PNNL have developed an interpretable, lightweight AI model that can easily predict weld microstructure features using only basic machine sensor inputs.

A suite of new modeling methods gives researchers better tools to predict molecular behavior inside cavities.

Secretary Wright visited PNNL's campus in Richland, Washington, late last year to meet with innovators in chemistry, biology, computing, and more.

Darío Gil, DOE's Undersecretary for Science, makes his inaugural visit to PNNL.

Neural networks techniques enable the efficient capture of electron correlation effects in reduced-dimensionality spaces.

Developing a new scheme to more efficiently solve quantum chemistry problems.

Pacific Northwest National Laboratory researchers developed a new theoretical method for studying highly correlated systems.

PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.

A new approach validates quantum algorithms for low-energy nuclear physics applications