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

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

Germany Harris, Dewayne Maye, Sarah Olocha, Shaniya Pettway, and Rayonna Redmon became the first interns of the Minority Serving Institution Partnership Program Partnership for Radiation Studies Consortium at PNNL.

A team of researchers at PNNL designed and constructed an atmospheric chamber to simulate the fate of volatile and semivolatile radioisotopes.

Advancing the science of radiation, especially among students at minority-serving institutions, is the goal of one of the Department of Energy’s newest consortia.

PNNL experts in nuclear forensics helped lead laboratories around the world through a rigorous test involving nuclear materials.