Using multiple specialized techniques, IDREAM researchers gained a better understanding of how trace impurities within gibbsite affect the hydrogen yield.
A research team from Pacific Northwest National Laboratory used machine reasoning to schedule scientific workflows while guaranteeing quality of service (QoS).
Researchers at PNNL have developed an interpretable, lightweight AI model that can easily predict weld microstructure features using only basic machine sensor inputs.
David Wunschel, a chemist and Laboratory Fellow, participated on a panel focused on how emerging technologies are transforming CBRN defense, detection, and response across military and government operations.
PNNL has developed a next-generation electrical resistivity tomography system for DOE that uses E4D software and AI-enhanced modeling to produce real-time subsurface images that help guide environmental remediation decisions.
RemPlex 2025 Global Summit on Environmental Remediation attendees share knowledge about cleanup and monitoring of complex sites worldwide; more than 100 presentations are posted online.
The Center for Continuum Computing at PNNL aims to integrate cloud platforms, high-performance computing, and edge devices into a seamless ecosystem that accelerates scientific discovery.
PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.