An AI program called AtomAI helped PNNL researchers reveal key design principles for protein interfaces with mineral surfaces.

Autonomous science laboratories are taking shape quickly across the PNNL campus.

PNNL is central to the Genesis Mission, leading and participating in projects central to the nation’s security and international competitiveness.

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

Mystery of how important aluminum minerals form and dissolve solved with materials science and advanced imaging studies.

Ice crystals are surprisingly tolerant of defects in their structure. The findings come from the first-ever molecular-resolution observations of nanoscale samples of ice frozen from liquid water.

More than 25,000 participants from industry, government, academia, and the public attended the event.

Brown University professor and PNNL joint appointee was honored for his exceptional work in applied mathematics.

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.

Formal mathematics both reveals what’s inside the black box of AI algorithms and pushes the boundary of what’s possible within math as a discipline.

A new generation of microelectronics research begins at PNNL.

Federal environmental permitting process to be fast-tracked with AI-powered tools and cloud-supported data analysis.

University of Washington Professor David Baker won the 2024 Nobel Prize in Chemistry for computational protein design.

Pacific Northwest National Laboratory scientists discussed their materials research at Accelerate’24.

Calculating quantum chemistry quickly allows scientists to refine their understanding of complex chemical systems.

A team of researchers at PNNL is developing a new approach to explore the higher-dimensional shape of cyber systems to identify signatures of adversarial attacks.

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

Joint Appointee Mitra Taheri spoke on the AI in Government episode of the EY Leading into Tomorrow podcast.

Researchers discussed this topic at the 2024 Analog Computing for Science Workshop co-led by Antonino Tumeo.

Ultra-thin layers of silk deposited on graphene in perfect alignment represent a key advance for the control needed in microelectronics and advanced neural network development.