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.

Vijay Murugesan highlights how AI can accelerate materials discovery at the Volta Foundation’s Battery Forum.

Bylaska and team will aim to make computational design of functional materials more targeted, impactful, and user friendly through this award.

Three PNNL technologies have been named 2024 R&D 100 Award winners.

Researchers will explore climate, pathogens and energy-efficient microelectronics using 3 million node hours on the nation’s supercomputers.

Complex metal alloys enter a new era of predictive design for aerospace and other high-temperature applications.

A breakthrough in electron microscopy based on deep learning can automatically visualize and identify areas of interest, helping to speed advances in materials science.

PNNL computing experts Robert Rallo and Court Corley contribute their knowledge to a recent DOE report on applications of AI to energy, materials, and the power grid.

PNNL joint appointee Sergei Kalinin earned the Peter Duncumb Award for excellence in microanalysis.