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

A new generation of microelectronics research begins at PNNL.

How to keep stray radiation from “shorting” superconducting qubits; a pair of studies shows where ionizing radiation is lurking and how to banish it.

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

The search for dark matter includes expertise in radio frequency signal detection, quantum sensing, and high-energy physics at PNNL.

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

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.

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.

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

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.

A new AI model developed at PNNL can identify patterns in electron microscope images of materials without requiring human intervention, allowing for more accurate and consistent materials science.

The convergence of artificial intelligence, cloud, and high-performance computing to accelerate scientific discovery is the focus of a multi-year collaboration between Microsoft and PNNL.