The work by the team at PNNL takes a critical step in leveraging ML to accelerate advanced manufacturing R&D, specifically for manufacturing techniques without access to efficient, first-principles simulations.
PNNL scientists carve a path to profit from carbon capture by creating a system that efficiently captures CO2 and converts it into one of the world’s most widely used chemicals: methanol.
Extreme winter storms are growing wetter and changing shape in the Western United States—such changes could compromise infrastructure designed to withstand only so much water.
PNNL mathematician Aaron Luttman contributed to the organizing committee for workshop exploring robust machine learning and artificial intelligence systems for the U.S. Army.
The Joint Appointment program at PNNL is one of the most diverse among other U.S. national laboratories, involving nearly 60 universities and research institutions in the United States and abroad.
A review article led by researcher Jade Holliman explores the different classes of metamaterials, from the underlying fundamental science to potential applications.
Steven Spurgeon’s research is featured in the cover of the MRS Bulletin along with his team’s invited perspective on the future of machine learning for electron and scanning probe microscopy.
A PNNL team developed and used a model framework to understand the performance and structural reliability of a state-of-the-art solid oxide electrolysis cell design.
As leaders in AI and machine learning, PNNL experts are sharing their latest findings at the 36th annual Neural Information Processing Systems (NeurIPS) Conference, Nov. 28–Dec. 9, 2022.
The world's current climate pledges won't limit global warming to 1.5 °C. We will overshoot. A new study shows that more ambitious climate pledges could minimize the overshoot.
PNNL gathered researchers from eight national laboratories plus the U.S. Department of Energy (DOE) to share ideas and build synergy at the Energy Equity and Environmental Justice Summit.
