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.
PNNL mathematician Aaron Luttman contributed to the organizing committee for workshop exploring robust machine learning and artificial intelligence systems for the U.S. Army.
Researchers at PNNL leveraged their experiences to connect with attendees at the 2022 SACNAS National Diversity in STEM Conference, October 27–29, 2022, in San Juan, Puerto Rico.
The nature of an iron oxide mineral and amount of light control electron movement from a light-absorbing molecule to the mineral, rather than the conditions of the surrounding aqueous environment.
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.