Hydrogen bonding and proton transfer control the identity and reactivity of molecules in highly concentrated and alkaline aluminate solutions.

Report for the Oregon Public Utility Commission highlights innovations and best practices for resilience and utility planning could be helpful to other states as well.

The Building and Environment journal named Drgoňa’s co-authored work one of 2022’s best papers.

Data-driven autonomous technology to rapidly design and deliver antiviral interventions targeting SARS-CoV-2 to reduce drug discovery timeline and advance bio preparedness capabilities.

Data scientist Jung Lee accepted the position of review editor for Frontiers in Computational Neuroscience after serving as a guest editor for a special issue.

Scientists have taken a key step toward harnessing a form of AI known as deep reinforcement learning to protect computer networks.

Lili Yao named associated editor at the Journal of Hydrology, a leading journal in the field of hydrological sciences.

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.

Four PNNL researchers specializing in nuclear waste management received awards from the ASTM International Committee C26 on Nuclear Fuel Cycle.

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.

STEM Education programs at DOE and national labs offer environmental management internships; applications are due soon for summer 2023.

New PNNL research makes it easier to differentiate between chemical and nuclear explosions.

Researchers developed a machine learning model that can analyze chemical reactions as they happen in an electron microscope.

PNNL-Sequim team wins ESTCP Project of the Year for their work supporting underwater munition remediation.

Researchers identified a new intermediate phase that forms during the crystallization of hydrated aluminum-containing salts.

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.

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.

PNNL researchers created rugged, adaptable, mass-manufacturable luminescent particle tracers for use in harsh environments.

Data-driven experiments can help determine best practices for Random Forest machine learning to predict water quality, particularly nitrate levels.