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.

A new sodium battery technology shows promise for helping integrate renewable energy into the electric grid. The battery uses Earth-abundant raw materials such as aluminum and sodium.

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.

The Public Infrastructure Security Cyber Education System is a university-community-nonprofit collaboration changing cyber education and cybersecurity.

Haewon McJeon will co-edit a special issue of Energy and Climate Change focused on national decarbonization strategies across the globe.

PNNL’s Karma Sawyer has been named a Distinguished Gilbreth Speaker by the National Academy of Engineering.

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.

Staff at PNNL recently completed a report highlighting commercial products enabled through projects funded by the Department of Energy’s Building Technologies Office.

Zhenyu (Henry) Huang acts as guest editor in an upcoming special issue on “Climate Change Mitigation and Adaptation in Power and Energy Systems." The special edition is published by Elsevier.

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 Simple Building Calculator, developed at PNNL, meets a need for a quick, interactive, and economic method to evaluate energy use—and potential savings from efficiency measures—in simple commercial buildings.

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

PNNL scientists Daniel Gaspar and Wei Wang were among nine scientists featured in journal article recognizing 2021-2022 ACS award honorees.

PNNL and Georgia Tech launch a joint institute focused on accelerating security for the critical infrastructure community.

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.

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