Exploring why some brains are more resilient to the impact of Alzheimer’s disease could lead to improved diagnoses, treatment and prevention.

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

A research buoy managed by PNNL has been deployed in Hawai’ian waters, collecting oceanographic and meteorological measurements off the coast of O’ahu.

Samantha Erwin was one of six speakers during the Careers Using Mathematics panel at the Nebraska Conference for Undergraduate Women in Mathematics.

Researchers combined crystallographic data and computational studies to investigate plutonium-ligand bonding within a hybrid material construct.

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.

PNNL is making waves in pursuit of a blue economy. Researchers are advancing science and technology to expand—and protect—the gifts of the sea.

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.

For a second year in a row, doctoral intern Jack Watson was awarded the Student Merit Award by the Society for Risk Analysis and the Resilience Analysis Specialty group.

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

A multi-institutional team of wind energy experts led by PNNL assessed the scientific grand challenges for offshore wind and provided recommendations for closing gaps in models.

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.

The American Society of Mechanical Engineers recognized radioactive material transportation expert Matt Feldman with its 2022 Dedicated Service Award.

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.

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.

PNNL researchers use machine learning and data analytics to assist with detection of nuclear proliferation and nuclear material trafficking.