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

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

Research shows that coupling geothermal power plants with lithium extraction from geothermal brine would make geothermal energy more economically viable, providing renewable energy and valuable raw materials.

Scientists are pioneering approaches in the branch of artificial intelligence known as machine learning to design and train computer software programs that guide the development of new manufacturing processes.

In honor of National Intern Day, PNNL and intern Jonathan Mills were named among the Top 100 in the nation by early-career recruiting firm WayUp.

PNNL researchers teamed up with SambaNova to enhance artificial intelligence for scientific computing.

As wildfires grow increasingly severe, PNNL researchers are working to slow their spread and mitigate their harmful effects.

TransMED analyzes patient data from similar diseases across multiple sources to understand COVID-19 patient outcome risk factors.

PNNL researchers design an artificial intelligence-guided electron microscope in a Laboratory Directed Research and Development study.

Parallel graph neural networks identify protein-ligand 3-D structural interactions to aid in protein function prediction and drug discovery.

This PNNL-developed separation system quickly and successfully separates larger particles from smaller ones at various scales, in different solid-liquid mixtures and at different flow rates.

PNNL and industry developing a faster, less expensive way to extract lithium for batteries used in cell phones and electric vehicles.

PNNL experts perform fundamental climate science work at multiple scales to understand impacts on energy, water, food, and infrastructure systems.

Tim Johnson has been selected for an open-ended term as associate editor of the Groundwater journal.

Recognizing how innovation and clean technologies at the very edge of the grid can work together to transition the electricity system, PNNL takes a multidisciplinary approach to advancing and integrating renewable energy solutions.

PNNL Chief Scientist for Computing Jim Ang will be part of a DOE Office of Science virtual discussion regarding industry collaborations on AI hardware.

PNNL data scientist to edit collection of research articles focused on understanding how microcircuits function in the brain and in artificial intelligence systems.

A paper co-authored by Courtney Corley was recently selected as the most influential paper for the Twenty-First National Conference on Artificial Intelligence.