A breakthrough in electron microscopy based on deep learning can automatically visualize and identify areas of interest, helping to speed advances in materials science.

Machine learning methods and data science can uncover hidden patterns in nanoparticle synthesis conditions and property outcomes.

PNNL’s software tool brings together artificial intelligence and satellite imagery to help responders assess damage and take action.

PNNL researchers are helping to better define the need for grid energy storage in future clean energy scenarios, as well as working to improve technologies for storing renewable energy so it's available when and where it's needed.

Artificial intelligence may help humans and computers work together to monitor materials that could be used to make weapons.

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.

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

A multi-institutional team has developed a deep learning framework to identify novel molecules as drug candidates.

Researchers developed a new tool that combines artificial intelligence and microscopy data to rapidly test new materials for fusion reactor use.

Contributions from researchers across Pacific Northwest National Laboratory (PNNL) were recently recognized in the preliminary findings of a Secretary of Energy Advisory Board (SEAB) report.

Combining its strength in biological sciences and data analytics, researchers at the Department of Energy's PNNL are working to enable a quick response to a biological incident — whether intentional, accidental or natural.