Machine learning models help identify important environmental properties that influence how often extreme rain events occur with critical intensity and duration.

PNNL researchers combined deep operator networks with spectral methods to efficiently and accurately solve complex equations.

PNNL is working with the Port of Seattle and Seattle City Light to assess the risks of long-term hydrogen storage that can bring clean power for decarbonization.

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.

Harnessing the sea for decarbonized energy is the focus of a new collaboration between PNNL and the University of Guam.

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

A PNNL team developed and used a model framework to understand the performance and structural reliability of a state-of-the-art solid oxide electrolysis cell design.

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.

Tailoring hydrogen-based energy storage solutions to specific real-world applications.

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

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.

Researchers found that certain oxide interface configurations remain stable in extreme environments, suggesting ways to build better performing, more reliable devices for fuel cells, space-based electronics, and nuclear energy.