A PNNL-developed computational framework accurately predicts the thermomechanical history and microstructure evolution of materials designed using solid phase processing, allowing scientists to custom design metals with desired properties.

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.

Research published in Journal of Manufacturing Processes demonstrates innovative single-step method to manufacture oxide dispersion strengthened copper materials from powder.

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.

Multidisciplinary teams from research and industry are brought together using management systems developed by a team of software engineers at PNNL.

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

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

A process developed at PNNL that converts biomass and waste into a chemical intermediate or into gasoline, diesel, and jet fuel is available for commercial licensing.

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

The Co-Optimization of Fuels & Engines initiative recently outlined six years of research in a findings and impact report.

Scientists from PNNL and the U.S. Department of Agriculture-Forest Services’ Pacific Northwest Research Station have partnered to evaluate potential climate and wildfire adaptation scenarios and resulting benefits from restoration forestry.

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.