PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.

PNNL researchers will pursue both fundamental and applied projects with this support from the Department of Energy

The Grid Storage Launchpad dedication event was attended by leaders in grid and transportation energy storage, battery innovation, and industry stakeholders working to transform America’s energy system.

Erich Hsieh, Deputy Assistant Secretary for OE’s Energy Storage Division, shared insights about the Grid Storage Launchpad and energy storage innovations .

Energy storage expert Vince Sprenkle leads Office of Electricity's first facility to advance next-generation energy storage technologies.

A streamlined informatics workflow that automatically processes complex data and provides the locations of double bonds in lipids.

Hi-C metagenomics-informed phage-host infection networks detected in soil pre- and post-drying.

Researchers designed a new, highly efficient, stable redox-active material from neutral red dye that can efficiently capture CO2 from air.

Leaders from the DOE Office of Energy Efficiency and Renewable Energy visited PNNL October 19–20 for a firsthand look at capabilities and research progress.

PNNL celebrates World Energy Storage Day (September 22) by introducing you to some of our best and brightest in this area.

PNNL welcomes six Department of Energy Office of Science Graduate Student Research awardees from across the nation.

Metabolomics analysis revealed differences in metabolome profiles for two distinct modes of fungal wood decay.

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 researchers design liquid-based porous electrolyte that could transport lithium ions more easily between electrodes, improving battery efficiency.

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.

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

Data from increasingly complex mass spectrometry instruments met with adaptable data analysis solution.