Pacific Northwest National Laboratory researchers developed a new theoretical method for studying highly correlated systems.

Nineteen PNNL-affiliated researchers have been named to Clarivate’s 2024 list of the world's most highly cited researchers.

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

In a recent publication in Nature Communications, a team of researchers presents a mathematical theory to address the challenge of barren plateaus in quantum machine learning.

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 .

A new approach validates quantum algorithms for low-energy nuclear physics applications

PNNL advisors joined a panel of Washington State emergency management personnel to discuss how partnerships with national laboratories are enabling science and technology solutions.

A new quantum algorithm speeds up simulations of coupled oscillator dynamics.

In a new approach, quantum flow algorithms are used to simulate many-body systems on quantum computers and effectively describe chemical processes.

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.

Recovery Simulator and Analysis tool further strengthens PNNL’s short-term resilience planning efforts for Puerto Rico.

A combined experimental and theoretical study identified multiple interactions that affect the performance of redox-active metal oxides for potential electrochemical separation and quantum computing applications.

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.

Report for the Oregon Public Utility Commission highlights innovations and best practices for resilience and utility planning could be helpful to other states as well.

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.

A new approach broadens the reach of quantum computers to previously challenging systems.

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

HiSVSIM enables large-scale quantum simulations through graph partitioning.