PNNL researchers Daniel Deng and Yehia Ibrahim join the 2025 class of National Academy of Inventors Fellows.

Scientists provide a guide for the rational application of continuum descriptions of fluid flows based on interfacial chemistry.

This achievement earned DIMPLES a Best Paper award at the ACM International Conference on Supercomputing 2025.

ThermalTracker-3D can help streamline permitting, reduce costs, and shorten development time of energy projects.

Three research teams led by PNNL organizers achieve journal publications following the Mathematics Research Communities program.

Delivering an integrated quantum-mechanical and experimental perspective on the effects of both intrinsic and externally applied electric fields at atomic-scale interfaces.

Expanding the nation’s largest wind power plant time series database; helping grid planners navigate a resilient energy future with better data.

Isotopically layered water films and slow heating enable diffusion tracking.

A team combined experiments and theory to predict the collective properties of electrolyte solutions.

The first measurement of the proton diffusion constant at cryogenic temperatures provides insights into the mechanism of proton movement in supercooled water.

PNNL researchers have published their paper, “Introducing Molecular Hypernetworks for Discovery in Multidimensional Metabolomics Data,” in the Journal of Proteome Research.

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

With the launch of a large research barge, PNNL and collaborators took another significant step to improve offshore wind forecasting that will lower risk and cost associated with offshore wind energy development.

Researchers investigated how stable nanoparticle suspensions form using facet engineering on hematite nanoparticles, demonstrating that controlling the faceting of nanoparticles can effectively maintain particle dispersity.

The nation is closer to its offshore wind energy goals than ever before, but better wind forecasting is still needed. To address this challenge, PNNL and collaborators are charting a new course with help from novel technology.

Researchers created a methodology to design custom network topologies that improve the execution time for scientific applications.

The first study modeling the mechanism of hydrated electron formation from aqueous iodide.

Researchers unveil user-friendly software to keep complex projects on budget and on schedule.

PNNL researchers present challenges and opportunities for fusing graph neural networks with deep reinforcement learning.

PNNL and University of Texas at El Paso leverage partnership and joint appointment program for a laboratory directed research and development project.