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

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

Two new publications provide emergency response agencies with critical insights into commercially available unmanned ground vehicles used for hazardous materials response.

The Wildfire Mitigation Plan Database was built to support electric utilities, state governments, policymakers, and regulators in understanding and improving wildfire risk and resilience strategies.

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

A new database allows researchers to connect lattice parameters and composition in olivine.

New methodology can evaluate the potential for carbon storage and critical mineral recovery in undercharacterized geological settings.

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.

Data from SOSAT web tool informs site selection and management for the subsurface injection of captured carbon dioxide.

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

To improve our ability to “see” into the subsurface, scientists need to understand how different mineral surfaces respond to electrical signals at the molecular scale.

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

The SHASTA program is doing a deep dive on subsurface hydrogen storage in underground caverns, helping to lay the foundation for a robust hydrogen economy.

PNNL research unlocks key findings needed for potential commercial deployment of carbon mineralization technology.

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

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.

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

PNNL researchers outline open problems in different scientific areas in the Journal of Combinatorics.

Methods review suggests ways to use artificial intelligence and machine learning to handle missing data when integrating two or more omics approaches.