The Generator Scorecard, developed by PNNL in partnership with BPA, automates generator evaluations, reducing engineering workloads and improving grid reliability.

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

PNNL computing experts Robert Rallo and Court Corley contribute their knowledge to a recent DOE report on applications of AI to energy, materials, and the power grid.

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.

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

ExaGO, a power grid simulation and optimization platform developed at PNNL is the first of its kind to run on Frontier.

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

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

COVID-19 had multiple impacts on the electric sector and the nature of those impacts varied on the scale analyzed.