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

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 ultra-low radiation cables to reduce background noise for highly sensitive neutrino and dark matter detectors.

PNNL’s Richard Saldanha will lead a new project to benchmark and validate models of cosmic ray exposure

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.