PNNL and The University of Texas at El Paso expand their premier partnership to include the Cyber Halo Innovation Research Program.

The popular approach of organizing soil bacteria into fast- or slow-growing groups is problematic because most bacteria grow at comparable rates in soil.

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

Under Secretary for Science and Innovation at the Department of Energy Geri Richmond visited PNNL.

SAGE is a high-efficiency genome integration strategy for bacteria that makes the stable introduction of new traits simple for newly discovered microbes.

Advances in understanding the nature of the chemical bond are featured in this special issue of The Journal of Chemical Physics.

Variations in burn severity are a key control on the chemical constituents of dissolved organic matter delivered to streams within a single burn perimeter.

First-of-its-kind genomic catalog lends insight into previously unknown processes at the river’s edge.

Deep learning helps make predictions on the effects of the El Niño-Southern Oscillation on river flows more accurate.

A PNNL innovation uses steam to recover heat from the high-temperature reactor effluent in the HTL process, substantially reducing the propensity for fouling and potentially reducing costs.

Department of Energy, Office of Science Director Asmeret Asefaw Berhe visited PNNL to learn about the Lab’s drive to conduct discovery science, commitment to science for an equitable future, and development of a diversified STEM workforce.

A multi-omics analysis provides the framework for gaining insights into the structure and function of microbial communities across multiple habitats on a planetary scale

A rich and largely untapped reservoir of lipids in soil environments was used to examine microorganisms’ physiological responses to drying-rewetting cycles.

Snekmer is an application for building and searching protein family models and novel sequence clusters.

Machine learning models help identify important environmental properties that influence how often extreme rain events occur with critical intensity and duration.

PNNL researchers discovered a faster, more economical method to verify the amount of biofuel in mixtures of bio and fossil fuel.

PNNL researchers combined deep operator networks with spectral methods to efficiently and accurately solve complex equations.

Data-driven autonomous technology to rapidly design and deliver antiviral interventions targeting SARS-CoV-2 to reduce drug discovery timeline and advance bio preparedness capabilities.

A team from the Environmental Molecular Sciences Laboratory published research, demonstrating that the soil microbes were directly involved in the stabilization of soil organic carbon and mineral weathering.

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.