Researchers developed a machine learning model that can analyze chemical reactions as they happen in an electron microscope.

As leaders in AI and machine learning, PNNL experts are sharing their latest findings at the 36th annual Neural Information Processing Systems (NeurIPS) Conference, Nov. 28–Dec. 9, 2022.

Organismal contribution and community interaction drive soil organic matter breakdown by microbes.

Identifying a novel gene function on soil viruses.

Microbes and viruses play key roles in carbon and nitrogen cycling in riverbed sediments.

Proteins from “auxiliary” genes in soil viruses play a role in the carbon cycle and climate, scientists have found.

PNNL researchers teamed up with SambaNova to enhance artificial intelligence for scientific computing.

A process developed at PNNL that converts biomass and waste into a chemical intermediate or into gasoline, diesel, and jet fuel is available for commercial licensing.

A novel combination of temperature and four-dimensional geophysical sensors revealed otherwise unseeable hydrologic dynamics below riverbed sediments.

Forms of nitrogen released by nitrogen-fixing bacteria differ across sampling scales and in structured environments.

Dominant and functionally important soil microbes show strong, predictable, and distinctly different associations with continental-scale gradients in climate, vegetation, and soil moisture.

PNNL researchers design an artificial intelligence-guided electron microscope in a Laboratory Directed Research and Development study.

A novel ecological measurement uncovered interactions between river corridor organic matter assemblages and microbial communities, highlighting potentially important microbial taxa and molecular formula types.

New data analysis approach gives researchers deeper understanding of microbial ecology and evolution.

Study highlights microbiome’s role in regulating xenobiotic metabolism.

Study reveals importance of accounting for micrometer scale distribution of substrates to predict carbon emissions from soil.

A multi-institutional team has developed a deep learning framework to identify novel molecules as drug candidates.

Soil moisture influences both the activity of soil DNA viruses, as well as the composition and abundance of RNA viruses.

Knowing which bacteria in a community are involved with carbon cycling could help scientists predict how microbial carbon storage and release could influence future climate dynamics.

A new article reviews bio-orthogonal click chemistry reagents that can be used as probes to label metabolic pathways in plants and microbes.