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.

Microbes that were previously frozen in soils are becoming more active. This study demonstrates the diverse RNA viral communities found in thawed permafrost.

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.