Machine learning methods and data science can uncover hidden patterns in nanoparticle synthesis conditions and property outcomes.

Soil is a massive reservoir of carbon, holding three times the amount of carbon than in the atmosphere. Soil is a massive reservoir of carbon, holding three times the amount of carbon than in the atmosphere.

A suite of experiments provide insight into how diopside reacts with carbon dioxide to form stable carbonate species.

Across the United States, organic carbon concentration imposes a primary control on river sediment respiration, with additional influences from organic matter chemistry.

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

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

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

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.

A review article examines a decade of work on the atom-scale process of turning carbon dioxide into stable minerals.

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

Providing a perspective on the importance of integrating experimental work with theory and simulations when designing new carbon capture solvents.

The PNNL-developed Eclipse VOLTTRON™ software platform continues to support new applications in energy efficiency and clean energy.

PNNL will provide technical resources and support to a national coalition of states and cities focused on implementing building performance standards to improve energy efficiency and reduce carbon emissions.

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

Modeling study suggests vigorous ventilation can cause spike in viral concentrations

PNNL researchers have developed a new class of chemistry focused on carbon capture technology and made incremental strides in driving down costs.

A code comparison study demonstrates a leap in the accuracy and reliability of modeling gas hydrate for the discovery and production of natural gas.

Researchers developed a new tool that combines artificial intelligence and microscopy data to rapidly test new materials for fusion reactor use.