A scenario approach was used to explore the potential future role of hydropower around the globe considering the multisectoral dynamics of regional energy systems and basin-specific water resources.

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 new report, based on a community workshop and literature review, summarizes some of the biggest challenges in understanding and modeling Earth system and human–Earth system dynamics in the Puget Sound region of Washington State.

Building new climate outcome scenarios by stitching together pieces of existing ones.

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.

Researchers found that specific landscape metrics consistently outperform traditional population downscaling methods.

A systematic, multiple scenario approach was used to analyze the compounding impacts of demands for land for biofuels with increased land scarcity under a diverse set of uncertainties.

rmap enables users with limited to no geographic information system knowledge use R to visualize simple tabular data.

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

The methods researchers use to estimate crop yield gaps determines the location of projected future agricultural “breadbasket” regions.

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.

Better representing electric capacity markets, economic retirements, and power-plant age structure provides a more robust understanding of the future evolution of the electric sector.

MSD-LIVE, a cloud-based data and code management system, will be released in summer 2022 for hundreds of scientists.

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

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

An improved model captures the interactions between the U.S. economy, energy, water, and land systems with increased spatial and temporal detail.

A climate change study co-authored by PNNL scientist Hailong Wang inspired an artist to create an exhibit based on the research.

PNNL contributes to 30 years of data on clouds, radiation, and other climate-making factors as part of field campaigns and analysis conducted by DOE's Atmospheric Radiation Measurement user facility.

New work developed a methodology to generate building temperature setpoint schedules for use by modelers in large-scale building energy simulations.

Storage Targets and Release Function Inference Tool uses data to infer the operating policies of large reservoirs on a national scale.