Researchers show application of a causal model better identifies direct and indirect causal relations compared to correlation and random forest analyses performed over the same dataset.

Researchers introduce revised emission treatment that conserves mass and preserves the original heterogeneity.

PNNL shared recent developments in artificial intelligence at the inaugural Special Competitive Studies Project AI for National Competitiveness Expo.

PNNL’s Nell Byler and Elise Bishoff shared their computer vision expertise with high school students at the National Science Bowl Science Day

Researchers are leveraging the ocean’s natural processes to advance approaches to remove carbon dioxide from the atmosphere and store it in the ocean.

Policy changes in power, energy, buildings, and more could help slow global temperature rise, according to a new report with co-authors from PNNL’s Joint Global Change Research Institute.

Research at PNNL and the University of Texas at El Paso are addressing computational challenges of thinking beyond the list and developing bioagent-agnostic signatures to assess threats.

PNNL’s Andrew Engel, Tony Chiang, and Sutanay Choudhury at the 2024 ICLR in Vienna.

A new study suggests accessing groundwater in key regions could become prohibitively expensive as lower-cost stores are used up.

Five PNNL scientists participated in an invitation-only Department of Energy-sponsored workshop on coastal systems. 

New work finds hurricanes are intensifying faster close to the coast—a trend that’s likely to ramp up in a warmer world.

With Earth system models running at higher resolutions, researchers evaluated the sensitivity of aerosol properties to horizontal grid spacing.

This study demonstrates a new model that integrates complex organic matter (OM) chemistry and multiple electron acceptors to predict kinetic rates of OM oxidation.

PNNL computing experts Robert Rallo and Court Corley contribute their knowledge to a recent DOE report on applications of AI to energy, materials, and the power grid.

A breakthrough in electron microscopy based on deep learning can automatically visualize and identify areas of interest, helping to speed advances in materials science.

Study reviews the loss of carbon dioxide from soils and explores most compelling challenges and opportunities for the future.

A set of papers combines surface science measurements on tiny particles with uncrewed aerial systems to better understand and model aerosols.

Study demonstrates that choosing more accurate numerical process coupling helps improve simulation of dust aerosol life cycle in a global climate model.

Study introduces a mathematically rigorous analysis framework that provides insight into the features and implications of coupling method choices.

A new model accounts for co-occurring environmental parameters to improve predictions of a hurricane’s intensification.