Scientists at PNNL harnessing advances in deep learning, deep reinforcement learning and generative AI to change how science is conducted and achieve original scientific results and breakthroughs.

PNNL publishes Port Electrification Handbook to aid the nation’s maritime and commercial ports during this energy transition.

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.

Visit featured information exchange and tours of PNNL work and facilities that relate to the work of the International Atomic Energy Agency.

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

New research sheds light on the biomolecular dangers of night shift work.

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

Data scientist at PNNL receives the Environmental and Engineering Geophysical Society and Geonics Limited Early Career Award for work with geophysical modeling and subsurface inversion codes.

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.

Vigorous exercise burns fat more in males than in females, but the benefits of exercise are broad for both sexes.

PNNL contributes to a nationwide research consortium investigating the molecular mechanisms triggered by endurance training.

Study explores Exploration of Coastal Hydrobiogeochemistry Across a Network of Gradients and Experiments, a consortium of scientists interested in the exchange between water and land in coastal systems.

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.

New research informs the development of foundational models for computational chemistry through hardware-software co-design.