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.
A breakthrough in electron microscopy based on deep learning can automatically visualize and identify areas of interest, helping to speed advances in materials science.
The next-generation ShAPE machine has arrived at PNNL, where it will help prove the mettle of the ShAPE extrusion technique. ShAPE 2 is designed to allow researchers to produce larger, more complex extrusions.
Researchers from PNNL have been assessing installation and use of electric heat pumps in an Alaskan community that relies on fuel oil for heat. The resulting information could advance electrification in cold rural areas across the nation.
A new AI model developed at PNNL can identify patterns in electron microscope images of materials without requiring human intervention, allowing for more accurate and consistent materials science.
Published in Nature Communications, Increased Asian Aerosols Drive a Slowdown of Atlantic Meridional Overturning Circulation, identifies the role aerosols over Asia is having on the AMOC, a complex system of currents in the Atlantic Ocean.
Researchers use models to represent relationships between climate and socio-economic processes, helping inform decisions for slowing climate change and enhancing resilience.
Battery energy storage systems are being proposed in municipalities across the U.S. PNNL researchers can help community planners guide safe siting and operations.
The use of disciplines in pure mathematics can increase the reliability and explainability of machine learning models that “transcend human intuition,” according to PNNL scientists.