The Center for Continuum Computing at PNNL aims to integrate cloud platforms, high-performance computing, and edge devices into a seamless ecosystem that accelerates scientific discovery.

PNNL’s Center for Cloud Computing is addressing challenges in data management and hybrid cloud solutions with industry and academic partnerships.

In a recent publication in Nature Communications, a team of researchers presents a mathematical theory to address the challenge of barren plateaus in quantum machine learning.

Three PNNL researchers will receive five continuous years of funding for projects through highly competitive DOE Early Career Research Program awards.

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

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

Reaching net-zero carbon emissions goals requires finding transformative paths to manage carbon in difficult-to-electrify economic sectors.

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

A streamlined informatics workflow that automatically processes complex data and provides the locations of double bonds in lipids.

Hi-C metagenomics-informed phage-host infection networks detected in soil pre- and post-drying.

Scientists have developed a better way to recognize denial-of-service internet attacks, improving detection by 90 percent.

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

PNNL researchers outline open problems in different scientific areas in the Journal of Combinatorics.

By mimicking nature, scientists make a breakthrough in breaking down wood.

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

Researchers have knitted together three large databases of information to help cybersecurity experts detect and prevent attacks.

Newly developed models can help rapidly optimize systems to meet the need for rapidly deployable commercial carbon capture.

Scientists have taken a key step toward harnessing a form of AI known as deep reinforcement learning to protect computer networks.

PNNL scientists carve a path to profit from carbon capture by creating a system that efficiently captures CO2 and converts it into one of the world’s most widely used chemicals: methanol.

HiSVSIM enables large-scale quantum simulations through graph partitioning.