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

PNNL researchers have developed a new, physics-informed machine learning model that accurately predicts how heat accumulates and dissipates during friction stir processing.

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.

This is the first time a flow battery with 24-hour discharge capacity will be deployed and tested in a field environment.

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

HiSVSIM enables large-scale quantum simulations through graph partitioning.

A new version of the Department of Energy’s Technical Resilience Navigator allows users to prioritize resilience solutions based on both risk reduction and emissions impact.

Researchers studied water cycling to assess the achievability of net-zero water usage at military installations in the Pacific Islands.