A breakthrough in electron microscopy based on deep learning can automatically visualize and identify areas of interest, helping to speed advances in materials science.
Research from PNNL and the University of Washington demonstrates the extension of the MBE for periodic systems and its use to decompose the lattice energies of different ice polymorphs.
The work by the team at PNNL takes a critical step in leveraging ML to accelerate advanced manufacturing R&D, specifically for manufacturing techniques without access to efficient, first-principles simulations.
PNNL researchers developed a hybrid quantum-classical approach for coupled-cluster Green’s function theory that maintains accuracy while cutting computational costs.
New mathematical tools developed at PNNL hold promise to transform the way we operate and defend complex cyber-physical systems, such as the power grid.
Contributions from researchers across Pacific Northwest National Laboratory (PNNL) were recently recognized in the preliminary findings of a Secretary of Energy Advisory Board (SEAB) report.
July in the Tri-Cities usually brings sunny skies, hot weather and high demand for electricity as many of us retreat to air-conditioned homes and offices.
Cybersecurity is increasingly top-of-mind and in the news. Individuals worry about identify theft and the compromise of financial and medical records. And the federal government battles myriad threats aimed at our national security.
At the Department of Energy's Pacific Northwest National Laboratory, we are developing sophisticated mathematical techniques and software tools to securely manage and analyze vast amounts of data.