PNNL researchers have shown an improved binarized neural network can deliver a low-cost and low-energy computation to help the performance of smart devices and the power grid.

The project received an Innovative and Novel Computational Impact on Theory and Experiment (INCITE) award, a highly competitive U.S. Department of Energy Office of Science program.

A new report outlines future research paths that are needed for airlines to reduce carbon emissions and notes that the only way to achieve emission reduction goals is with Sustainable Aviation Fuels.

A nanosized metal catalyst directly bonded to a metal-organic framework is active in converting carbon dioxide to methanol.

PNNL data scientists Ján Drgoňa and Draguna Vrabie are working to reduce deployment costs of advanced energy control systems for buildings.

Researchers developed a new tool that combines artificial intelligence and microscopy data to rapidly test new materials for fusion reactor use.

PNNL and Microsoft Quantum partner to link quantum circuits to powerful government supercomputers.

PNNL bioenergy expert Dan Gaspar has been selected to assume leadership of DOE’s Co-Optimization of Fuels and Engines initiative.

Pacific Northwest National Laboratory researchers developed a graphical processing unit (GPU)-centered quantum computer simulator that can be 10 times faster than any other quantum computer simulator.

Researchers at PNNL have developed a bacteria testing system called OmniScreen that combines biological and synthetic chemistry with machine learning to hunt down pathogens before they strike.

PNNL scientists have developed a catalyst that converts ethanol into C5+ ketones that can serve as the building blocks for everything from solvents to jet fuel.

PNNL’s new Smart Power Grid Simulator, or Smart-PGSim, combines high-performance computing and artificial intelligence to optimize power grid simulations without sacrificing accuracy.

PNNL researchers are contributing expertise and hydrothermal liquefaction technology to a project that intercepts harmful algal blooms from water, treats the water, and concentrates algae for transformation to biocrude.

A perspective article in the Journal of the American Chemical Society by a team of PNNL researchers shows the way forward to understand ammonia oxidation.

Infusing data science and artificial intelligence into electron microscopy could advance energy storage, quantum information science, and materials design.

NIH awarded $1.7 million to researchers from PNNL, WSU, and NREL to continue fundamental research into catalytic bias—a phenomenon in the protein environment that shifts the direction and speed of an enzyme’s catalytic reaction.

PNNL and the Georgia Institute of Technology vow to collaborate more impactfully and more often in a new MOU announced October 23, 2020.

PNNL has three small-scale spectroscopy devices that are speeding up the testing and analysis of candidate novel materials used in energy storage research and environmental remediation.

Tracking down nefarious users is just one example of work at PNNL’s Center for Advanced Technology Evaluation, a computing proving ground supported by DOE’s Advanced Scientific Computing Research program.

Pacific Northwest National Laboratory researchers used machine learning to explore the largest water clusters database, identifying—with the most accurate neural network—important information about this life-essential molecule.