Machine learning techniques are accelerating the development of stronger alloys for power plants, which will yield efficiency, cost, and decarbonization benefits.
Marcel Baer is a computational scientist working in PNNL’s Physical Sciences Division with a prominent effort in materials science and physical bioscience.
With quantum chemistry, researchers led by PNNL computational scientist Simone Raugei are discovering how enzymes such as nitrogenase serve as natural catalysts that efficiently break apart molecular bonds to control energy and matter.
PNNL teamed with academia and industry to develop a novel zero-emission methane pyrolysis process that produces both hydrogen and high-value carbon solids suitable for an array of manufacturing applications.
PNNL’s newest solvent captures carbon dioxide from power plants for as little as $47.10 per metric ton, marking a significant milestone in the journey to lower the cost of carbon capture.
A research team from Pacific Northwest National Laboratory developed an apparatus that evaluates the performance of high-temperature fluids in hydraulic fracturing for enhanced geothermal systems.
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.
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.
In a new review, PNNL researchers outline how to convert stranded biomass to sustainable fuel using electrochemical reduction reactions in mini-refineries powered by renewable energy.