A PNNL-developed computational framework accurately predicts the thermomechanical history and microstructure evolution of materials designed using solid phase processing, allowing scientists to custom design metals with desired properties.
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.
Research published in Journal of Manufacturing Processes demonstrates innovative single-step method to manufacture oxide dispersion strengthened copper materials from powder.
Tiffany Kaspar’s work has advanced the discovery and understanding of oxide materials, helping develop electronics, quantum computing, and energy production. She strives to communicate her science to the public.
PNNL researchers developed a hybrid quantum-classical approach for coupled-cluster Green’s function theory that maintains accuracy while cutting computational costs.
A comprehensive understanding of the electronic structure of uranyl ions provides insight into the chemistry of nuclear waste and uranium separation technologies.
Developed at PNNL, Shear Assisted Processing and Extrusion, or ShAPE™, uses significantly less energy and can deliver components like wire, tubes and bars 10 times faster than conventional extrusion, with no sacrifice in quality.
