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.
Scientists are pioneering approaches in the branch of artificial intelligence known as machine learning to design and train computer software programs that guide the development of new manufacturing processes.
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.
PNNL's Tegan Emerson was invited to be one of two plenary speakers at the inaugural AIM 2022 congress. The Minerals, Metals & Materials Society organized AIM 2022 to connect materials and manufacturing researchers from around the world.