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.
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.
A paper from PNNL and Southern California Edison describing new methodologies for assessing electric vehicle impacts to the grid was selected as a best paper by IEEE.
PNNL will demonstrate how new technologies, innovative approaches and partnering with others can lead to net-zero emissions and decarbonization of operations.
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.
Working on puzzles with her grandpa helped instill Emilie Purvine’s interest in math from an early age. That interest later turned to being co-captain for her high school math team, a degree in mathematics, and eventually a career at PNNL.
