EZBattery Model allows energy storage researchers to more quickly and easily identify the best performing battery designs without the need for extensive physical prototyping or computationally expensive simulations.
To improve our ability to “see” into the subsurface, scientists need to understand how different mineral surfaces respond to electrical signals at the molecular scale.
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.