| Abstract Scope |
The Energy Storage Research Alliance (ESRA, https://energystoragera.org/) aims at tackling fundamental science challenges in beyond-Li-ion energy storage systems. The Materials Acceleration Platform (MAP) crosscut in ESRA leverages the advances in artificial intelligence/machine learning (AI/ML) and automation to accelerate the discovery, synthesis, and characterization of new and existing energy storage materials. We use AI/ML to enable and accelerate physics-constrained information extraction from characterization, combine computational discovery with autonomous synthesis, create autonomous electrochemical characterization laboratories, and make possible large scale predictions of dynamic systems with ML interatomic potentials (MLIPs). We will discuss efforts in ESRA-MAP to allow extraction of new insights from microscopy and spectroscopy data, to accelerate the design and synthesis of new solid state ion conductors, and to simulate complex reactions at interfaces.
This work is funded by ESRA, an Energy Innovation Hub funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences. |