(Invited) Energy Storage Research Alliance - Accelerating Materials Discovery, Synthesis, Characterization, and Understanding with AI/ML
TL;DR
This work uses AI to enable and accelerate physics-constrained information extraction from characterization, combine computational discovery and design with autonomous synthesis with closed-loop feedback, create autonomous electrochemical characterization laboratories, and make possible large scale predictions of dynamic systems with ML interatomic potentials (MLIPs).
Abstract
The Energy Storage Research Alliance (ESRA, https://energystoragera.org/) aims at tackling fundamental science challenges in beyond-Li-ion energy storage systems. Underlying the scientific thrusts are the crosscutting efforts which aim at developing cutting-edge capabilities. In particular, the Materials Acceleration Platform (MAP) crosscut leverages and contributes towards 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, especially for Na and Zn. Under the MAP crosscut, we use AI to enable and accelerate physics-constrained information extraction from characterization, combine computational discovery and design with autonomous synthesis with closed-loop feedback, 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 the Energy Storage Research Alliance "ESRA" (DE-AC02-06CH11357), an Energy Innovation Hub funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences.