Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling.

ABSTRACT: Developing high-energy and efficient battery technologies is a crucial aspect of advancing the electrification of transportation and aviation. However, battery innovations can take years to deliver. In the case of non-aqueous battery electrolyte solutions, the many design variables in selecting multiple solvents, salts and their relative ratios make electrolyte optimization time-consuming and laborious. To overcome these issues, we propose in this work an experimental design that couples robotics (a custom-built automated experiment named "Clio") to machine-learning (a Bayesian optimization-based experiment planner named "Dragonfly"). An autonomous optimization of the electrolyte conductivity over a single-salt and ternary solvent design space identifies six fast-charging non-aqueous elect

SUBMITTER: Dave A 

PROVIDER: S-EPMC9515088 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature