Autonomous discovery of battery electrolytes with robotic experimentation and machine learning – Physics World

Join the audience for a live webinar at 6 p.m. BST/1 p.m. EST on 12 August 2020 on the discovery of a novel battery electrolyte that was guided by machine-learning software without human intervention Want to take part in this webinar? Innovations in batteries take years to formulate and commercialize, requiring extensive experimentation during the design and optimization phases. We approached the design and selection of a battery electrolyte through a black-box optimization algorithm directly integrated into a robotic test stand. We report here the discovery of a novel battery electrolyte by this experiment completely guided by the machine-learning software without human intervention. Motivated by the recent trend toward super-concentrated aqueous electrolytes for high-performance batteries, we utilize Dragonfly – a Bayesian machine-learning software package – to search mixtures of commonly used lithium and sodium salts for super-concentrated aqueous electrolytes with wide electrochemical stability windows.