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Autonomous discovery of battery electrolytes with robotic experimentation and machine learning

Available to watch now, The Electrochemical Society in partnership with the Royal Society of Chemistry on the discovery of a novel battery electrolyte that was guided by machine-learning software without human intervention

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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.

This webinar presented by Venkat Viswanathan, will help the audience to:

  • Learn about the importance of robotic experimentation
  • Learn about machine-learning guided design of experiments
  • Learn about the frontier of remote experimentation

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Venkat Viswanathan is an Associate Professor of Mechanical Engineering at Carnegie Mellon University. He received his PhD from Stanford University working on lithium-air batteries. His current research focus is on understanding and developing novel electrochemical devices for energy storage and utilization.

 

 

 

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