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

Author

Listed:
  • Adarsh Dave

    (Carnegie Mellon University
    Carnegie Mellon University)

  • Jared Mitchell

    (Carnegie Mellon University
    Carnegie Mellon University)

  • Sven Burke

    (Carnegie Mellon University
    Carnegie Mellon University)

  • Hongyi Lin

    (Carnegie Mellon University
    Carnegie Mellon University)

  • Jay Whitacre

    (Carnegie Mellon University
    Carnegie Mellon University)

  • Venkatasubramanian Viswanathan

    (Carnegie Mellon University
    Carnegie Mellon University)

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 electrolyte solutions in two work-days and forty-two experiments. This result represents a six-fold time acceleration compared to a random search performed by the same automated experiment. To validate the practical use of these electrolytes, we tested them in a 220 mAh graphite∣∣LiNi0.5Mn0.3Co0.2O2 pouch cell configuration. All the pouch cells containing the robot-developed electrolytes demonstrate improved fast-charging capability against a baseline experiment that uses a non-aqueous electrolyte solution selected a priori from the design space.

Suggested Citation

  • Adarsh Dave & Jared Mitchell & Sven Burke & Hongyi Lin & Jay Whitacre & Venkatasubramanian Viswanathan, 2022. "Autonomous optimization of non-aqueous Li-ion battery electrolytes via robotic experimentation and machine learning coupling," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32938-1
    DOI: 10.1038/s41467-022-32938-1
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    1. Hongyuan Sheng & Jingwen Sun & Oliver Rodríguez & Benjamin B. Hoar & Weitong Zhang & Danlei Xiang & Tianhua Tang & Avijit Hazra & Daniel S. Min & Abigail G. Doyle & Matthew S. Sigman & Cyrille Costent, 2024. "Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    2. Juran Noh & Hieu A. Doan & Heather Job & Lily A. Robertson & Lu Zhang & Rajeev S. Assary & Karl Mueller & Vijayakumar Murugesan & Yangang Liang, 2024. "An integrated high-throughput robotic platform and active learning approach for accelerated discovery of optimal electrolyte formulations," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    3. Snehi Shrestha & Kieran James Barvenik & Tianle Chen & Haochen Yang & Yang Li & Meera Muthachi Kesavan & Joshua M. Little & Hayden C. Whitley & Zi Teng & Yaguang Luo & Eleonora Tubaldi & Po-Yen Chen, 2024. "Machine intelligence accelerated design of conductive MXene aerogels with programmable properties," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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