Autonomous Discovery of Battery Electrolytes with Robotic Experimentation and Machine-Learning

20 November 2019, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Innovations in batteries take years to formulate, requiring extensive experimentation during the design and optimization phases. We approach the design of a battery electrolyte as a black-box optimization problem. We report here the discovery of a novel battery electrolyte by a robotic electrolyte experiment guided by machine-learning software. 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. Dragonfly autonomously managed the robotic test-stand, recommending electrolyte designs to test and receiving experimental feed- back in real time. Within 40 hours of continuous experimentation, Dragonfly discovered a novel, high-performing aqueous sodium electrolyte that a human-guided design process may have missed. This result demonstrates the possibility of integrating robotics with machine-learning to rapidly and autonomously discover novel battery materials.

Keywords

Autonomous discovery
Aqueous Electrolyte Solution
Concentrated Electrolytes
High Voltage Electrolytes

Supplementary materials

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ML Otto SI
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ML Otto extended
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