Autonomous optimization of nonaqueous battery electrolytes via robotic experimentation and machine learning

• URL: http://arxiv.org/abs/2111.14786v1
• Date: Tue, 23 Nov 2021 00:15:19 GMT
• Title: Autonomous optimization of nonaqueous battery electrolytes via robotic experimentation and machine learning
• Authors: Adarsh Dave, Jared Mitchell, Sven Burke, Hongyi Lin, Jay Whitacre and Venkatasubramanian Viswanathan
• Abstract summary: We introduce a novel workflow that couples robotics to machine-learning for efficient optimization of a non-aqueous battery electrolyte. A custom-built automated experiment named "Clio" is coupled to Dragonfly - a Bayesian optimization-based experiment planner. Clio autonomously optimize electrolyte conductivity over a single-salt, ternary solvent design space.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we introduce a novel workflow that couples robotics to machine-learning for efficient optimization of a non-aqueous battery electrolyte. A custom-built automated experiment named "Clio" is coupled to Dragonfly - a Bayesian optimization-based experiment planner. Clio autonomously optimizes electrolyte conductivity over a single-salt, ternary solvent design space. Using this workflow, we identify 6 fast-charging electrolytes in 2 work-days and 42 experiments (compared with 60 days using exhaustive search of the 1000 possible candidates, or 6 days assuming only 10% of candidates are evaluated). Our method finds the highest reported conductivity electrolyte in a design space heavily explored by previous literature, converging on a high-conductivity mixture that demonstrates subtle electrolyte chemical physics.

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