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- W3216918789 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 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." @default.
- W3216918789 created "2021-12-06" @default.
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- W3216918789 date "2021-11-23" @default.
- W3216918789 modified "2023-09-26" @default.
- W3216918789 title "Autonomous optimization of nonaqueous battery electrolytes via robotic experimentation and machine learning." @default.
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