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29 March 2024 |
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Article overview
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Autonomous discovery of battery electrolytes with robotic experimentation and machine-learning | Adarsh Dave
; Jared Mitchell
; Kirthevasan Kandasamy
; Sven Burke
; Biswajit Paria
; Barnabas Poczos
; Jay Whitacre
; Venkatasubramanian Viswanathan
; | Date: |
22 Oct 2019 | Abstract: | 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. Dragonfly autonomously managed the robotic
test-stand, recommending electrolyte designs to test and receiving experimental
feedback in real time. In 40 hours of continuous experimentation over a
four-dimensional design space with millions of potential candidates, Dragonfly
discovered a novel, mixed-anion aqueous sodium electrolyte with a wider
electrochemical stability window than state-of-the-art sodium electrolyte. A
human-guided design process may have missed this optimal electrolyte. This
result demonstrates the possibility of integrating robotics with
machine-learning to rapidly and autonomously discover novel battery materials. | Source: | arXiv, 2001.9938 | Services: | Forum | Review | PDF | Favorites |
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