Accelerating aqueous electrolyte design with automated full-cell battery experimentation and Bayesian optimization
(2025) In Cell Reports Physical Science 6(5).- Abstract
The integration of automation and data-driven methodologies offers a promising approach to accelerating materials discovery in energy storage research. Thus far, in battery research, coin-cell assembly has advanced to become nearly fully automated but remains largely disconnected from data-driven methods. To bridge the disconnect, this work presents a self-driving laboratory framework to accelerate electrolyte discovery by integrating automated coin-cell assembly, galvanostatic cycling of LiFePO4||Li4Ti5O12 organic-aqueous full cells, and Bayesian optimization for selecting subsequent experiments based on prior results. The study explored an organic-aqueous hybrid electrolyte system comprising... (More)
The integration of automation and data-driven methodologies offers a promising approach to accelerating materials discovery in energy storage research. Thus far, in battery research, coin-cell assembly has advanced to become nearly fully automated but remains largely disconnected from data-driven methods. To bridge the disconnect, this work presents a self-driving laboratory framework to accelerate electrolyte discovery by integrating automated coin-cell assembly, galvanostatic cycling of LiFePO4||Li4Ti5O12 organic-aqueous full cells, and Bayesian optimization for selecting subsequent experiments based on prior results. The study explored an organic-aqueous hybrid electrolyte system comprising four co-solvents and two lithium-conducting salts. Using this framework, cells with an optimized electrolyte cycled with at least 94% Coulombic efficiency. Additionally, online electrochemical mass spectrometry revealed that the optimized organic co-solvents successfully mitigated the parasitic hydrogen evolution reaction. The results highlight the potential of combining Bayesian optimization with autonomous full-cell experimentation while contributing new electrolyte design insights for next-generation aqueous batteries.
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- author
- Yik, Jackie T. ; Hvarfner, Carl LU ; Sjölund, Jens ; Berg, Erik J. and Zhang, Leiting
- organization
- publishing date
- 2025-05
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- aqueous batteries, automation, Bayesian optimization, high throughput, operando gas analysis, self-driving labs
- in
- Cell Reports Physical Science
- volume
- 6
- issue
- 5
- article number
- 102548
- publisher
- Cell Press
- external identifiers
-
- scopus:105002782717
- ISSN
- 2666-3864
- DOI
- 10.1016/j.xcrp.2025.102548
- language
- English
- LU publication?
- yes
- id
- 26453d6c-0870-42e4-b994-085c875f34a6
- date added to LUP
- 2025-09-01 10:31:55
- date last changed
- 2025-09-01 10:32:16
@article{26453d6c-0870-42e4-b994-085c875f34a6,
abstract = {{<p>The integration of automation and data-driven methodologies offers a promising approach to accelerating materials discovery in energy storage research. Thus far, in battery research, coin-cell assembly has advanced to become nearly fully automated but remains largely disconnected from data-driven methods. To bridge the disconnect, this work presents a self-driving laboratory framework to accelerate electrolyte discovery by integrating automated coin-cell assembly, galvanostatic cycling of LiFePO<sub>4</sub>||Li<sub>4</sub>Ti<sub>5</sub>O<sub>12</sub> organic-aqueous full cells, and Bayesian optimization for selecting subsequent experiments based on prior results. The study explored an organic-aqueous hybrid electrolyte system comprising four co-solvents and two lithium-conducting salts. Using this framework, cells with an optimized electrolyte cycled with at least 94% Coulombic efficiency. Additionally, online electrochemical mass spectrometry revealed that the optimized organic co-solvents successfully mitigated the parasitic hydrogen evolution reaction. The results highlight the potential of combining Bayesian optimization with autonomous full-cell experimentation while contributing new electrolyte design insights for next-generation aqueous batteries.</p>}},
author = {{Yik, Jackie T. and Hvarfner, Carl and Sjölund, Jens and Berg, Erik J. and Zhang, Leiting}},
issn = {{2666-3864}},
keywords = {{aqueous batteries; automation; Bayesian optimization; high throughput; operando gas analysis; self-driving labs}},
language = {{eng}},
number = {{5}},
publisher = {{Cell Press}},
series = {{Cell Reports Physical Science}},
title = {{Accelerating aqueous electrolyte design with automated full-cell battery experimentation and Bayesian optimization}},
url = {{http://dx.doi.org/10.1016/j.xcrp.2025.102548}},
doi = {{10.1016/j.xcrp.2025.102548}},
volume = {{6}},
year = {{2025}},
}