Designing viable molecular candidates is pivotal to devising low-cost and sustainable storage systems. A reinforcement learning framework has been developed that can identify stable candidates for redox flow batteries in the large search space of organic radicals.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$119.00 per year
only $9.92 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Li, Z. & Lu, Y.-C. Adv. Mater. 32, 2002132 (2020).
S. V., S. S. et al. Nat. Mach. Intell. https://doi.org/10.1038/s42256-022-00506-3 (2022).
Huskinson, B. et al. Nature 505, 195–198 (2014).
Hollas, A. et al. Nat. Energy 3, 508–514 (2018).
S. V., S. S. et al. Chem. Sci. 12, 13158–13166 (2021).
Sanchez-Lengeling, B. & Aspuru-Guzik, A. Science 361, 360–365 (2018).
Yang, X., Wang, Y., Byrne, R., Schneider, G. & Yang, S. Chem. Rev. 119, 10520–10594 (2019).
Zhavoronkov, A. et al. Nat. Biotechnol. 37, 1038–1040 (2019).
De Luna, P., Wei, J., Bengio, Y., Aspuru-Guzik, A. & Sargent, E. Nature 552, 23–27 (2017).
Seifrid, M., Hattrick-Simpers, J., Aspuru-Guzik, A., Kalil, T. & Cranford, S. Matter 5, 1972–1976 (2022).
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Rights and permissions
About this article
Cite this article
Cao, Y., Ser, C.T., Skreta, M. et al. Reinforcement learning supercharges redox flow batteries. Nat Mach Intell 4, 667–668 (2022). https://doi.org/10.1038/s42256-022-00523-2
Published:
Issue Date:
DOI: https://doi.org/10.1038/s42256-022-00523-2
