Machine learning assisted synthesis of lithium-ion batteries cathode materials

Cited 1 time in webofscience Cited 0 time in scopus
  • Hit : 48
  • Download : 0
Optimizing synthesis parameters is crucial in fabricating an ideal cathode material; however, the design space is too vast to be fully explored using an Edisonian approach. Here, by clustering eleven domain-expert-deriveddescriptors from literature, we use an inverse design surrogate model to build up the experimental parameters-property relationship. Without struggling with the trial-and-error method, the model enables design variables prediction that serves as an effective strategy for cathode retrosynthesis. More importantly, not only did we overcome the data scarcity problem, but the machine learning model has guided us to achieve cathode with high discharge capacity and Coulombic efficiency of 209.5 mAh/g and 86%, respectively. This work demonstrates an inverse design-to-device pipeline with unprecedented potential to accelerate the discovery of highenergy-density cathodes.
Publisher
ELSEVIER
Issue Date
2022-07
Language
English
Article Type
Article
Citation

NANO ENERGY, v.98

ISSN
2211-2855
DOI
10.1016/j.nanoen.2022.107214
URI
http://hdl.handle.net/10203/296890
Appears in Collection
MS-Journal Papers(저널논문)CH-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 1 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0