Skip to Main content Skip to Navigation
Journal articles

What Can Text Mining Tell Us About Lithium‐Ion Battery Researchers’ Habits?

Abstract : Artificial Intelligence (AI) has the promise of providing a paradigm shift in battery R&D by significantly accelerating the discovery and optimization of materials, interfaces, phenomena, and processes. However, the efficiency of any AI approach ultimately relies on rapid access to high-quality and interpretable large datasets. Scientific publications contain a tremendous wealth of relevant data and these can possibly, but not certainly, be used to develop reliable AI algorithms useful for battery R&D. To address this, we present here a text mining study wherein we unravel lithium-ion battery researchers' habits when reporting results, reason on how these habits link to issues of lacking reproducibility and discuss the remaining challenges to be tackled in order to develop a more credible and impactful AI for battery R&D.
Document type :
Journal articles
Complete list of metadata

https://hal.sorbonne-universite.fr/hal-03163309
Contributor : Hal Sorbonne Université Gestionnaire Connect in order to contact the contributor
Submitted on : Tuesday, March 9, 2021 - 11:01:52 AM
Last modification on : Wednesday, October 20, 2021 - 3:28:04 AM
Long-term archiving on: : Thursday, June 10, 2021 - 6:42:53 PM

File

batt.202000288.pdf
Publication funded by an institution

Identifiers

`

Citation

Hassna El‐bousiydy, Teo Lombardo, Emiliano Primo, Marc Duquesnoy, Mathieu Morcrette, et al.. What Can Text Mining Tell Us About Lithium‐Ion Battery Researchers’ Habits?. Batteries & Supercaps, Wiley, 2021, ⟨10.1002/batt.202000288⟩. ⟨hal-03163309⟩

Share

Metrics

Record views

324

Files downloads

428