4.6 Article

What Can Text Mining Tell Us About Lithium-Ion Battery Researchers' Habits?

Journal

BATTERIES & SUPERCAPS
Volume 4, Issue 5, Pages 758-766

Publisher

WILEY-V C H VERLAG GMBH
DOI: 10.1002/batt.202000288

Keywords

artificial intelligence; battery; reproducibility crisis; standards; text mining

Funding

  1. ALISTORE European Research Institute
  2. European Union's Horizon 2020 research and innovation programme through the European Research Council (ARTISTIC project) [772873]
  3. Institut Universitaire de France
  4. Swedish Energy Agency Batterifondsprogrammet
  5. French National Research Agency through the Labex STORE-EX project [ANR-10LABX-76-01]

Ask authors/readers for more resources

Artificial Intelligence has the potential to revolutionize battery R&D by accelerating material discovery, but its success depends on access to high-quality data. Scientific publications may provide valuable data for developing reliable AI algorithms, but challenges still need to be addressed.
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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available