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What Can Text Mining Tell Us About Lithium‐Ion Battery Researchers’ Habits?
Author(s) -
ElBousiydy Hassna,
Lombardo Teo,
Primo Emiliano N.,
Duquesnoy Marc,
Morcrette Mathieu,
Johansson Patrik,
Simon Patrice,
Grimaud Alexis,
Franco Alejandro A.
Publication year - 2021
Publication title -
batteries and supercaps
Language(s) - English
Resource type - Journals
ISSN - 2566-6223
DOI - 10.1002/batt.202000288
Subject(s) - battery (electricity) , computer science , lithium (medication) , data science , lithium ion battery , quality (philosophy) , order (exchange) , artificial intelligence , psychology , business , psychiatry , physics , power (physics) , quantum mechanics , finance
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.