What Can Text Mining Tell Us About Lithium‐Ion Battery Researchers’ Habits?
Hassna El‐Bousiydy, Teo Lombardo, Emiliano N. Primo, Marc Duquesnoy, Mathieu Morcrette, Patrik Johansson, Patrice Simon, Alexis Grimaud, Alejandro A. Franco
Abstract
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.