What Can Text Mining Tell Us About Lithium-Ion Battery Researchers' Habits?
Journal article, 2021
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.
text mining
artificial intelligence
standards
battery
reproducibility crisis
Author
Hassna El-Bousiydy
Centre national de la recherche scientifique (CNRS)
University of Picardie Jules Verne
Teo Lombardo
University of Picardie Jules Verne
Centre national de la recherche scientifique (CNRS)
Emiliano N. Primo
University of Picardie Jules Verne
Centre national de la recherche scientifique (CNRS)
Marc Duquesnoy
University of Picardie Jules Verne
Centre national de la recherche scientifique (CNRS)
Mathieu Morcrette
Centre national de la recherche scientifique (CNRS)
University of Picardie Jules Verne
Patrik Johansson
Chalmers, Physics, Materials Physics
Patrice Simon
Paul Sabatier University
Institut Universitaire de France
Centre national de la recherche scientifique (CNRS)
Alexis Grimaud
Paris Observatory
Collège de France
Centre national de la recherche scientifique (CNRS)
Alejandro A. Franco
Centre national de la recherche scientifique (CNRS)
Institut Universitaire de France
University of Picardie Jules Verne
Batteries and Supercaps
25666223 (eISSN)
Vol. 4 5 758-766Subject Categories
Communication Systems
Computer Science
Computer Systems
DOI
10.1002/batt.202000288