What Can Text Mining Tell Us About Lithium-Ion Battery Researchers' Habits?
Artikel i vetenskaplig tidskrift, 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

Författare

Hassna El-Bousiydy

Centre national de la recherche scientifique (CNRS)

Université de Picardie Jules Verne

Teo Lombardo

Université de Picardie Jules Verne

Centre national de la recherche scientifique (CNRS)

Emiliano N. Primo

Université de Picardie Jules Verne

Centre national de la recherche scientifique (CNRS)

Marc Duquesnoy

Université de Picardie Jules Verne

Centre national de la recherche scientifique (CNRS)

Mathieu Morcrette

Centre national de la recherche scientifique (CNRS)

Université de Picardie Jules Verne

Patrik Johansson

Chalmers, Fysik, Materialfysik

Patrice Simon

Universite Paul Sabatier Toulouse III

Institut Universitaire de France

Centre national de la recherche scientifique (CNRS)

Alexis Grimaud

Observatoire de Paris

Collège de France

Centre national de la recherche scientifique (CNRS)

Alejandro A. Franco

Centre national de la recherche scientifique (CNRS)

Institut Universitaire de France

Université de Picardie Jules Verne

Batteries and Supercaps

25666223 (eISSN)

Vol. In Press

Ämneskategorier

Kommunikationssystem

Datavetenskap (datalogi)

Datorsystem

DOI

10.1002/batt.202000288

Mer information

Senast uppdaterat

2023-05-26