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.

standards

battery

text mining

reproducibility crisis

artificial intelligence

Författare

Hassna El-Bousiydy

Centre national de la recherche scientifique (CNRS)

Université de Picardie Jules Verne

Teo Lombardo

Centre national de la recherche scientifique (CNRS)

Université de Picardie Jules Verne

Emiliano N. Primo

Centre national de la recherche scientifique (CNRS)

Université de Picardie Jules Verne

Marc Duquesnoy

Université de Picardie Jules Verne

Centre national de la recherche scientifique (CNRS)

Mathieu Morcrette

Université de Picardie Jules Verne

Centre national de la recherche scientifique (CNRS)

Patrik Johansson

Chalmers, Fysik, Materialfysik

Patrice Simon

Institut Universitaire de France

Centre national de la recherche scientifique (CNRS)

Universite Paul Sabatier Toulouse III

Alexis Grimaud

Centre national de la recherche scientifique (CNRS)

Collège de France

Observatoire de Paris

Alejandro A. Franco

Centre national de la recherche scientifique (CNRS)

Université de Picardie Jules Verne

Institut Universitaire de France

BATTERIES & SUPERCAPS

2566-6223 (eISSN)

Vol. In Press

Ämneskategorier

Kommunikationssystem

Datavetenskap (datalogi)

Datorsystem

DOI

10.1002/batt.202000288

Mer information

Senast uppdaterat

2021-03-17