What Can Text Mining Tell Us About Lithium-Ion Battery Researchers' Habits?

被引：8

作者：

El-Bousiydy, Hassna

Lombardo, Teo

Primo, Emiliano N.

Duquesnoy, Marc

Morcrette, Mathieu

Johansson, Patrik

Simon, Patrice

Grimaud, Alexis

Franco, Alejandro A.

机构：

[1] Laboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules Verne, Hub de l'Energie, 15, rue Baudelocque, Amiens Cedex 1

[2] ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, Amiens Cedex 1

[3] Réseau sur le Stockage Electrochimique de l'Energie (RS2E), FR CNRS 3459, Hub de l'Energie 15, rue Baudelocque, Amiens Cedex 1

[4] Department of Physics, Chalmers University of Technology, Göteborg

[5] CIRIMAT, Université de Toulouse, CNRS, INPT, UPS, Université Toulouse 3 Paul Sabatier, Bât. CIRIMAT, 118, route de Narbonne, Toulouse cedex 9

[6] UMR CNRS 8260 “Chimie du Solide et Energie”, Collège de France, 11 Place Marcelin Berthelot, Paris Cedex 05

[7] Sorbonne Universités - UPMC Univ Paris 06, 4 Place Jussieu, Paris

[8] Institut Universitaire de France, 103 boulevard Saint Michel, Paris

来源：

BATTERIES & SUPERCAPS | 2021年 / 4卷 / 05期

基金：

欧盟地平线“2020”;

关键词：

artificial intelligence; battery; reproducibility crisis; standards; text mining;

D O I：

10.1002/batt.202100076

中图分类号：

O646 [电化学、电解、磁化学];

学科分类号：

081704 ;

摘要：

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.

引用

页码：689 / 689

页数：1

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