Text mining assisted review of the literature on Li-O2 batteries

Amangeldi Torayev (1, 2, 3) , Pieter C M M Magusin (2, 3) , Clare P Grey (2, 3) , Céline Merlet (4, 5, 3) , Alejandro Franco (1, 3, 5, 6)

1 LRCS - Laboratoire réactivité et chimie des solides - UMR CNRS 7314
2 CAM - University of Cambridge [UK]
3 ALISTORE-ERI - Advanced Lithium Energy Storage Systems - ALISTORE-ERI
4 CIRIMAT - Centre interuniversitaire de recherche et d'ingenierie des matériaux
5 RS2E - Réseau sur le stockage électrochimique de l'énergie
6 IUF - Institut universitaire de France

The high theoretical capacity of Li-O 2 batteries attracts a lot of attention and this field has expanded significantly in the last two decades. In a more general way, the large number of articles being published daily makes it difficult for researchers to keep track of the progress in science. Here we develop a text mining program in an attempt to facilitate the process of reviewing the literature published in a scientific field and apply it to Li-O 2 batteries. We analyze over 1800 articles and use the text mining program to extract reported discharge capacities, for the first time, which allows us to show the clear progress made in recent years. In this paper, we focus on three main challenges of Li-O 2 batteries, namely the stability-cyclability, the low practical capacity and the rate capability. Indeed, according to our text mining program, articles dealing with these issues represent 86% of the literature published in the field. For each topic, we provide a bibliometric analysis of the literature before focusing on a few key articles which allow us to get insights into the physics and chemistry of such systems. We believe that text mining can help readers find breakthrough papers in a field (e.g. by identifying papers reporting much higher performances) and follow the developments made at the state of the art (e.g. by showing trends in the numbers of papers published-a decline in a given topic probably being the sign of limitations). With the progress of text mining algorithms in the future, the process of reviewing a scientific field is likely to become more and more automated, making it easier for researchers to get the 'big picture' in an unfamiliar scientific field.

Mots clés: text mining; machine learning; lithium oxygen batteries

Domaines: Chimie théorique et/ou physique; Recherche d'information [cs.IR]; Sciences de l'environnement

Amangeldi Torayev, Pieter C M M Magusin, Clare P Grey, Céline Merlet, Alejandro Franco. Text mining assisted review of the literature on Li-O2 batteries. Journal of Physics: Materials, 2019, 2 (4), pp.044004. ⟨10.1088/2515-7639/ab3611⟩. ⟨hal-02299034⟩