Capacitive tendency concept alongside supervised machine-learning toward classifying electrochemical behavior of battery and pseudocapacitor materials

Siraprapha Deebansok, Jie Deng, Etienne Calvez, Yachao Zhu, Olivier Crosnier, Thierry Brousse and Olivier Fontaine ()
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Siraprapha Deebansok: VISTEC, Institute of Science and Technology
Jie Deng: Chengdu University
Etienne Calvez: Nantes Université, CNRS, Institut des Matériaux de Nantes Jean Rouxel, IMN
Yachao Zhu: ICGM, Université de Montpellier, CNRS
Olivier Crosnier: Nantes Université, CNRS, Institut des Matériaux de Nantes Jean Rouxel, IMN
Thierry Brousse: Nantes Université, CNRS, Institut des Matériaux de Nantes Jean Rouxel, IMN
Olivier Fontaine: VISTEC, Institute of Science and Technology

Nature Communications, 2024, vol. 15, issue 1, 1-9

Abstract: Abstract In recent decades, more than 100,000 scientific articles have been devoted to the development of electrode materials for supercapacitors and batteries. However, there is still intense debate surrounding the criteria for determining the electrochemical behavior involved in Faradaic reactions, as the issue is often complicated by the electrochemical signals produced by various electrode materials and their different physicochemical properties. The difficulty lies in the inability to determine which electrode type (battery vs. pseudocapacitor) these materials belong to via simple binary classification. To overcome this difficulty, we apply supervised machine learning for image classification to electrochemical shape analysis (over 5500 Cyclic Voltammetry curves and 2900 Galvanostatic Charge-Discharge curves), with the predicted confidence percentage reflecting the shape trend of the curve and thus defined as a manufacturer. It’s called “capacitive tendency”. This predictor not only transcends the limitations of human-based classification but also provides statistical trends regarding electrochemical behavior. Of note, and of particular importance to the electrochemical energy storage community, which publishes over a hundred articles per week, we have created an online tool to easily categorize their data.

Date: 2024
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DOI: 10.1038/s41467-024-45394-w

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