Issue 17, 2022

Machine learning for flow batteries: opportunities and challenges

Abstract

With increased computational ability of modern computers, the rapid development of mathematical algorithms and the continuous establishment of material databases, artificial intelligence (AI) has shown tremendous potential in chemistry. Machine learning (ML), as one of the most important branches of AI, plays an important role in accelerating the discovery and design of key materials for flow batteries (FBs), and the optimization of FB systems. In this perspective, we first provide a fundamental understanding of the workflow of ML in FBs. Moreover, recent progress on applications of the state-of-art ML in both organic FBs and vanadium FBs are discussed. Finally, the challenges and future directions of ML research in FBs are proposed.

Graphical abstract: Machine learning for flow batteries: opportunities and challenges

Article information

Article type
Perspective
Submitted
17 Jan 2022
Accepted
06 Apr 2022
First published
07 Apr 2022
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2022,13, 4740-4752

Machine learning for flow batteries: opportunities and challenges

T. Li, C. Zhang and X. Li, Chem. Sci., 2022, 13, 4740 DOI: 10.1039/D2SC00291D

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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