Machine learning for flow batteries: opportunities and challenges.
Chem Sci
; 13(17): 4740-4752, 2022 May 04.
Article
en En
| MEDLINE
| ID: mdl-35655893
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.
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1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Chem Sci
Año:
2022
Tipo del documento:
Article
Pais de publicación:
Reino Unido