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Redox Flow Batteries: Machine Learning Coupled Multi‐Scale Modeling for Redox Flow Batteries (Adv. Theory Simul. 2/2020)
Author(s) -
Bao Jie,
Murugesan Vijayakumar,
Kamp Carl Justin,
Shao Yuyan,
Yan Litao,
Wang Wei
Publication year - 2020
Publication title -
advanced theory and simulations
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.068
H-Index - 17
ISSN - 2513-0390
DOI - 10.1002/adts.202070004
Subject(s) - flow battery , redox , solver , computer science , flow (mathematics) , battery (electricity) , scale (ratio) , partial differential equation , artificial neural network , computational science , artificial intelligence , materials science , mechanics , physics , thermodynamics , quantum mechanics , metallurgy , power (physics) , programming language
The framework of a model combining a deep neural network and a partial differential equation solver for redox flow batteries is introduced by Jie Bao, Wei Wang, and co‐workers in article number 1900167. Their model establishes a critical link between the micro‐structure of a flow‐battery component and its performance at the device scale, thereby providing rationale for further operational and material optimization.