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.

中文翻译：

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氧化还原液流电池：针对氧化还原液流电池的机器学习耦合多尺度建模（Adv。Theory Simul。2/2020）

鲍保杰，王伟和同事在文章编号1900167中介绍了一种结合了深度神经网络和偏微分方程求解器的氧化还原液流电池模型的框架。他们的模型建立了氧化锌液流电池微观结构之间的关键链接。电池组件及其在设备规模上的性能，从而为进一步的操作和材料优化提供了依据。