The partitioning of ions between polymeric ion-exchange membranes (IEMs) and the surrounding liquid is governed by the activity coefficients of the ions, which, in turn, significantly impact various ion transport processes within these membranes, notably conductivity. This study introduces a computational framework to predict ions’ activity coefficients in charged ion-exchange membranes (IEMs). This method employs a machine learning (ML) model using molecular-scale characteristics obtained from molecular dynamics (MD) simulations, particularly by emphasizing solvation properties within the context of IEMs. Specifically, the framework utilizes graph convolutional networks (GCN) to establish connections between the chemical structure of the polymer and the molecular-level attributes. This ultimately leads to determining macroscopic attributes, such as the activity coefficient, across a range of IEM materials having random copolymer and block copolymer systems. Furthermore, saliency maps were generated to identify the critical features of polymer molecules that correlate with the ion activity coefficients. The graph-based prediction strategy proved highly accurate in predicting ion activity coefficients within IEMs, even with relatively small training data sets.

中文翻译：

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聚合物离子交换膜中离子活性系数预测的基于图形的建模和分子动力学


聚合物离子交换膜 （IEM） 和周围液体之间的离子分配受离子活性系数的控制，这反过来又会显著影响这些膜内的各种离子传输过程，尤其是电导率。本研究引入了一个计算框架来预测带电离子交换膜 （IEM） 中离子的活性系数。该方法采用机器学习 （ML） 模型，使用从分子动力学 （MD） 模拟中获得的分子尺度特性，特别是通过强调 IEM 上下文中的溶剂化特性。具体来说，该框架利用图卷积网络 （GCN） 在聚合物的化学结构和分子水平属性之间建立连接。这最终导致确定一系列具有无规共聚物和嵌段共聚物系统的 IEM 材料的宏观属性，例如活性系数。此外，生成显著性图以确定与离子活性系数相关的聚合物分子的关键特征。事实证明，基于图形的预测策略在预测 IEM 中的离子活度系数方面非常准确，即使训练数据集相对较小也是如此。