Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, Colorado 80523, United States.
Department of Civil and Environmental Engineering, Technion - Israel Institute of Technology, Haifa 32000, Israel.
Environ Sci Technol. 2023 Nov 21;57(46):17851-17862. doi: 10.1021/acs.est.2c08384. Epub 2023 Mar 14.
Recent studies have increasingly applied machine learning (ML) to aid in performance and material design associated with membrane separation. However, whether the knowledge attained by ML with a limited number of available data is enough to capture and validate the fundamental principles of membrane science remains elusive. Herein, we applied explainable artificial intelligence (XAI) to thoroughly investigate the knowledge learned by ML on the mechanisms of ion transport across polyamide reverse osmosis (RO) and nanofiltration (NF) membranes by leveraging 1,585 data from 26 membrane types. The Shapley additive explanation method based on cooperative game theory was used to unveil the influences of various ion and membrane properties on the model predictions. XAI shows that the ML can capture the important roles of size exclusion and electrostatic interaction in regulating membrane separation properly. XAI also identifies that the mechanisms governing ion transport possess different relative importance to cation and anion rejections during RO and NF filtration. Overall, we provide a framework to evaluate the knowledge underlying the ML model prediction and demonstrate that ML is able to learn fundamental mechanisms of ion transport across polyamide membranes, highlighting the importance of elucidating model interpretability for more reliable and explainable ML applications to membrane selection and design.
最近的研究越来越多地应用机器学习 (ML) 来辅助膜分离相关的性能和材料设计。然而，通过有限数量的可用数据获得的 ML 知识是否足以捕捉和验证膜科学的基本原理仍然难以捉摸。在此，我们应用可解释的人工智能 (XAI) 通过利用来自 26 种膜类型的 1585 个数据，深入研究 ML 对聚酰胺反渗透 (RO) 和纳滤 (NF) 膜中离子传输机制的学习知识。基于合作博弈论的 Shapley 加法解释方法用于揭示各种离子和膜特性对模型预测的影响。XAI 表明，ML 可以正确地捕捉尺寸排阻和静电相互作用在调节膜分离中的重要作用。XAI 还确定了在 RO 和 NF 过滤过程中，控制离子传输的机制对阳离子和阴离子排斥具有不同的相对重要性。总的来说，我们提供了一个评估 ML 模型预测背后知识的框架，并证明 ML 能够学习聚酰胺膜中离子传输的基本机制，突出了阐明模型可解释性对于更可靠和可解释的 ML 应用于膜选择和设计的重要性。
