Polymeric membranes have been widely used for liquid and gas separation in various industrial applications over the past few decades because of their exceptional versatility and high tunability. Traditional trial-and-error methods for material synthesis are inadequate to meet the growing demands for high-performance membranes. Machine learning (ML) has demonstrated huge potential to accelerate design and discovery of membrane materials. In this review, we cover strengths and weaknesses of the traditional methods, followed by a discussion on the emergence of ML for developing advanced polymeric membranes. We describe methodologies for data collection, data preparation, the commonly used ML models, and the explainable artificial intelligence (XAI) tools implemented in membrane research. Furthermore, we explain the experimental and computational validation steps to verify the results provided by these ML models. Subsequently, we showcase successful case studies of polymeric membranes and emphasize inverse design methodology within a ML-driven structured framework. Finally, we conclude by highlighting the recent progress, challenges, and future research directions to advance ML research for next generation polymeric membranes. With this review, we aim to provide a comprehensive guideline to researchers, scientists, and engineers assisting in the implementation of ML to membrane research and to accelerate the membrane design and material discovery process.

中文翻译：

---

用于膜分离的新型聚合物材料的机器学习辅助逆向设计和发现


在过去的几十年里，聚合物膜因其卓越的多功能性和高可调性而被广泛用于各种工业应用中的液体和气体分离。传统的材料合成试错方法不足以满足对高性能膜日益增长的需求。机器学习 （ML） 在加速膜材料的设计和发现方面表现出了巨大的潜力。在这篇综述中，我们介绍了传统方法的优缺点，然后讨论了用于开发高级聚合物膜的 ML 的出现。我们描述了数据收集、数据准备、常用的 ML 模型以及膜研究中实施的可解释人工智能 （XAI） 工具的方法。此外，我们还解释了实验和计算验证步骤，以验证这些 ML 模型提供的结果。随后，我们展示了聚合物膜的成功案例研究，并强调 ML 驱动的结构化框架内的逆向设计方法。最后，我们强调了推进下一代聚合物膜 ML 研究的最新进展、挑战和未来研究方向。通过这篇综述，我们旨在为协助将 ML 实施到膜研究中并加速膜设计和材料发现过程的研究人员、科学家和工程师提供全面的指南。