Dangayach Raghav, Jeong Nohyeong, Demirel Elif, Uzal Nigmet, Fung Victor, Chen Yongsheng
School of Civil & Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
Department of Civil Engineering, Abdullah Gul University, 38039 Kayseri, Turkey.
Environ Sci Technol. 2025 Jan 21;59(2):993-1012. doi: 10.1021/acs.est.4c08298. Epub 2024 Dec 16.
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.
在过去几十年中，聚合物膜因其卓越的通用性和高度可调节性，已在各种工业应用中广泛用于液体和气体分离。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）已展现出加速膜材料设计与发现的巨大潜力。在本综述中，我们阐述了传统方法的优缺点，随后讨论了用于开发先进聚合物膜的机器学习的出现。我们描述了数据收集、数据准备方法、常用的机器学习模型以及膜研究中实施的可解释人工智能（XAI）工具。此外，我们解释了验证这些机器学习模型所提供结果的实验和计算验证步骤。随后，我们展示了聚合物膜的成功案例研究，并强调了在机器学习驱动的结构化框架内的逆向设计方法。最后，我们通过突出推进下一代聚合物膜机器学习研究的最新进展、挑战和未来研究方向来得出结论。通过本综述，我们旨在为研究人员、科学家和工程师提供全面的指南，协助他们将机器学习应用于膜研究，并加速膜设计和材料发现过程。
