Cao Zhonglin, Barati Farimani Omid, Ock Janghoon, Barati Farimani Amir
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh Pennsylvania 15213, United States.
Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh Pennsylvania 15213, United States.
Nano Lett. 2024 Mar 13;24(10):2953-2960. doi: 10.1021/acs.nanolett.3c05137. Epub 2024 Mar 4.
Porous membranes, either polymeric or two-dimensional materials, have been extensively studied because of their outstanding performance in many applications such as water filtration. Recently, inspired by the significant success of machine learning (ML) in many areas of scientific discovery, researchers have started to tackle the problem in the field of membrane design using data-driven ML tools. In this Mini Review, we summarize research efforts on three types of applications of machine learning in membrane design, including (1) membrane property prediction using ML, (2) gaining physical insight and drawing quantitative relationships between membrane properties and performance using explainable artificial intelligence, and (3) ML-guided design, optimization, or virtual screening of membranes. On top of the review of previous research, we discuss the challenges associated with applying ML for membrane design and potential future directions.
多孔膜，无论是聚合物膜还是二维材料，因其在水过滤等许多应用中的出色性能而受到广泛研究。最近，受机器学习（ML）在许多科学发现领域取得的重大成功启发，研究人员开始使用数据驱动的ML工具来解决膜设计领域的问题。在本综述中，我们总结了机器学习在膜设计中的三种应用的研究成果，包括（1）使用ML预测膜性能，（2）利用可解释人工智能获得物理见解并绘制膜性能与性能之间的定量关系，以及（3）ML指导的膜设计、优化或虚拟筛选。在回顾先前研究的基础上，我们讨论了将ML应用于膜设计所面临的挑战以及潜在的未来方向。
