Machine learning for membrane design and discovery
Haoyu Yin, Muzi Xu, Zhiyao Luo, Xiaotian Bi, Jiali Li, Sui Zhang, Xiaonan Wang
Abstract
Membrane technologies are becoming increasingly versatile and helpful today for sustainable development. Machine Learning (ML), an essential branch of artificial intelligence (AI), has substantially impacted the research and development norm of new materials for energy and environment. This review provides an overview and perspectives on ML methodologies and their applications in membrane design and discovery. A brief overview of membrane technologies is first provided with the current bottlenecks and potential solutions. Through an applications-based perspective of AI-aided membrane design and discovery, we further show how ML strategies are applied to the membrane discovery cycle (including membrane material design, membrane application, membrane process design, and knowledge extraction), in various membrane systems, ranging from gas, liquid, and fuel cell separation membranes. Furthermore, the best practices of integrating ML methods and specific application targets in membrane design and discovery are presented with an ideal paradigm proposed. The challenges to be addressed and prospects of AI applications in membrane discovery are also highlighted in the end.