摘要
随着人工智能技术的飞速发展,机器学习作为人工智能的核心分支,已经在多个领域大放异彩。膜材料领域作为现代化学工程的基石,其性能优化和设计创新一直是研究的热点。机器学习算法能够快速筛选和评估膜材料的候选结构并预测其在不同条件下的性能表现,加快膜材料的研发进度。首先介绍了机器学习算法流程及常见的机器学习模型,然后总结归纳目前已有的膜材料公开数据集,接着总结了机器学习在膜设计与制造、膜性能预测、辅助膜筛选和优化等领域的研究成果,最后讨论了机器学习在膜材料研发中面临的挑战并展望其发展前景。
With the rapid development of artificial intelligence technology,machine learning,as a core branch of artificial intelligence,has made a splash in many fields.The field of membrane materials,as the cornerstone of modern chemical engineering,has been a hot research topic for its performance optimization and design innovation.Machine learning algorithms can quickly screen and evaluate the candidate structures of membrane materials and predict their performance under different conditions,accelerating the development of membrane materials.This paper firstly introduced the machine learning algorithm workflow and common machine learning models,then summarized the existing public datasets of membrane materials.Following that,it reviewed the research achievements of machine learning in the fields of membrane design and fabrication,membrane performance prediction,and assisted membrane screening and optimization.Finally,it discussed the challenges faced by machine learning in the research and development of membrane materials,and looked forward to the prospect of its development.
作者
李立涵
魏明杰
吴斌
Li Lihan;Wei Mingjie;Wu Bin(College of Economic and Management,Nanjing Tech University,Nanjing 211816;State Key Laboratory of Materials-Oriented Chemical Engineering,Nanjing Tech University,Nanjing 211816)
出处
《化工新型材料》
北大核心
2025年第7期1-7,共7页
New Chemical Materials
基金
国家重点研发计划(2022YFB3805201)。
关键词
机器学习
数据驱动
膜材料设计
性能预测
优化
machine learning
data-driven
membrane material design
performance prediction
optimization
作者简介
李立涵(1999-),男,硕士研究生,主要从事基于机器学习的膜材料性能预测工作;通讯作者:吴斌(1979-),男,教授,硕士生导师,主要从事机器学习及进化计算方面的研究,E-mail:wubin@njtech.edu.cn。
