机器学习技术在气体吸附多孔材料研究中的应用进展  

Progress in application of machine learning technology to study of porous materials for gas adsorption

作　　者：李勃然 李晖[1] 田子奇 LI Bo-ran;LI Hui;TIAN Zi-qi(Beijing University of Chemical Technology,Beijing 100029,China;Ningbo Institute of Materials Technology&Engineering,Chinese Academy of Sciences,Ningbo 315201,China)

机构地区：[1]北京化工大学,北京100029 [2]中国科学院宁波材料技术与工程研究所,浙江宁波315201

出　　处：《现代化工》2022年第4期17-22,共6页Modern Chemical Industry

基　　金：国家自然科学基金重大项目(91934303);国家自然科学基金青年科学基金项目(21803074)。

摘　　要：简要介绍了材料研究领域常用的机器学习算法,探讨了通过数据挖掘技术进行材料研究的方法流程,回顾了机器学习技术在多孔碳、沸石和金属有机骨架3类重要吸附材料研究中的典型案例,最后对这一技术在气体吸附材料研发中的应用前景进行了展望。Machine learning techniques, which have rapidly developed in recent years, are increasingly applied to assist in discovery of gas adsorption materials.The machine learning algorithms commonly used in the field of materials research are introduced, and the process for materials research through data mining techniques is explored.Typical cases of machine learning techniques in the research of three types of important adsorbent materials are reviewed, including porous carbon, zeolite and metal-organic framework.An outlook on the application of this technique in the R&D of gas adsorbent materials is provided.

关 键 词：气体吸附材料 机器学习 多孔碳 沸石 金属有机骨架 

分 类 号：TQ028.8[化学工程]