低碳化学与化工2025，Vol.50Issue(10)：82-90,9.DOI:10.12434/j.issn.2097-2547.20250212
面向轻烃分离的MOF材料设计:多尺度计算与机器学习应用进展
Rational design of MOF for light hydrocarbon separation:Advances in multiscale computing and machine learning applications
任嘉豪 1侯焕娣 1董明 1李吉广 1陶梦莹1
作者信息
- 1. 中石化石油化工科学研究院有限公司,北京 100083
- 折叠
摘要
Abstract
As a kind of nanoporous crystalline material,metal-organic framework(MOF)are constructed by self-assembly of metal nodes and organic ligands.Due to the advantages such as large specific surface area and high structural tunability,MOF has shown great application potential in light hydrocarbon separation fields,including heavy hydrocarbon removal in natural gas,alkane and alkene separation and acetylene purification.However,realizing efficient"on-demand"design of target application materials from tens of thousands of MOF remains a huge challenge.The computer-aided technology provides an efficient tool to easily realize separation performance prediction of a large number of MOF through theoretical calculations and data-driven approaches.Based on cutting-edge research achievements in computer-aided MOF design,the application advances in multiscale computing methods(quantum chemistry calculation,molecular dynamics simulation and Monte Carlo simulation)in the study of MOF for light hydrocarbon adsorption/separation were systematically reviewed,and case studies of emerging machine learning methods to assist MOF development were discussed.Finally,by combining the technical characteristics of multiscale computing and data-driven methods,the future development directions of computer-aided MOF design were prospected.关键词
金属-有机框架材料/轻烃分离/多尺度计算/机器学习Key words
metal-organic framework/light hydrocarbon separation/multiscale computing/machine learning分类
化学化工引用本文复制引用
任嘉豪,侯焕娣,董明,李吉广,陶梦莹..面向轻烃分离的MOF材料设计:多尺度计算与机器学习应用进展[J].低碳化学与化工,2025,50(10):82-90,9.
