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

任嘉豪; 侯焕娣; 董明; 李吉广; 陶梦莹

doi:10.12434/j.issn.2097-2547.20250212

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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

分离材料与净化技术 | 更新时间：2025-10-22

- 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
- 低碳化学与化工 2025, 50(10): 82-90.
- 作者机构：
  
  中石化石油化工科学研究院有限公司，北京 100083
- 作者简介：
  
  任嘉豪（1994—），博士，助理研究员，研究方向为金属-有机框架材料的开发及其应用，E-mail：renjiahao.ripp@sinopec.com。
- 基金信息：
- DOI：10.12434/j.issn.2097-2547.20250212
  中图分类号： TQ028.1;O647.3
- 收稿：2025-05-06，
  
  修回：2025-06-03，
  
  纸质出版：2025-10-25
- 稿件说明：
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任嘉豪,侯焕娣,董明等.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.

REN Jiahao,HOU Huandi,DONG Ming,et al.Rational design of MOF for light hydrocarbon separation: Advances in multiscale computing and machine learning applications[J].Low-Carbon Chemistry and Chemical Engineering,2025,50(10):82-90.
任嘉豪,侯焕娣,董明等.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. DOI： 10.12434/j.issn.2097-2547.20250212.

REN Jiahao,HOU Huandi,DONG Ming,et al.Rational design of MOF for light hydrocarbon separation: Advances in multiscale computing and machine learning applications[J].Low-Carbon Chemistry and Chemical Engineering,2025,50(10):82-90. DOI： 10.12434/j.issn.2097-2547.20250212.

摘要

金属-有机框架（Metal-Organic Framework，MOF）材料是一类由金属节点和有机配体通过自组装精确构建的纳米多孔晶体。由于MOF材料具有比表面积大和结构可调性高等优点，已在天然气脱重烃、烷烯烃分离以及乙炔纯化等轻烃分离领域展现出巨大的应用潜力。从数以万计的MOF材料中，实现目标应用材料的高效“按需”设计是一个巨大的挑战。计算机辅助技术为研究者提供了一种高效工具，通过理论计算与数据驱动模式可轻松地实现大量MOF材料的分离性能预测。基于计算机辅助MOF材料设计的前沿研究成果，系统梳理了多尺度计算方法（量子化学计算、分子动力学模拟和蒙特卡罗模拟）在MOF材料用于轻烃吸附/分离研究中的应用进展，并讨论了新兴机器学习手段辅助MOF材料开发的研究实例。最后，结合多尺度计算和数据驱动方法的技术特点，对计算机辅助MOF材料开发的未来发展方向进行了展望。

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

Keywords

references

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