Adsorption behavior of metal-organic frameworks: From single simulation, high-throughput computational screening to machine learning

被引：27

作者：

Yan, Yaling ^{[1
]}

Zhang, Lulu ^{[1
]}

Li, Shuhua ^{[1
]}

Liang, Hong ^{[1
]}

Qiao, Zhiwei ^{[1
]}

机构：

[1] Guangzhou Univ, Sch Chem & Chem Engn, Guangzhou Key Lab New Energy & Green Catalysis, Guangzhou 510006, Peoples R China

来源：

COMPUTATIONAL MATERIALS SCIENCE | 2021年 / 193卷

基金：

中国国家自然科学基金;

关键词：

Metal-organic frameworks; Simulation; High-throughput computational screening; Machine learning; STRUCTURE-PROPERTY RELATIONSHIP; CARBON-DIOXIDE SEPARATION; CHIRAL STATIONARY PHASES; MONTE-CARLO SIMULATIONS; POROUS MATERIALS; MEMBRANE SEPARATION; MOLECULAR-DYNAMICS; HYDROGEN STORAGE; CO2; NETWORKS;

D O I：

10.1016/j.commatsci.2021.110383

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Recently, the development and application of porous materials have attracted increasing attention, and metalorganic frameworks (MOFs) have become ?stars? in the emerging material field because of their high porosities and ultra-high specific surface areas. In this review, the calculated works of MOFs in this field in the past ten years were summarized, especially for MOF adsorbents. With the continuous growth of the number of adsorbent materials, simulations have gradually transitioned from the single simulation, high-throughput computational screening (HTCS) to machine-learning (ML)-assisted HTCS. The purpose of this paper is to sort out the research progress and current ideas for the adsorption simulations of MOFs. Finally, we highlight the bottlenecks and challenges for the future commercialization of HTCS based on ML and the ML-assisted HTCS methods that are suitable for solving the research problems in this field. We also speculate about the future development directions of this field, hoping to promote the practical application of porous adsorbents.

引用

页数：15

共 104 条

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