Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation

被引：149

作者：

Altintas, Cigdem ^{[1
]}

Altundal, Omer Faruk ^{[1
]}

Keskin, Seda ^{[1
]}

Yildirim, Ramazan ^{[2
]}

机构：

[1] Koc Univ, Dept Chem & Biol Engn, TR-34450 Istanbul, Turkey

[2] Bogazici Univ, Dept Chem Engn, TR-34342 Istanbul, Turkey

来源：

JOURNAL OF CHEMICAL INFORMATION AND MODELING | 2021年 / 61卷 / 05期

基金：

欧洲研究理事会;

关键词：

Metal-organic frameworks; Machine learning; High-throughput computational screening; Gas storage; Gas separation; Structure-performance relationships; Modeling; Material design; ARTIFICIAL NEURAL-NETWORKS; COMPUTATION-READY; MOLECULAR SIMULATION; HYDROGEN STORAGE; POROUS MATERIALS; WATER STABILITY; CO2; CAPTURE; ADSORPTION; DESIGN; MOFS;

D O I：

10.1021/acs.jcim.1c00191

中图分类号：

R914 [药物化学];

学科分类号：

100701 ;

摘要：

The acceleration in design of new metal organic frameworks (MOFs) has led scientists to focus on high-throughput computational screening (HTCS) methods to quickly assess the promises of these fascinating materials in various applications. HTCS studies provide a massive amount of structural property and performance data for MOFs, which need to be further analyzed. Recent implementation of machine learning (ML), which is another growing field in research, to HTCS of MOFs has been very fruitful not only for revealing the hidden structure-performance relationships of materials but also for understanding their performance trends in different applications, specifically for gas storage and separation. In this review, we highlight the current state of the art in ML-assisted computational screening of MOFs for gas storage and separation and address both the opportunities and challenges that are emerging in this new field by emphasizing how merging of ML and MOF simulations can be useful.

引用

页码：2131 / 2146

页数：16

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