Machine learning-assisted high-throughput computational screening of MOFs and advances in gas separation research

被引：0

作者：

Hu, Jialang ^{[1
]}

Jiang, Mingyuan ^{[1
]}

Jin, Lyuming ^{[1
]}

Zhang, Yonggang ^{[1
]}

Hu, Peng ^{[1
]}

Ji, Hongbing ^{[1
]}

机构：

[1] College of Chemical Engineering, Zhejiang University of Technology, Zhejiang, Hangzhou

来源：

Huagong Xuebao/CIESC Journal | 2025年 / 76卷 / 05期

关键词：

adsorption; high-throughput computational screening; machine learning; metal organic frameworks; molecular simulation; separation;

D O I：

10.11949/0438-1157.20241229

中图分类号：

学科分类号：

摘要：

Metal organic frameworks (MOFs) have garnered extensive research interest in fields such as gas storage, adsorption separation, and catalysis due to their high surface area, large pore volume, and tunable structures. In recent years, the surge in the number of MOFs has posed unprecedented challenges in finding the ideal MOF for specific applications. In this scenario, high-throughput computational screening (HTCS) has become the most effective research method for screening high-performance target MOFs from a vast array of materials. HTCS will generate a large amount of multidimensional data, which can be further used for machine learning (ML) training. Recently, applying ML to HTCS of MOFs has become a new hotspot, which can not only reveal the potential structure-performance relationships of materials but also provide insights into their performance trends in different applications, especially in gas storage and separation. In this review, we highlight the latest advances in ML-assisted HTCS in the field of MOFs gas separation, systematically analyze the internal mechanism of ML and HTCS collaboration to improve screening efficiency in the search for high-performance MOFs, and explore the opportunities and challenges presented in this new field. © 2025 Materials China. All rights reserved.

引用

页码：1973 / 1996

页数：23

共 128 条

[51]

Glasby L T, Gubsch K, Bence R, Et al., DigiMOF: a database of metal-organic framework synthesis information generated via text mining, Chemistry of Materials, 35, 11, pp. 4510-4524, (2023)

[52]

Domingues N P, Moosavi S M, Talirz L, Et al., Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF, Communications Chemistry, 5, 1, (2022)

[53]

Tshitoyan V, Dagdelen J, Weston L, Et al., Unsupervised word embeddings capture latent knowledge from materials science literature, Nature, 571, 7763, pp. 95-98, (2019)

[54]

First E L, Floudas C A., MOFomics: computational pore characterization of metal-organic frameworks, Microporous and Mesoporous Materials, 165, pp. 32-39, (2013)

[55]

Rosen A S, Iyer S M, Ray D, Et al., Machine learning the quantum-chemical properties of metal-organic frameworks for accelerated materials discovery, Matter, 4, 5, pp. 1578-1597, (2021)

[56]

Krallinger M, Rabal O, Lourenco A, Et al., Information retrieval and text mining technologies for chemistry, Chemical Reviews, 117, 12, pp. 7673-7761, (2017)

[57]

Luo Y, Bag S, Zaremba O, Et al., MOF synthesis prediction enabled by automatic data mining and machine learning, Angewandte Chemie International Edition, 61, 19, (2022)

[58]

Hai G T, Gao H Y, Zhao G X, Et al., Difference between metal-S and metal-O bond orders: a descriptor of oxygen evolution activity for isolated metal atom-doped MoS<sub>2</sub> nanosheets, iScience, 20, pp. 481-488, (2019)

[59]

Doitomi K, Hirao H., Hybrid computational approaches for deriving quantum mechanical insights into metal-organic frameworks, Tetrahedron Letters, 58, 24, pp. 2309-2317, (2017)

[60]

Thai H T., Machine learning for structural engineering: a state-of-the-art review, Structures, 38, pp. 448-491, (2022)

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