Machine Learning and High-throughput Computational Screening of Metal-organic Framework for Separation of Methane/ethane/propane

Cai, Chengzhi; Li, Lifeng; Deng, Xiaomei; Li, Shuhua; Hong, Liang; Qiao, Zhiwei

doi:10.6023/a20030065

Cited by 18 publications

(9 citation statements)

References 26 publications

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“…These data were used to build quantitative structure–property relations (QSPRs), to predict the mechanical stability of structures, to estimate gas storage capacity of MOFs, , and to design novel MOFs by ML . Analysis of MOFs with ML has accelerated in recent years ,− for a variety of fields such as identifying electronic structure properties of MOFs, , predicting colors of MOFs, defining the oxidation states of metals in MOFs, assigning partial charges to MOF atoms, , optimizing the swing adsorption process conditions with MOFs, , and for predicting the performances of MOFs as sensors, , heat pumps, and gas storage and separation materials. ,− …”

Section: Introductionmentioning

confidence: 99%

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

Altıntaş

Altundal

Keskın

et al. 2021

J. Chem. Inf. Model.

140

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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.

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Section: Introductionmentioning

confidence: 99%

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

Altıntaş

Altundal

Keskın

et al. 2021

J. Chem. Inf. Model.

140

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“…In recent years, both ML and MF techniques have been successfully applied in a wide variety of different fields, such as new drug development, 31 prediction of material performance, 32 and material design 33 . In addition, machine learning (ML) and molecular fingerprints (MF) have been applied in the screening or design of high‐performance materials 34,35 . For example, six ML algorithms were employed to predict the selectivity of CO 2 /N 2 in MOFs.…”

Section: Introductionmentioning

confidence: 99%

Molecular fingerprint and machine learning to accelerate design of high‐performance homochiral metal–organic frameworks

Qiao

et al. 2021

AIChE Journal

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Computational screening was employed to calculate the enantioseparation capabilities of 45 functionalized homochiral metal–organic frameworks (FHMOFs), and machine learning (ML) and molecular fingerprint (MF) techniques were used to find new FHMOFs with high performance. With increasing temperature, the enantioselectivities for (R,S)‐1,3‐dimethyl‐1,2‐propadiene are improved. The “glove effect” in the chiral pockets was proposed to explain the correlations between the steric effect of functional groups and performance of FHMOFs. Moreover, the neighborhood component analysis and RDKit/MACCS MFs show the highest predictive effect on enantioselectivities among the four ML classification algorithms with nine MFs that were tested. Based on the importance of MF, 85 new FHMOFs were designed, and a newly designed FHMOF, NO2‐NHOH‐FHMOF, with high similarity to the optimal MFs achieved improved chiral separation performance, with enantioselectivities of 85%. The design principles and new chiral pockets obtained by ML and MFs could facilitate the development of new materials for chiral separation.

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“…例如, Qiao 等 [46] 采用分子模拟技术研究了 6,013 种真实 MOF 膜对 15 种二元气体混合物的分离性能, 并结合四种 ML 算法(决策树、反向传播神经网络、支持向量机和随机森林)基于六个物理描述符构建了 MOF 膜分离性能的 ML 预测模型. 此外, 机器学习还被用于气体混合物的吸附分离研究，比如 CH 4 /C 2 H 6 /C 3 H 8 分离 [47] 、 CH 4 /H 2 分离 [48] 、 CH 4 /C 2 H 6 /C 3 H 8 / CO 2 /H 2 S 分离 [49] 和 C 2 H 6 /C 2 H 4 分离 [50] .…”

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Large-Scale Computational Screening of Metal-Organic Framework Membranes for Ethane/Ethylene Separation

Cheng¹,

Wang²,

Li³

et al. 2022

Acta Chimica Sinica

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Compared to the traditional heat-driven cryogenic distillation process, the membrane separation based on metalorganic frameworks (MOFs) is a technically and economically viable alternative for ethane/ethylene (C2H6/C2H4) separation. To accelerate the application of MOF membranes in this gas separation field, this study performed a large-scale computational screening of 12,020 real MOFs for the identification of optimal C2H6-selective MOF membrane materials. According to geometric and chemical analyses, 2,192 MOFs without open metal sites and having pore limiting diameter no less than 0.38 nm were first screened out. Grand canonical Monte Carlo and molecular dynamics simulations were subsequently carried out to mimic the adsorption and diffusion behaviors of ethane and ethylene in these MOFs respectively, based on which their C2H6/C2H4 membrane selectivities and C2H6 permeabilities were estimated. The results showed that MISQIQ04 exhibited the highest C2H6/C2H4 membrane selectivity (4.16) and moderate C2H6 permeability (4.35×10 5 Barrer). Additionally, structureperformance relationships between the C2H6/C2H4 membrane selectivity and structural properties of MOFs were investigated, covering the largest cavity diameter (LCD), pore limiting diameter (PLD), density (ρ), gravimetric surface area (GSA), void fraction (VF), and pore volume (PV). The results indicated that MOFs with the structural characteristics of 0.4 nm≤LCD≤1 nm, 0.38 nm≤PLD≤0.75 nm, 0.8 g/cm 3 ≤ρ≤2.5 g/cm 3 , GSA≤1,700 m 2 /g, 0.3≤VF≤0.73, and PV≤0.85 cm 3 /g are optimal membrane materials for C2H6/C2H4 separation. Finally, a machine learning (ML) classifier was developed to achieve rapid prescreening of high-performing MOF membranes from a large MOF database, whose transferability was discussed on a hypothetical MOF database. Further t-Distributed Stochastic Neighbor Embedding analysis revealed that the ML model developed merely relying on a single MOF dataset generally exhibited poor transferability. Selecting the most representative and diverse MOFs from the entire MOF space for model development can help to improve the transferability and generalization ability of the developed model.

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Machine Learning and High-throughput Computational Screening of Metal-organic Framework for Separation of Methane/ethane/propane

Cited by 18 publications

References 26 publications

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

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

Molecular fingerprint and machine learning to accelerate design of high‐performance homochiral metal–organic frameworks

Large-Scale Computational Screening of Metal-Organic Framework Membranes for Ethane/Ethylene Separation

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