Machine learning and high-throughput computational screening of hydrophobic metal–organic frameworks for capture of formaldehyde from air

Yuan, Xueying; Deng, Xiaomei; Cai, Chengzhi; Shi, Zenan; Liang, Hong; Li, Shuhua; Qiao, Zhiwei

doi:10.1016/j.gee.2020.06.024

Cited by 44 publications

(25 citation statements)

References 52 publications

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“…However, there is nearly no such report to date although machine learning and artificial intelligence are applied in many other fields. [179][180][181] (7) The decomposition temperature of ILs and DESs from TGA should be reevaluated by developing novel and simple methods because the mass loss from TGA may include both volatility and decomposition.…”

Section: Conclusion and Outlooksmentioning

confidence: 99%

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Thermal, chemical, electrochemical, radiolytic and biological stability of ionic liquids and deep eutectic solvents

Chen

Liu

et al. 2022

New J. Chem.

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Section: Conclusion and Outlooksmentioning

confidence: 99%

“…However, there is nearly no such report to date although machine learning and artificial intelligence are applied in many other fields. 179–181…”

Section: Conclusion and Outlooksmentioning

confidence: 99%

Thermal, chemical, electrochemical, radiolytic and biological stability of ionic liquids and deep eutectic solvents

Chen

Liu

et al. 2022

New J. Chem.

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“… 53 Analysis of MOFs with ML has accelerated in recent years 25 , 54 − 73 for a variety of fields such as identifying electronic structure properties of MOFs, 63 , 64 predicting colors of MOFs, 61 defining the oxidation states of metals in MOFs, 62 assigning partial charges to MOF atoms, 59 , 73 optimizing the swing adsorption process conditions with MOFs, 58 , 60 and for predicting the performances of MOFs as sensors, 55 , 56 heat pumps, 57 and gas storage and separation materials. 66 , 74 − 79 …”

Section: Introductionmentioning

confidence: 99%

“…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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“…In the last twenty years, new materials metal–organic frameworks (MOFs), self-assembled by a wide range of organic links and metal nodes, have been considered to have potential for use in domains such as drug delivery [ 3 ], catalysis [ 4 ], gas storage [ 5 , 6 , 7 ], gas adsorption and separation [ 8 , 9 , 10 , 11 , 12 ] due to their excellent characteristics such as large surface area and high porosity. Commonly, MOFs can be applied as adsorbates and membranes for the separation of gas mixtures.…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Screening and Machine Learning for Metal–Organic Framework Membranes to Capture CO2 from Flue Gas

et al. 2022

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To combat global warming, as an energy-saving technology, membrane separation can be applied to capture CO2 from flue gas. Metal–organic frameworks (MOFs) with characteristics like high porosity have great potential as membrane materials for gas mixture separation. In this work, through a combination of grand canonical Monte Carlo and molecular dynamics simulations, the permeability of three gases (CO2, N2, and O2) was calculated and estimated in 6013 computation–ready experimental MOF membranes (CoRE–MOFMs). Then, the relationship between structural descriptors and permeance performance, and the importance of available permeance area to permeance performance of gas molecules with smaller kinetic diameters were found by univariate analysis. Furthermore, comparing the prediction accuracy of seven classification machine learning algorithms, XGBoost was selected to analyze the order of importance of six structural descriptors to permeance performance, through which the conclusion of the univariate analysis was demonstrated one more time. Finally, seven promising CoRE-MOFMs were selected, and their structural characteristics were analyzed. This work provides explicit directions and powerful guidelines to experimenters to accelerate the research on membrane separation for the purification of flue gas.

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Machine learning and high-throughput computational screening of hydrophobic metal–organic frameworks for capture of formaldehyde from air

Cited by 44 publications

References 52 publications

Thermal, chemical, electrochemical, radiolytic and biological stability of ionic liquids and deep eutectic solvents

Thermal, chemical, electrochemical, radiolytic and biological stability of ionic liquids and deep eutectic solvents

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

Large-Scale Screening and Machine Learning for Metal–Organic Framework Membranes to Capture CO2 from Flue Gas

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