Data-driven design of metal–organic frameworks for wet flue gas CO2 capture

Boyd, Peter G.; Chidambaram, Arunraj; García-Díez, Enrique; Ireland, Christopher P.; Daff, Thomas D.; Bounds, Richard; Gładysiak, Andrzej; Schouwink, Pascal; Moosavi, Seyed Mohamad; Maroto‐Valer, M. Mercedes; Reimer, Jeffrey A.; Navarro, Jorge A. R.; Woo, Tom K.; García, Susana; Stylianou, Kyriakos C.; Smit, Berend

doi:10.1038/s41586-019-1798-7

Cited by 491 publications

(478 citation statements)

References 29 publications

(30 reference statements)

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“…This Tinkertoy algorithm, however, gave only a few underlying nets 27 . An alternative approach, using topology-based algorithms has been applied by Gomez-Gualdron et al 28 for their ToBaCCo database (~13,000 structures), and by Boyd and Woo 4,29 for their BW-DB (over 300,000 structures). A comprehensive review of this topic can be found here 30 .…”

Section: Resultsmentioning

confidence: 99%

Understanding the diversity of the metal-organic framework ecosystem

et al. 2020

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Millions of distinct metal-organic frameworks (MOFs) can be made by combining metal nodes and organic linkers. At present, over 90,000 MOFs have been synthesized and over 500,000 predicted. This raises the question whether a new experimental or predicted structure adds new information. For MOF chemists, the chemical design space is a combination of pore geometry, metal nodes, organic linkers, and functional groups, but at present we do not have a formalism to quantify optimal coverage of chemical design space. In this work, we develop a machine learning method to quantify similarities of MOFs to analyse their chemical diversity. This diversity analysis identifies biases in the databases, and we show that such bias can lead to incorrect conclusions. The developed formalism in this study provides a simple and practical guideline to see whether new structures will have the potential for new insights, or constitute a relatively small variation of existing structures.

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Section: Resultsmentioning

confidence: 99%

Understanding the diversity of the metal-organic framework ecosystem

et al. 2020

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“…Nevertheless, trial and error approaches to identifying the best MOF host are still employed. In this sense, apart from the need to enhance both the creation of novel MOF databases 185 and the use of theoretical calculations 186 to predict the most appropriate MOF to host each guest species, it seems mandatory to further explore the development of new MOF-generation algorithms 187 to facilitate the selectionvia computational screeningof the most appropriate MOFs.…”

Section: Discussionmentioning

confidence: 99%

Isolating reactive metal-based species in Metal–Organic Frameworks – viable strategies and opportunities

et al. 2020

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“…As for chemical processes, the waste of carbon resources and volatilization of organic solvents during these actions lead to further environmental pollution, equipment corrosion, and complex post-treatment issues. The traditional technique for separating carbon dioxide is solvent washing, such as the use of an alcohol amine solution [7][8][9][10]. Although this conventional method can reduce the concentration of carbon dioxide in the air, it is extremely expensive, the solvent is difficult to regenerate, the operation is complicated, and it consumes a great deal of energy [11].…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Screening and Machine Learning to Predict the Computation-Ready, Experimental Metal-Organic Frameworks for CO2 Capture from Air

Deng

Yang

et al. 2020

Applied Sciences

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The rising level of CO2 in the atmosphere has attracted attention in recent years. The technique of capturing CO2 from higher CO2 concentrations, such as power plants, has been widely studied, but capturing lower concentrations of CO2 directly from the air remains a challenge. This study uses high-throughput computer (Monte Carlo and molecular dynamics simulation) and machine learning (ML) to study 6013 computation-ready, experimental metal-organic frameworks (CoRE-MOFs) for CO2 adsorption and diffusion properties in the air with very low concentrations of CO2. First, the law influencing CO2 adsorption and diffusion in air is obtained as a structure-performance relationship, and then the law influencing the performance of CO2 adsorption and diffusion in air is further explored by four ML algorithms. Random forest (RF) was considered the optimal algorithm for prediction of CO2 selectivity, with an R value of 0.981, and this algorithm was further applied to analyze the relative importance of each metal-organic framework (MOF) descriptor quantitatively. Finally, 14 MOFs with the best properties were successfully screened out, and it was found that a key to capturing a low concentration CO2 from the air was the diffusion performance of CO2 in MOFs. When the pore-limiting diameter (PLD) of a MOF was closer to the CO2 dynamic diameter, this MOF could possess higher CO2 diffusion separation selectivity. This study could provide valuable guidance for the synthesis of new MOFs in experiments that capture directly low concentration CO2 from the air.

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Data-driven design of metal–organic frameworks for wet flue gas CO2 capture

Cited by 491 publications

References 29 publications

Understanding the diversity of the metal-organic framework ecosystem

Understanding the diversity of the metal-organic framework ecosystem

Isolating reactive metal-based species in Metal–Organic Frameworks – viable strategies and opportunities

Large-Scale Screening and Machine Learning to Predict the Computation-Ready, Experimental Metal-Organic Frameworks for CO2 Capture from Air

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