In silico discovery of metal-organic frameworks for precombustion CO2 capture using a genetic algorithm

In the past decade, there has been a rise in a number of computational screening works to facilitate finding optimal metal-organic frameworks (MOF) for variety of different applications. Unfortunately, most of these screening works are limited to its initial set of materials and result in brute-force type of a screening approach. In this work, we present a systematic strategy that can find materials with desired property from an extremely diverse and large MOF set of over 100 trillion possible MOFs using machine learning and evolutionary algorithm. It is demonstrated that our algorithm can discover 964 MOFs with methane working capacity over 200 cm3 cm−3 and 96 MOFs with methane working capacity over 208 cm3 cm−3, which is the current world record. We believe that this methodology can facilitate a new type of a screening approach that takes advantage of the modular nature in MOFs, and can readily be extended to other important applications as well.

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Discovery of Record-Breaking Metal-Organic Frameworks for Methane Storage using Evolutionary Algorithm and Machine Learning

10.26434/chemrxiv.12333077 ◽

2020 ◽

Author(s):

Sangwon Lee ◽

Baekjun Kim ◽

Jihan Kim

Keyword(s):

Machine Learning ◽

Evolutionary Algorithm ◽

Metal Organic Frameworks ◽

Working Capacity ◽

Computational Screening ◽

Screening Approach ◽

Systematic Strategy ◽

Metal Organic ◽

New Type ◽

Modular Nature

In the past decade, there has been a rise in a number of computational screening works to facilitate finding optimal metal-organic frameworks (MOF) for variety of different applications. Unfortunately, most of these screening works are limited to its initial set of materials and result in brute-force type of a screening approach. In this work, we present a systematic strategy that can find materials with desired property from an extremely diverse and large MOF set of over 100 trillion possible MOFs using machine learning and evolutionary algorithm. It is demonstrated that our algorithm can discover 964 MOFs with methane working capacity over 200 cm3 cm−3 and 96 MOFs with methane working capacity over 208 cm3 cm−3, which is the current world record. We believe that this methodology can facilitate a new type of a screening approach that takes advantage of the modular nature in MOFs, and can readily be extended to other important applications as well.

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Materials design by evolutionary optimization of functional groups in metal-organic frameworks

Science Advances ◽

10.1126/sciadv.1600954 ◽

2016 ◽

Vol 2 (11) ◽

pp. e1600954 ◽

Cited By ~ 35

Author(s):

Sean P. Collins ◽

Thomas D. Daff ◽

Sarah S. Piotrkowski ◽

Tom K. Woo

Keyword(s):

Genetic Algorithm ◽

Functional Groups ◽

Functional Property ◽

Metal Organic Frameworks ◽

Search Space ◽

Evolutionary Optimization ◽

Uptake Capacity ◽

High Performing ◽

Metal Organic ◽

Derivatives Of

A genetic algorithm that efficiently optimizes a desired physical or functional property in metal-organic frameworks (MOFs) by evolving the functional groups within the pores has been developed. The approach has been used to optimize the CO2 uptake capacity of 141 experimentally characterized MOFs under conditions relevant for postcombustion CO2 capture. A total search space of 1.65 trillion structures was screened, and 1035 derivatives of 23 different parent MOFs were identified as having exceptional CO2 uptakes of >3.0 mmol/g (at 0.15 atm and 298 K). Many well-known MOF platforms were optimized, with some, such as MIL-47, having their CO2 adsorption increase by more than 400%. The structures of the high-performing MOFs are provided as potential targets for synthesis.

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Tuning the Redox Activity of Metal−Organic Frameworks for Enhanced, Selective O2 Binding

10.26434/chemrxiv.10316681.v1 ◽

2019 ◽

Author(s):

Andrew Rosen ◽

M. Rasel Mian ◽

Timur Islamoglu ◽

Haoyuan Chen ◽

Omar Farha ◽

...

Keyword(s):

Density Functional ◽

Metal Organic Frameworks ◽

Redox Activity ◽

Screening Methods ◽

Bridging Ligands ◽

Metal Centers ◽

Computational Screening ◽

Open Metal Sites ◽

Metal Organic ◽

Unsaturated Metal Sites

Metal−organic frameworks (MOFs) with coordinatively unsaturated metal sites are appealing as adsorbent materials due to their tunable functionality and ability to selectively bind small molecules. Through the use of computational screening methods based on periodic density functional theory, we investigate O2 and N2 adsorption at the coordinatively unsaturated metal sites of several MOF families. A variety of design handles are identified that can be used to modify the redox activity of the metal centers, including changing the functionalization of the linkers (replacing oxido donors with sulfido donors), anion exchange of bridging ligands (considering μ-Br-, μ-Cl-, μ-F-, μ-SH-, or μ-OH- groups), and altering the formal oxidation state of the metal. As a result, we show that it is possible to tune the O2 affinity at the open metal sites of MOFs for applications involving the strong and/or selective binding of O2. In contrast with O2 adsorption, N2 adsorption at open metal sites is predicted to be relatively weak across the MOF dataset, with the exception of MOFs containing synthetically elusive V2+ open metal sites. As one example from the screening study, we predict that exchanging the μ-Cl- ligands of M2Cl2(BBTA) (H2BBTA = 1H,5H-benzo(1,2-d:4,5-d′)bistriazole) with μ-OH- groups would significantly enhance the strength of O2 adsorption at the open metal sites without a corresponding increase in the N2 affinity. Experimental investigation of Co2Cl2(BBTA) and Co2(OH)2(BBTA) confirms that the former exhibits only weak physisorption, whereas the latter is capable of chemisorbing O2 at room temperature. The chemisorption behavior is attributed to the greater electron-donating character of the μ-OH- ligands and the presence of H-bonding interactions between the μ-OH- bridging ligands and the O2 adsorbate.

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