Accelerating the discovery of acid gas-selective MOFs for natural gas purification: A combination of machine learning and molecular fingerprint

Gao, Weiqun; Zheng, Weizhong; Yan, Kexin; Sun, Weizhen; Zhao, Ling

doi:10.1016/j.fuel.2023.128757

Cited by 10 publications

(3 citation statements)

References 72 publications

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“…The interaction between MOFs and adsorbent molecules, besides being related to their relative positions, also depended on a large extent on the type and number of exposed metal sites, organic ligands, and modified functional groups [1,2]. However, the molecular structure was not a numerical value or a collection of numerical values, which must be transformed into a digital form to be used as input variables.…”

Section: Rdf R F P P Ementioning

confidence: 99%

“…Carbon dioxide and light hydrocarbon were important raw materials for energy gas and chemical products in the modern petrochemical industry, and had a very important strategic position. For the petrochemical industry, the gas products in the form of mixtures cannot be directly used for industrial applications, and needed to be further separated and purified to obtain high-quality products, so as to carry out the next step of research and application [1,2].…”

Section: Introductionmentioning

confidence: 99%

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Application of machine learning in MOFs for gas adsorption and separation

Yang,

Qi,

Wang

et al. 2023

Mater. Res. Express

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Metal-organic frameworks (MOFs) with high specific surface area, permanent porosity and extreme modifiability have great potential for gas storage and separation applications. Considering the theoretically nearly infinite variety of MOFs, it is difficult but necessary to achieve high-throughput computational screening (HTCS) of high-performance MOFs for specific applications. Machine learning (ML) is a field of computer science where one of its research directions is the effective use of information in a big data environment, focusing on obtaining hidden, valid and understandable knowledge from huge amounts of data, and has been widely used in materials research. This paper firstly briefly introduces the MOFs databases and related algorithms for ML, followed by a detailed review of the research progress on HTCS of MOFs based on ML according to four classes of descriptors, including geometrical, chemical, topological and energy-based, for gas storage and separation, and finally a related outlook is presented. This paper aims to deepen readers' understanding of ML-based MOF research, and to provide some inspirations and help for related research.

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Section: Rdf R F P P Ementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of machine learning in MOFs for gas adsorption and separation

Yang,

Qi,

Wang

et al. 2023

Mater. Res. Express

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“…Most of the MOF-based ML studies focus on gas uptake on MOFs 17 and screening of materials for CO 2 capture 18–21 and separation. 22 There are also some studies focusing on synthesis conditions to predict stability as well as inverse material design using ML. 23 Additionally, Lin et al reviewed new MOF databases developed from the experimental and DFT-based data.…”

Section: Introductionmentioning

confidence: 99%

Analysis of photocatalytic CO₂ reduction over MOFs using machine learning

Özsoysal,

Oral,

Yıldırım

2024

J. Mater. Chem. A

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Computational Simulations of Metal–Organic Frameworks to Enhance Adsorption Applications

Daglar,

Gulbalkan,

Aksu

et al. 2024

Advanced Materials

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Metal–organic frameworks (MOFs), renowned for their exceptional porosity and crystalline structure, stand at the forefront of gas adsorption and separation applications. Shortly after their discovery through experimental synthesis, computational simulations quickly become an important method in broadening the use of MOFs by offering deep insights into their structural, functional, and performance properties. This review specifically addresses the pivotal role of molecular simulations in enlarging the molecular understanding of MOFs and enhancing their applications, particularly for gas adsorption. After reviewing the historical development and implementation of molecular simulation methods in the field of MOFs, high‐throughput computational screening (HTCS) studies used to unlock the potential of MOFs in CO2 capture, CH4 storage, H2 storage, and water harvesting are visited and recent advancements in these adsorption applications are highlighted. The transformative impact of integrating artificial intelligence with HTCS on the prediction of MOFs’ performance and directing the experimental efforts on promising materials is addressed. An outlook on current opportunities and challenges in the field to accelerate the adsorption applications of MOFs is finally provided.

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Accelerating the discovery of acid gas-selective MOFs for natural gas purification: A combination of machine learning and molecular fingerprint

Cited by 10 publications

References 72 publications

Application of machine learning in MOFs for gas adsorption and separation

Application of machine learning in MOFs for gas adsorption and separation

Analysis of photocatalytic CO₂ reduction over MOFs using machine learning

Computational Simulations of Metal–Organic Frameworks to Enhance Adsorption Applications

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Accelerating the discovery of acid gas-selective MOFs for natural gas purification: A combination of machine learning and molecular fingerprint

Cited by 10 publications

References 72 publications

Application of machine learning in MOFs for gas adsorption and separation

Application of machine learning in MOFs for gas adsorption and separation

Analysis of photocatalytic CO2 reduction over MOFs using machine learning

Computational Simulations of Metal–Organic Frameworks to Enhance Adsorption Applications

Contact Info

Product

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Analysis of photocatalytic CO₂ reduction over MOFs using machine learning