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Machine Learning for Predicting Gas Adsorption Capacities of Metal Organic Framework

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Machine Learning and Deep Learning in Computational Toxicology

Part of the book series: Computational Methods in Engineering & the Sciences ((CMES))

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Abstract

Metal organic frameworks (MOFs) have been widely used in gas adsorption-based applications due to their high porosities and modification in chemical and physical properties. There are many MOFs available for applications. However, gas adsorption capacities are not known for most MOFs and it is not practical to experimentally test their gas adsorption capacities. Therefore, a variety of machine learning models have been developed for predicting gas adsorption capacities of MOFs. In this chapter, we summarized the machine learning models developed for predicting gas adsorption capacities of MOFs and their applications.

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  1. National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, 72079, USA

    Wenjing Guo, Jie Liu, Fan Dong, Tucker A. Patterson & Huixiao Hong

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  1. Wenjing Guo
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  2. Jie Liu
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  3. Fan Dong
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  4. Tucker A. Patterson
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Correspondence to Huixiao Hong .

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  1. Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA

    Huixiao Hong

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Guo, W., Liu, J., Dong, F., Patterson, T.A., Hong, H. (2023). Machine Learning for Predicting Gas Adsorption Capacities of Metal Organic Framework. In: Hong, H. (eds) Machine Learning and Deep Learning in Computational Toxicology. Computational Methods in Engineering & the Sciences. Springer, Cham. https://doi.org/10.1007/978-3-031-20730-3_28

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