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A large-scale screening of metal-organic frameworks for iodine capture combining molecular simulation and machine learning

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Abstract

We performed large-scale molecular simulation to screen and identify metal-organic framework materials for gaseous iodine capture, as part of our ongoing effort in addressing management and handling issues of various radionuclides in the grand scheme of spent nuclear fuel reprocessing. Starting from the computation-ready experimental (CoRE) metal-organic frameworks (MOFs) database, grand canonical Monte Carlo simulation was employed to predict the iodine uptake values of the MOFs. A ranking list of MOFs based on their iodine uptake capabilities was generated, with the Top 10 candidates identified and their respective adsorption sites visualized. Subsequently, machine learning was used to establish structure-property relationships to correlate MOFs’ various structural and chemical features with their corresponding performances in iodine capture, yielding interpretable common features and design rules for viable MOF adsorbents. The research strategy and framework of the present study could aid the development of high-performing MOF adsorbents for capture and recovery of radioactive iodine, and moreover, other volatile environmentally hazardous species.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (No. 22176135, C.L.). Additionally, this research was supported by the Fundamental Research Funds for the Central Universities in China (No. YJ201976, C.L.) and start-up funds from the School of Chemical Engineering, Sichuan University (C.L.).

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  1. These authors contributed equally to this work.

Authors and Affiliations

  1. School of Chemical Engineering, Sichuan University, Chengdu, 610065, China

    Min Cheng, Zhiyuan Zhang, Shihui Wang, Kexin Bi, Yiyang Dai, Chong Liu, Li Zhou & Xu Ji

  2. Laboratory of Nuclear Energy Chemistry, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China

    Kong-qiu Hu & Wei-qun Shi

  3. College of Architecture and Environment, Sichuan University, Chengdu, 610065, China

    Zhongde Dai

  4. School of Carbon Neutrality Future Technology, Sichuan University, Chengdu, 610065, China

    Zhongde Dai

  5. Department of Bioprocess Engineering, Institute of Biotechnology, Technische Universität Berlin, Berlin, 10623, Germany

    Kexin Bi

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  1. Min Cheng
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  9. Li Zhou
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Correspondence to Chong Liu or Li Zhou.

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The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Data Accessibility Statement

Additional data supporting the findings of this study are available from the corresponding author [C.L.] on request.

Highlights

• Screened 8862 metal-organic frameworks for I2 capture via molecular simulation.

• Ranked metal-organic frameworks on predicted I2 uptake and identified Top 10.

• Established quantitative structure-property relationships via machine learning.

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11783_2023_1748_MOESM1_ESM.pdf

A large-scale screening of metal-organic frameworks for iodine capture combining molecular simulation and machine learning

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Cheng, M., Zhang, Z., Wang, S. et al. A large-scale screening of metal-organic frameworks for iodine capture combining molecular simulation and machine learning. Front. Environ. Sci. Eng. 17, 148 (2023). https://doi.org/10.1007/s11783-023-1748-3

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