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

Abstract

● 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.

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.

Graphical abstract

Keywords

Iodine capture / Metal-organic framework / Large-scale screening / Molecular simulation / Machine learning

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