High-Throughput Computational Screening and Interpretable Machine Learning of Metal-organic Frameworks for Iodine Capture

TL;DR

This study combines high-throughput computational screening and interpretable machine learning to identify key structural and chemical features of metal-organic frameworks that optimize iodine capture in humid environments, aiding nuclear waste management.

Contribution

It introduces an integrated approach using machine learning and molecular fingerprints to predict and interpret iodine adsorption performance of MOFs, revealing critical structural and chemical factors.

Findings

Henry's coefficient and heat of adsorption are key chemical factors.

Six-membered rings and nitrogen atoms in MOFs enhance iodine adsorption.

Machine learning models accurately predict iodine capture capabilities.

Abstract

The removal of leaked radioactive iodine isotopes in humid environments holds significant importance in nuclear waste management and nuclear accident mitigation. In this study, high-throughput computational screening and machine learning were combined to reveal the iodine capture performance of 1816 metal-organic framework (MOF) materials under humid air conditions. Firstly, the relationship between the structural characteristics of MOFs and their adsorption properties was explored, with the aim of identifying the optimal structural parameters for iodine capture. Subsequently, two machine learning regression algorithms - Random Forest and CatBoost, were employed to predict the iodine adsorption capabilities of MOFs. In addition to 6 structural features, 25 molecular features and 8 chemical features were incorporated to enhance the prediction accuracy of the machine learning algorithms. Feature importance was assessed to determine the relative influence of various features on iodine adsorption performance, in which the Henry's coefficient and heat of adsorption to iodine were found the two most crucial chemical factors. Furthermore, four types of molecular fingerprints were introduced for providing comprehensive and detailed structural information of MOF materials. The top 20 most significant MACCS molecular fingerprints were picked out, revealing that the presence of six-membered ring structures and nitrogen atoms in the MOFs were the key structural factors that enhanced iodine adsorption, followed by the existence of oxygen atoms. This work combined high-throughput computation, machine learning, and molecular fingerprints to comprehensively elucidate the multifaceted factors influencing the iodine adsorption performance of MOFs, offering profound insightful guidelines for screening and structural design of advanced MOF materials.