Uncovering the Relationship between Metal Elements and Mechanical Stability for Metal–Organic Frameworks

Assessing the mechanical robustness of metal-organic frameworks (MOFs) is crucial to enhance their applicability in various fields. Although considerable research has been conducted on the relationship between the mechanical properties of MOFs and their structural features (such as pore size, surface area, and topology), the insufficient exploration of metal elements has prevented researchers from fully understanding their mechanical behavior. To plug this knowledge gap, we constructed a database of mechanical properties for 20,342 MOFs included in the QMOF database using molecular simulations to investigate the impact of metal elements on mechanical stability. Through Shapley additive explanations (SHAP) analysis, we found that Co and Ln could enhance the structural stability of MOFs. We validated these findings using newly generated hypothetical MOFs. Notably, we adopted an interpretable machine learning technique to analyze the contribution of remarkably diverse metal elements in the 20,342 MOFs to the mechanical properties of each MOF. We anticipate that this research will serve as a valuable tool for future studies on identifying mechanically robust MOFs suitable for various industrial applications.

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Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2024-09-23
DOI
https://doi.org/10.1021/acsami.4c07775
Citations
8
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.94

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article

Uncovering the Relationship between Metal Elements and Mechanical Stability for Metal–Organic Frameworks

Heekyu Kim, Kyoungmin Min, Jaejun Lee, Jaejung Park et al.
8 citations
ACS Applied Materials & Interfaces
Machine Learning in Materials Science
0.94
article

Uncovering the Relationship between Metal Elements and Mechanical Stability for Metal–Organic Frameworks

Heekyu Kim, Kyoungmin Min, Jaejun Lee, Jaejung Park, Inhyo Lee, Seung‐Chul Lee, Minseon Kim
article en
8 citations

Abstract

Assessing the mechanical robustness of metal-organic frameworks (MOFs) is crucial to enhance their applicability in various fields. Although considerable research has been conducted on the relationship between the mechanical properties of MOFs and their structural features (such as pore size, surface area, and topology), the insufficient exploration of metal elements has prevented researchers from fully understanding their mechanical behavior. To plug this knowledge gap, we constructed a database of mechanical properties for 20,342 MOFs included in the QMOF database using molecular simulations to investigate the impact of metal elements on mechanical stability. Through Shapley additive explanations (SHAP) analysis, we found that Co and Ln could enhance the structural stability of MOFs. We validated these findings using newly generated hypothetical MOFs. Notably, we adopted an interpretable machine learning technique to analyze the contribution of remarkably diverse metal elements in the 20,342 MOFs to the mechanical properties of each MOF. We anticipate that this research will serve as a valuable tool for future studies on identifying mechanically robust MOFs suitable for various industrial applications.

ACS Applied Materials & InterfacesVol. 16(39)
Pohang University of Science and Technology (KR), Soongsil University (KR), Korea Advanced Institute of Science and Technology (KR)
National Research Foundation of Korea
Openalex Percentile: Top 23%
Machine Learning in Materials Science
0.94
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