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揭示金属有机框架中金属元素与机械稳定性之间的关系。

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

作者信息

Lee Inhyo, Lee Jaejun, Kim Minseon, Park Jaejung, Kim Heekyu, Lee Seungchul, Min Kyoungmin

机构信息

School of Mechanical Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Republic of Korea.

Department of Mechanical Engineering, Pohang University of Science and Technology, 77 Cheongam-ro, Pohang 37673, Republic of Korea.

出版信息

ACS Appl Mater Interfaces. 2024 Oct 2;16(39):52162-52178. doi: 10.1021/acsami.4c07775. Epub 2024 Sep 22.

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.

摘要

评估金属有机框架材料(MOFs)的机械稳定性对于提高其在各个领域的适用性至关重要。尽管已经对MOFs的机械性能与其结构特征(如孔径、表面积和拓扑结构)之间的关系进行了大量研究,但对金属元素的探索不足阻碍了研究人员全面了解其机械行为。为了填补这一知识空白,我们使用分子模拟构建了一个包含QMOF数据库中20342种MOFs机械性能的数据库,以研究金属元素对机械稳定性的影响。通过Shapley加法解释(SHAP)分析,我们发现Co和Ln可以增强MOFs的结构稳定性。我们使用新生成的假设MOFs验证了这些发现。值得注意的是,我们采用了一种可解释的机器学习技术来分析20342种MOFs中显著不同的金属元素对每种MOF机械性能的贡献。我们预计这项研究将成为未来识别适用于各种工业应用的机械稳定MOFs研究的宝贵工具。

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