Akagi Kazuto, Naito Hisashi, Saikawa Takafumi, Kotani Motoko, Yoshikawa Hirofumi
Advanced Institute for Materials Research (AIMR), Tohoku University, 2-1-1 Katahira, Sendai, Miyagi, 980-8577, Japan.
Graduate School of Mathematics, Nagoya University, Furocho, Nagoya, 464-8602, Japan.
Sci Rep. 2024 May 26;14(1):12021. doi: 10.1038/s41598-024-62858-7.
Metal-organic frameworks (MOFs), self-assembled porous materials synthesized from metal ions and organic ligands, are promising candidates for the direct capture of CO from the atmosphere. In this work, we developed a regression model to predict the optimal component of the MOF that governs the amount of CO adsorption per volume based on experimentally observed adsorption and structure data combined with MOF adsorption sites. The structural descriptors were generated by topological data analysis with persistence diagrams, an advanced mathematical method for quantifying the rings and cavities within the MOF. This enables us to analyze direct effects and significance of the geometric structure of the MOF on the efficiency of CO adsorption in a novel way. The proposed approach is proved to be highly correlated with experimental data and thus offers an effective screening tool for MOFs with optimized structures.
金属有机框架材料（MOFs）是由金属离子和有机配体自组装而成的多孔材料，是直接从大气中捕获一氧化碳的有前景的候选材料。在这项工作中，我们开发了一种回归模型，基于实验观察到的吸附和结构数据以及MOF吸附位点，预测决定每单位体积一氧化碳吸附量的MOF的最佳成分。结构描述符是通过使用持久图的拓扑数据分析生成的，持久图是一种用于量化MOF内的环和腔的先进数学方法。这使我们能够以一种新颖的方式分析MOF几何结构对一氧化碳吸附效率的直接影响和重要性。所提出的方法被证明与实验数据高度相关，因此为具有优化结构的MOF提供了一种有效的筛选工具。
