In Silico Investigation into H2 Uptake in MOFs: Combined Text/Data Mining and Structural Calculations.,Langmuir

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In Silico Investigation into H2 Uptake in MOFs: Combined Text/Data Mining and Structural Calculations.
Langmuir ( IF 3.7 ) Pub Date : 2019-12-31 , DOI: 10.1021/acs.langmuir.9b03618
Omer Tayfuroglu ₁ , Abdulkadir Kocak ₁ , Yunus Zorlu ₁

Affiliation

Metal-organic frameworks (MOFs) with high surface areas and adjustable lattice structures are attractive for gas storage and thus have been a great interest in research. Although tremendous amount of data on MOFs have been available in the literature, there are very few studies considering methodological approach for H2 uptake properties of MOFs. In this study, we systematically investigated the H2 uptake capabilities of MOFs by means of text and data mining (TDM) through retrieving data of the surface areas (SA) and pore volumes (PV) from published manuscripts. In addition, we calculated theoretical SA and PV values of all real MOFs available in Cambridge Structural Database (CSD). Prior to calculation, we applied an automated structure analysis algorithm that loads the coordinates of molecules from CSD experimental X-ray single-crystal structure and removes guest/solvent contaminants from the structure. We compared SA, PV, and H2 uptake data from both TDM and structural calculation techniques and unraveled a list of MOFs with H2 uptakes predicted from both experimental and theoretical SA/PV values that may be regarded as the most promising candidates for H2 storage. The extensive and systematic TDM strategy estimates 5975 experimental SA and 7748 experimental PV values (2080 MOFs with SA + PV values) with 78-82% success rate. In addition, structural calculations reveal the theoretical SA and PV values along with a theoretical H2 adsorption limit of MOFs in the absence of guest molecules. Combination of both TDM and structural calculation strategies provides a more comprehensive perspective for the investigation of hydrogen storage capacities in MOFs, which elucidates plausibility of new compounds as candidates for H2 storage materials.

中文翻译：

在计算机科学中对MOF中的H2吸收进行调查：文本/数据挖掘和结构计算相结合。

具有高表面积和可调节晶格结构的金属有机框架（MOF）对于气体存储具有吸引力，因此引起了人们极大的研究兴趣。尽管文献中已经提供了大量有关MOF的数据，但很少有研究针对MOF的H2吸收特性的方法学方法进行研究。在这项研究中，我们通过从已发表的手稿中获取表面积（SA）和孔体积（PV）的数据，通过文本和数据挖掘（TDM）系统地研究了MOF的H2吸收能力。此外，我们计算了剑桥结构数据库（CSD）中所有可用MOF的理论SA和PV值。在计算之前，我们应用了自动结构分析算法，该算法从CSD实验X射线单晶结构中加载分子的坐标，并从该结构中去除了来宾/溶剂污染物。我们比较了来自TDM和结构计算技术的SA，PV和H2吸收数据，并列出了从实验和理论SA / PV值预测的H2吸收的MOF列表，这些SA / PV值可能被认为是最有希望的H2储存候选者。广泛而系统的TDM策略可估算5975个实验SA和7748个实验PV值（具有SA + PV值的2080个MOF），成功率为78-82％。此外，结构计算揭示了在没有客体分子的情况下，MOF的理论SA和PV值以及理论H2吸附极限。

更新日期：2020-01-01

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