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Synthesis and Characterization of Functionalized Metal-organic Frameworks

Synthesis and Characterization of Functionalized Metal-organic Frameworks

Published on: September 5, 2014

Text Mining Metal-Organic Framework Papers.

Sanghoon Park¹, Baekjun Kim¹, Sihoon Choi¹

¹Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST) , 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea.

Journal of Chemical Information and Modeling

|December 12, 2017

Summary

This summary is machine-generated.

A new text mining algorithm efficiently extracts surface area and pore volume data from metal-organic framework (MOF) HTML files. This tool aids in discovering MOF structure-property relationships and compiling extensive reference datasets.

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Area of Science:

Materials Science
Computational Chemistry
Data Science

Background:

Accurate characterization of material properties like surface area and pore volume is crucial for advancing materials science.
Metal-organic frameworks (MOFs) possess tunable properties, making their large-scale data collection and analysis essential for discovering structure-property relationships.
Manual data extraction from scientific literature is time-consuming and prone to errors.

Purpose of the Study:

To develop and validate a text mining algorithm for automated extraction of surface area and pore volume data from MOF manuscripts.
To assess the algorithm's accuracy and identify limitations in processing diverse data formats.

Main Methods:

A text mining algorithm was designed to identify surface area and pore volume values by searching for common units (e.g., m²/g, cm³/g) within HTML manuscript files.
The algorithm was trained and tested on a dataset of over 200 MOFs, including a sample set and a randomly chosen test set.

Main Results:

The algorithm achieved high accuracy on the sample set, identifying 90% of surface area values and 88.8% of pore volume values.
On a randomly selected test set, accuracies were 73.2% for surface area and 85.1% for pore volume.
Identified errors were primarily due to complex sentence structures and ambiguous MOF naming conventions (e.g., bolded notations).

Conclusions:

The developed text mining algorithm offers a promising approach for the automated and efficient extraction of key physical properties of MOFs.
This tool can significantly accelerate the process of building comprehensive databases for MOFs, facilitating the exploration of structure-property correlations.
Further refinement of the algorithm is needed to address challenges posed by varied data presentation in scientific literature.