DigiMOF: A Database of Metal-Organic Framework Synthesis Information Generated via Text Mining

Lawson T Glasby; Kristian Gubsch; Rosalee Bence; Rama Oktavian; Kesler Isoko; Seyed Mohamad Moosavi; Joan L Cordiner; Jason C Cole; Peyman Z Moghadam

doi:10.1021/acs.chemmater.3c00788

DigiMOF: A Database of Metal-Organic Framework Synthesis Information Generated via Text Mining

Chem Mater. 2023 May 18;35(11):4510-4524. doi: 10.1021/acs.chemmater.3c00788. eCollection 2023 Jun 13.

Authors

Lawson T Glasby¹, Kristian Gubsch¹, Rosalee Bence¹, Rama Oktavian¹, Kesler Isoko¹, Seyed Mohamad Moosavi², Joan L Cordiner¹, Jason C Cole³, Peyman Z Moghadam^{1

4}

Affiliations

¹ Department of Chemical and Biological Engineering, The University of Sheffield, Sheffield S1 3JD, U.K.
² Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, Ontario M5S 3E5, Canada.
³ Cambridge Crystallographic Data Centre, Cambridge CB2 1EZ, U.K.
⁴ Department of Chemical Engineering, University College London, London WC1E 7JE, U.K.

Abstract

The vastness of materials space, particularly that which is concerned with metal-organic frameworks (MOFs), creates the critical problem of performing efficient identification of promising materials for specific applications. Although high-throughput computational approaches, including the use of machine learning, have been useful in rapid screening and rational design of MOFs, they tend to neglect descriptors related to their synthesis. One way to improve the efficiency of MOF discovery is to data-mine published MOF papers to extract the materials informatics knowledge contained within journal articles. Here, by adapting the chemistry-aware natural language processing tool, ChemDataExtractor (CDE), we generated an open-source database of MOFs focused on their synthetic properties: the DigiMOF database. Using the CDE web scraping package alongside the Cambridge Structural Database (CSD) MOF subset, we automatically downloaded 43,281 unique MOF journal articles, extracted 15,501 unique MOF materials, and text-mined over 52,680 associated properties including the synthesis method, solvent, organic linker, metal precursor, and topology. Additionally, we developed an alternative data extraction technique to obtain and transform the chemical names assigned to each CSD entry in order to determine linker types for each structure in the CSD MOF subset. This data enabled us to match MOFs to a list of known linkers provided by Tokyo Chemical Industry UK Ltd. (TCI) and analyze the cost of these important chemicals. This centralized, structured database reveals the MOF synthetic data embedded within thousands of MOF publications and contains further topology, metal type, accessible surface area, largest cavity diameter, pore limiting diameter, open metal sites, and density calculations for all 3D MOFs in the CSD MOF subset. The DigiMOF database and associated software are publicly available for other researchers to rapidly search for MOFs with specific properties, conduct further analysis of alternative MOF production pathways, and create additional parsers to search for additional desirable properties.