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Updated: May 10, 2026

Synthesis and Characterization of Functionalized Metal-organic Frameworks

Synthesis and Characterization of Functionalized Metal-organic Frameworks

Published on: September 5, 2014

Data Management and Analysis of Metal-Organic Framework Synthesis Using Data Models.

Felix Neubauer¹, Kenichi Endo², Frederic Bender³

¹Institute for Parallel and Distributed Systems, University of Stuttgart, Universitätsstraße 32, Stuttgart 70569, Germany.

Journal of Chemical Information and Modeling

|May 8, 2026

Summary

This study introduces a data model and workflow to make metal-organic framework (MOF) synthesis FAIR and AI-ready. This enhances reproducibility and accelerates the discovery of new MOFs through data-driven optimization.

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Published on: October 6, 2023

Area of Science:

Materials Science
Chemistry
Data Science

Background:

Reproducible synthesis of metal-organic frameworks (MOFs) relies on detailed procedural documentation.
Current documentation practices hinder data sharing, reproducibility, and AI-driven optimization.
Making synthesis data findable, accessible, interoperable, and reusable (FAIR) is crucial for advancing MOF research.

Purpose of the Study:

To develop a machine-readable data model and processing workflow for MOF synthesis and characterization.
To ensure data quality, enable interoperability, and facilitate data-driven analysis.
To promote the digitalization of synthetic chemistry and accelerate MOF discovery.

Main Methods:

Development of a JSON Schema data model for MOF synthesis and characterization.
Implementation of a data-processing workflow to parse, validate, and serialize synthesis data.
Application of the workflow to Fe-terephthalate MOF and MOCOF-1 systems, with powder X-ray diffraction (PXRD) data analysis.
Utilizing a decision tree for PXRD data analysis to identify critical synthesis parameters.

Main Results:

Demonstrated feasibility and usefulness of the data model and workflow with two MOF systems.
Successful parsing, validation, and serialization of synthesis and characterization data.
Identification of critical synthesis parameters influencing phase selectivity and yield through PXRD data analysis.
The workflow proved modular, extensible, and adaptable to various data sources and analyses.

Conclusions:

The proposed data model and workflow make MOF synthesis FAIR and AI-ready.
This strategy fosters the digitalization of synthetic chemistry and accelerates materials discovery.
The approach enhances reproducibility and enables systematic optimization of MOF synthesis procedures.