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

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.