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Distinguishing Metal-Organic Frameworks.

Senja Barthel1, Eugeny V Alexandrov2,3, Davide M Proserpio2,4

  • 1Laboratory of Molecular Simulation, Institut des Sciences et Ingénierie Chimiques, Valais, Ecole Polytechnique Fédérale de Lausanne (EPFL), Rue de l'Industrie 17, CH-1951 Sion, Switzerland.

Crystal Growth & Design
|March 16, 2018
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Summary
This summary is machine-generated.

This study introduces an algorithm to identify identical metal-organic frameworks (MOFs) based on their bond networks. The method ensures accurate MOF structure comparison, crucial for database analysis and materials discovery.

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Area of Science:

  • Materials Science
  • Chemistry
  • Computational Chemistry

Background:

  • Metal-organic frameworks (MOFs) are advanced porous materials with diverse applications.
  • Accurate identification and comparison of MOF structures are essential for database management and research.
  • Existing methods may not fully capture structural identity based on bonding.

Purpose of the Study:

  • To develop and present a novel algorithm for determining the identity of metal-organic frameworks.
  • To establish a robust method for comparing MOF structures based on their underlying bond networks.
  • To validate the algorithm's efficacy using a real-world MOF database.

Main Methods:

  • The algorithm defines MOFs as identical if they share the same bond network, considering atom types.
  • It employs a set of descriptors derived from the bond network to distinguish between structures.
  • The approach was tested on the CoRe MOF database, utilizing DFT-optimized structures and DDEC partial atomic charges via ToposPro.

Main Results:

  • The algorithm successfully distinguishes between different metal-organic framework structures.
  • It provides a reliable method for assessing MOF identity based on topological and chemical information.
  • Analysis of the CoRe MOF database demonstrated the algorithm's practical applicability.

Conclusions:

  • The presented algorithm offers a precise and efficient way to identify identical metal-organic frameworks.
  • This method enhances the reliability of MOF database analysis and facilitates materials discovery.
  • The approach is valuable for computational materials science and chemical informatics.