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Updated: May 12, 2026

Synthesis and Characterization of Functionalized Metal-organic Frameworks
Published on: September 5, 2014
Predicting the Thermodynamic Limits of Metal-Organic Framework Metastability
Blake Dallmann1, Aryan Saha2, Andrew S Rosen1
1Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544, United States.
This study reveals that all metal-organic frameworks (MOFs) are thermodynamically metastable, with porosity incurring an energetic penalty. The energy above hull metric predicts MOF synthesizability and aids machine learning model development.
Area of Science:
- Materials Science
- Computational Chemistry
- Chemical Engineering
Background:
- Metal-organic frameworks (MOFs) offer vast potential due to their tunable structures.
- Understanding the thermodynamic stability of MOFs is crucial for their practical applications.
- Predicting MOF stability and synthesizability remains a significant challenge.
Purpose of the Study:
- To computationally determine the thermodynamic stability of over 20,000 MOFs and coordination polymers.
- To investigate factors influencing MOF metastability, including composition and constituent elements.
- To establish the energy above hull as a metric for MOF synthesizability and to create a comprehensive database.
Main Methods:
- Density functional theory (DFT) calculations were employed to compute formation energies.
- Convex hull phase diagrams were constructed to assess thermodynamic stability.
- The energy above hull (E_above_hull) was calculated as a primary stability indicator.
Main Results:
- All investigated MOFs were found to be thermodynamically metastable.
- Permanent porosity was associated with an inherent energetic penalty, increasing metastability.
- MOF composition and metal/linker identity significantly influence the degree of metastability.
Conclusions:
- The energy above hull serves as a reliable metric for predicting MOF synthesizability.
- The QMOF-Thermo Database provides accessible thermodynamic data for MOFs and coordination polymers.
- Machine learning interatomic potentials can be benchmarked and improved using this database for predicting MOF stability.
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