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Large-scale screening of hypothetical metal-organic frameworks
Christopher E Wilmer1, Michael Leaf, Chang Yeon Lee
1Department of Chemical and Biological Engineering, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208, USA.
Nature Chemistry
|January 25, 2012
Summary
Researchers computationally generated over 137,000 metal-organic frameworks (MOFs) to discover new materials. They identified over 300 MOFs with superior methane storage capacity, validating one experimentally.
Area of Science:
- Materials Science
- Computational Chemistry
- Nanotechnology
Background:
- Metal-organic frameworks (MOFs) are highly porous crystalline materials built from metal ions and organic linkers.
- The vast combinatorial space of potential MOF structures remains largely unexplored due to synthetic challenges.
- Existing MOFs represent a small fraction of theoretically possible structures, limiting their application potential.
Purpose of the Study:
- To develop and apply a computational strategy for generating and screening hypothetical MOFs.
- To identify novel MOF structures with enhanced methane storage capabilities.
- To uncover structure-property relationships guiding MOF design.
Main Methods:
- Generation of a large database of hypothetical MOFs (137,953) from a library of 102 building blocks.
- High-throughput computational screening of MOFs for pore-size distribution, surface area, and methane storage capacity.
- Experimental synthesis and characterization of a top-performing predicted MOF.
Main Results:
- Identification of over 300 hypothetical MOFs exhibiting superior predicted methane storage capacity compared to known materials.
- Discovery of structure-property correlations, highlighting methyl-functionalized MOFs as high-performance candidates.
- Experimental validation of the predicted methane storage capacity for a selected MOF.
Conclusions:
- Computational high-throughput screening is an effective approach to explore the vast MOF design space.
- Novel MOFs with significantly enhanced methane storage properties can be computationally discovered.
- This methodology accelerates the discovery of advanced materials for gas storage applications.
