Seyed Mohamad Moosavi^1,2*, Aditya Nandy², Kevin Maik Jablonka¹, Daniele Ongari¹, Jon Paul Janet², Peter G. Boyd¹, Yongjin Lee³, Berend Smit^1*, Heather J. Kulik^2*

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

2 Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States

3 School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China

* Corresponding authors emails: seyedmohamad.moosavi@epfl.ch, berend.smit@epfl.ch, hjkulik@mit.edu

DOI10.24435/materialscloud:3y-gr [version v1]

Publication date: Jun 26, 2020

How to cite this record

Seyed Mohamad Moosavi, Aditya Nandy, Kevin Maik Jablonka, Daniele Ongari, Jon Paul Janet, Peter G. Boyd, Yongjin Lee, Berend Smit, Heather J. Kulik, Understanding the diversity of the metal-organic framework ecosystem, Materials Cloud Archive 2020.67 (2020), https://doi.org/10.24435/materialscloud:3y-gr

Description

By combining metal nodes and organic linkers one can make millions of different metal-organic frameworks (MOFs). At present over 90,000 MOFs have been synthesized and there are databases with over 500,000 predicted structures. This raises the question of whether a new experimental or predicted structure adds new information. For MOF-chemists the chemical design space is a combination of pore geometry, metal nodes, organic linkers, and functional groups, but at present we do not have a formalism to quantify optimal coverage of chemical design space. In this work, we show how machine learning can be used to quantify similarities of MOFs. This quantification allows us to use techniques from ecology to analyse the chemical diversity of these materials in terms of diversity metrics. In particular, we show that this diversity analysis can identify biases in the databases, and how such bias can lead to incorrect conclusions. This formalism provides us with a simple and powerful practical guideline to see whether a set of new structures will have the potential for new insights, or constitute a relatively small variation of existing structures.

Materials Cloud sections using this data

Files

File name	Size	Description
README.txt MD5md5:45f1ae671784be292291717c0a3bfcc6	3.9 KiB	Description of files included in this repository
MOF_diversity.tar.gz MD5md5:228d66f4c020796d94711624ed0eb731	2.4 GiB	This Tarball contains the structures, features and labels, feature importance, diversity metrics, timeline, and force field parameters, exploratory data analysis that were used and needed to reproduce the study.

License

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External references

Keywords

machine learning metal-organic frameworks diversity chemical space