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Understanding the diversity of the metal-organic framework ecosystem

Seyed Mohamad Moosavi1,2*, Aditya Nandy2, Kevin Maik Jablonka1, Daniele Ongari1, Jon Paul Janet2, Peter G. Boyd1, Yongjin Lee3, Berend Smit1*, Heather J. Kulik2*

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), doi: 10.24435/materialscloud:3y-gr.


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.

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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.


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machine learning metal-organic frameworks diversity chemical space

Version history:

2020.67 (version v1) [This version] Jun 26, 2020 DOI10.24435/materialscloud:3y-gr