With ever-growing numbers of metal-organic framework (MOF) materials being reported, new computational approaches are required for a quantitative understanding of structure-property correlations in MOFs. Here, we show how structural coarse-graining and embedding ("unsupervised learning") schemes can together give new insights into the geometric diversity of MOF structures. Based on a curated data set of 1262 reported experimental structures, we automatically generate coarse-grained and rescaled representations which we couple to a kernel-based similarity metric and to widely used embedding schemes. This approach allows us to visualize the breadth of geometric diversity within individual topologies and to quantify the distributions of local and global similarities across the structural space of MOFs. The methodology is implemented in an openly available Python package and is expected to be useful in future high-throughput studies.

Visualization and Quantification of Geometric Diversity in Metal–Organic Frameworks / T.C. Nicholas, E.V. Alexandrov, V.A. Blatov, A.P. Shevchenko, D.M. Proserpio, A.L. Goodwin, V.L. Deringer. - In: CHEMISTRY OF MATERIALS. - ISSN 0897-4756. - 33:21(2021), pp. 8289-8300. [10.1021/acs.chemmater.1c02439]

Visualization and Quantification of Geometric Diversity in Metal–Organic Frameworks

D.M. Proserpio
;
2021

Abstract

With ever-growing numbers of metal-organic framework (MOF) materials being reported, new computational approaches are required for a quantitative understanding of structure-property correlations in MOFs. Here, we show how structural coarse-graining and embedding ("unsupervised learning") schemes can together give new insights into the geometric diversity of MOF structures. Based on a curated data set of 1262 reported experimental structures, we automatically generate coarse-grained and rescaled representations which we couple to a kernel-based similarity metric and to widely used embedding schemes. This approach allows us to visualize the breadth of geometric diversity within individual topologies and to quantify the distributions of local and global similarities across the structural space of MOFs. The methodology is implemented in an openly available Python package and is expected to be useful in future high-throughput studies.
Metal organic frameworks; Machine Learning methods
Settore CHIM/03 - Chimica Generale e Inorganica
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/882834
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