Abstract
Millions of distinct metal-organic frameworks (MOFs) can be made by combining metal nodes and organic linkers. At present, over 90,000 MOFs have been synthesized and over 500,000 predicted. This raises the question 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 develop a machine learning method to quantify similarities of MOFs to analyse their chemical diversity. This diversity analysis identifies biases in the databases, and we show that such bias can lead to incorrect conclusions. The developed formalism in this study provides a simple and practical guideline to see whether new structures will have the potential for new insights, or constitute a relatively small variation of existing structures.
Highlights
Millions of distinct metal-organic frameworks (MOFs) can be made by combining metal nodes and organic linkers
We introduce a systematic approach to quantify the chemical diversity of the different MOF libraries, and use these insights to remove these biases from the different libraries
One of the aims of this work is to express the diversity of a MOF database in terms of features that can be related to the chemistry that is used in synthesizing MOFs as well as generating the libraries of hypothetical structures
Summary
Millions of distinct metal-organic frameworks (MOFs) can be made by combining metal nodes and organic linkers. One can envision an extremely successful experimental group focusing on the systematic synthesis of a particular class of MOFs for a specific application Such successes may stimulate other groups to synthesise similar MOFs, which may bias research efforts towards this class of MOFs. In libraries of hypothetical MOFs, biases can be introduced by algorithms that favour the generation of a specific subsets of MOFs. At present, we do not have a theoretical framework to evaluate chemical diversity of MOFs. At present, we do not have a theoretical framework to evaluate chemical diversity of MOFs Such a framework is essential to identify possible biases, quantify the diversity of these libraries, and develop optimal screening strategies. The question on how to correctly sample material design space holds for many classes of materials
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