Understanding the diversity of the metal-organic framework ecosystem

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.

Unravelling thermal stress due to thermal expansion mismatch in metal–organic frameworks for methane storage

“…the void fraction. 77 To investigate the effect of the chemical building blocks on the thermal expansion, the other building blocks and topology should ideally be kept xed. 17 All 52 frameworks in our set display NTE behavior in the temperature range from 200 K to 400 K. We nd thermal expansion coefficients in a wide range from À5 MK À1 to À131 MK À1 with an average of À30 MK À1 in Table 2.…”

Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation

“…Preprocessing of data before the ML modeling can be also crucial for the success of analysis. To begin with, the missing values, duplications, or inconsistencies in the data, which are especially common in the data sets constructed from multiple sources, should be eliminated (inconsistencies among the MOF data, as reported by Moosavi et al, 124 were briefly discussed in the previous section). Various transformations on the descriptors (like normalization, standardization, or encoding) may also be needed while the dimensionality of the data can be reduced for smaller and more robust models as discussed above.…”

Too Many Materials and Too Many Applications: An Experimental Problem Waiting for a Computational Solution

“… 53 Indeed, the key to the success of this approach is to find effective metrics for the “diversity” between structures in the context of a particular application. 54 …”

“…The main question for a new material then becomes is this structure different enough from the others already included in the training set to justify the use of expensive molecular simulations over ML predictions? 54 …”