MOFSocialNet: Exploiting Metal-Organic Framework Relationships via Social Network Analysis

Abstract

The number of metal-organic frameworks (MOF) as well as the number of applications of this material are growing rapidly. With the number of characterized compounds exceeding 100,000, manual sorting becomes impossible. At the same time, the increasing computer power and established use of automated machine learning approaches makes data science tools available, that provide an overview of the MOF chemical space and support the selection of suitable MOFs for a desired application. Among the different data science tools, graph theory approaches, where data generated from numerous real-world applications is represented as a graph (network) of interconnected objects, has been widely used in a variety of scientific fields such as social sciences, health informatics, biological sciences, agricultural sciences and economics. We describe the application of a particular graph theory approach known as social network analysis to MOF materials and highlight the importance of community (group) detection and graph node centrality. In this first application of the social network analysis approach to MOF chemical space, we created MOFSocialNet. This social network is based on the geometrical descriptors of MOFs available in the CoRE-MOFs database. MOFSocialNet can discover communities with similar MOFs structures and identify the most representative MOFs within a given community. In addition, analysis of MOFSocialNet using social network analysis methods can predict MOF properties more accurately than conventional ML tools. The latter advantage is demonstrated for the prediction of gas storage properties, the most important property of these porous reticular networks.

Machine Learning Approach to Community Detection in a High-Entropy Alloy Interaction Network

“…For a demonstration, they used social network analysis to identify the most representative MOFs in this research data set and to detect MOF communities. 33 …”

“…MOFSocialNet is able to guide MOF researchers through the vast chemical space of existing and hypothetical MOFs. For a demonstration, they used social network analysis to identify the most representative MOFs in this research data set and to detect MOF communities …”

“…For a demonstration, they used social network analysis to identify the most representative MOFs in this research data set and to detect MOF communities. 33 In another study carried out by Ahajjam et al, 25 a scalable and deterministic approach is proposed to identify communities using leader nodes called the community leader recognition approach. Their approach has two main steps: the first step is to retrieve the leaders and the second step is to identify the community using the similarities between the nodes.…”

Graph Comparison of Molecular Crystals in Band Gap Prediction Using Neural Networks

“…In recent years, graph representation has received a great deal of attention. Molecular structures can be represented as graph data consisting of nodes and edges, and graph neural networks (GNNs) can handle regression and classification tasks. − Recent studies on inorganic crystals also have successfully used graphs for inorganic crystal structures. − Graph-based approaches have been applied not only to molecules and bulk inorganic materials but also to metal–organic frameworks, two-dimensional materials, and even molecular dynamics simulations. − Furthermore, model architectures such as ALIGNN and M3GNet, which incorporate angle information, have been developed to improve prediction accuracy. , …”

Materials Informatics of Molecular Crystals Compared by Molecular and Crystal Representations: The Case of Band Gap Prediction

“…[9,10,[13][14][15][16][17] Graph-based approaches have attracted increasing attention in material research, and have been applied not only to molecules and bulk inorganics, but also metal-organic frameworks, two-dimensional materials, and further material simulations. [18][19][20] In contrast, MI research on organic molecular crystals has progressed to a lesser extent than polymers and inorganic crystals. This could be due to the shortage of databases containing molecular crystal structures and property data.…”