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

Analyzing the impact of sustainable economic development from the policy text network: Based on the practice of China's bay area policy

In order to break through the surface analysis of the content structure of policy texts, an in-depth discussion of the linkage between regional policy makers and objectives is helpful to analyze the formation mechanism of policy effects. Through social network analysis and multi-index analysis, this study takes the QianwanNew Area of Ningbo and the Guangdong-Hong Kong-Macao Greater Bay Area as representatives to explore the policy framework for the sustainable development of manufacturing industry in the two bay areas respectively. Through the construction of government department cooperation network, policy keyword co-occurrence network, department keyword correlation network, and the analysis of network density, network centrality, structural holes, and cohesive subgroups, it is found that the impact results show great differences, which is related to the network structure of manufacturing policy text.

Exfoliation of 2D Metal-Organic Frameworks: toward Advanced Scalable Materials for Optical Sensing

Two-dimensional metal-organic frameworks (MOFs) occupy a special place among the large family of functional 2D materials. Even at a monolayer level, 2D MOFs exhibit unique sensing, separation, catalytic, electronic, and conductive properties due to the combination of porosity and organo-inorganic nature. However, lab-to-fab transfer for 2D MOF layers faces the challenge of their scalability, limited by weak interactions between the organic and inorganic building blocks. Here, comparing three top-down approaches to fabricate 2D MOF layers (sonication, freeze-thaw, and mechanical exfoliation), The technological criteria have established for creation of the layers of the thickness up to 1 nm with a record aspect ratio up to 2*10^4:1. The freezing-thaw and mechanical exfoliation are the most optimal approaches; wherein the rate and manufacturability of the mechanical exfoliation rivaling the greatest scalability of 2D MOF layers obtained by freezing-thaw (21300:1 vs 1330:1 aspect ratio), leaving the sonication approach behind (with a record 900:1 aspect ratio) have discovered. The high quality 2D MOF layers with a record aspect ratio demonstrate unique optical sensitivity to solvents of a varied polarity, which opens the way to fabricate scalable and freestanding 2D MOF-based atomically thin chemo-optical sensors by industry-oriented approach.

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

In material informatics, the representation of the material structure is fundamentally essential to obtaining better prediction results, and graph representation has attracted much attention in recent years. Molecular crystals can be graphically represented in molecular and crystal representations, but a comparison of which representation is more effective has not been examined. In this study, we compared the prediction accuracy between molecular and crystal graphs for band gap prediction. The results showed that the prediction accuracies using crystal graphs were better than those obtained using molecular graphs. While this result is not surprising, error analysis quantitatively evaluated that the error of the crystal graph was 0.4 times that of the molecular graph with moderate correlation. The novelty of this study lies in the comparison of molecular crystal representations and in the quantitative evaluation of the contribution of crystal structures to the band gap.

MOFGalaxyNet: a social network analysis for predicting guest accessibility in metal-organic frameworks utilizing graph convolutional networks

Metal-organic frameworks (MOFs), are porous crystalline structures comprising of metal ions or clusters intricately linked with organic entities, displaying topological diversity and effortless chemical flexibility. These characteristics render them apt for multifarious applications such as adsorption, separation, sensing, and catalysis. Predominantly, the distinctive properties and prospective utility of MOFs are discerned post-manufacture or extrapolation from theoretically conceived models. For empirical researchers unfamiliar with hypothetical structure development, the meticulous crystal engineering of a high-performance MOF for a targeted application via a bottom-up approach resembles a gamble. For example, the precise pore limiting diameter (PLD), which determines the guest accessibility of any MOF cannot be easily inferred with mere knowledge of the metal ion and organic ligand. This limitation in bottom-up conceptual understanding of specific properties of the resultant MOF may contribute to the cautious industrial-scale adoption of MOFs.Consequently, in this study, we take a step towards circumventing this limitation by designing a new tool that predicts the guest accessibility-a MOF key performance indicator-of any given MOF from information on only the organic linkers and the metal ions. This new tool relies on clustering different MOFs in a galaxy-like social network, MOFGalaxyNet, combined with a Graphical Convolutional Network (GCN) to predict the guest accessibility of any new entry in the social network. The proposed network and GCN results provide a robust approach for screening MOFs for various host-guest interaction studies.

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

There is a growing trend toward the use of interaction network methods and algorithms, including community-based detection methods, in various fields of science. The approach is already used in many applications, for example, in social sciences and health informatics to analyze behavioral patterns during the COVID-19 pandemic, protein-protein networks in biological sciences, agricultural science, economy, and so forth. This paper attempts to build interaction networks based on high-entropy alloy (HEA) descriptors in order to discover HEA communities with similar functionality. In addition, these communities could be leveraged to discover new alloys not yet included in the data set without any experimental laboratory effort. This research has been carried out using two community detection algorithms, the Louvain algorithm and the enhanced particle swarm optimization (PSO) algorithm. The data set, which is used in this paper, includes 90 HEAs and 6 descriptors. The results reveal 13 alloy communities, and the accuracy of the results is validated by the modularity. The experimental results show that the method with the PSO-based community detection algorithm can achieve alloy communities with an average accuracy improvement of 0.26 compared to the Louvain algorithm. Furthermore, some characteristics of HEAs, for example, their phase composition, could be predicted by the extracted communities. Also, the HEA phase composition has been predicted by the proposed method and achieved about 93% precision.