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

Abstract

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.

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

“…Hence in this study, we designed a new approach for predicting the guest accessibility of any MOF by applying a node classification algorithm using graph convolutional networks (GCNs) on a MOF social network, MOFGalaxyNet, created from a social analysis of the metal ions and organic ligands. Social network analysis (SNA) was initially established in the field of social sciences [ 12 , 13 ] but has been expanded to health informatics [ 14 – 17 ], agriculture [ 18 ], life sciences [ 19 , 20 ], economy [ 21 , 22 ], and materials science [ 23 , 24 ]. Graphs are now becoming ubiquitous because of their ability to model complex systems of various kinds, requiring only little adaption to a specific case.…”