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.

Automated summary

This paper builds interaction networks based on high-entropy alloy descriptors to discover HEA communities with similar functionality. Using two community detection algorithms, the research reveals 13 alloy communities and shows that the PSO-based algorithm outperforms the Louvain algorithm in accuracy. Additionally, characteristics of HEAs and phase composition can be predicted by the extracted communities.