Abstract
Similarity analysis of chemical elements has long been studied from various perspectives. With the help of complex network theory, we study similarities from the network perspective using an undirected chemical network with 97 elements and 2198 edges. We proposed a new similarity index using the number of common neighbor elements as well as common non-neighbor elements. It is shown that this similarity index can be used to measure similarities between elements as well as group similarities among elements, and most similar elements are located near each other in the Periodic Table. Moreover, we find a similarity relation for 19 elements that have eight surrounding elements. This relation indicates that the tendency of the central element to form binary compounds is closely related to the surrounding eight elements, which can be used to predict potential binary compounds, thus provides a new way to study chemistry.
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The data used in this paper is attached as the supplementary file in our previous paper listed in [10]. In this paper, data is also uploaded as the supplementary material.
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Acknowledgements
The author would like to thank the anonymous reviewer for valuable suggestions and comments. This work was supported by the National Natural Science Foundation of China under Grant No. 70971089.
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Mao, G., Liu, R. & Zhang, N. Similarity analysis of chemical elements based on compounds network. J Math Chem 61, 1522–1531 (2023). https://doi.org/10.1007/s10910-023-01473-9
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DOI: https://doi.org/10.1007/s10910-023-01473-9