Classifying superheavy elements by machine learning
Name
PhysRevA.99.022110.pdf
Size
1.85 MB
Format
Adobe PDF
Checksum (MD5)
b29b3e6eae530c9da1c20d1c7ee66b80
Author(s)
Gong, Sheng
Wu, Wei
Wang, Fancy Qian
Liu, Jie
Zhao, Yu
Shen, Yiheng
Wang, Shuo
Sun, Qiang
Wang, Qian
Date Issued
February 2019
Journal
Physical Review A
Publisher
American Physical Society
Citation
Gong, Sheng et al. "Classifying superheavy elements by machine learning." Physical Review A 99, 2 (February 2019): 022110 © 2019 American Physical Society
Version
Final published version
Abstract
Among the 118 elements listed in the periodic table, there are nine superheavy elements (Mt, Ds, Mc, Rg, Nh, Fl, Lv, Ts, and Og) that have not yet been well studied experimentally because of their limited half-lives and production rates. How to classify these elements for further study remains an open question. For superheavy elements, although relativistic quantum-mechanical calculations for the single atoms are more accurate and reliable than those for their molecules and crystals, there is no study reported to classify elements solely based on atomic properties. By using cutting-edge machine learning techniques, we find the relationship between atomic data and classification of elements, and further identify that Mt, Ds, Mc, Rg, Lv, Ts, and Og should be metals, while Nh and Fl should be metalloids. These findings not only highlight the significance of machine learning for superheavy atoms but also challenge the conventional belief that one can determine the characteristics of an element only by looking at its position in the table.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Terms of Use
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DOI of Published Version
https://doi.org/10.1103/PhysRevA.99.022110
