Classifying superheavy elements by machine learning

Sheng Gong, Wei Wu, Fancy Qian Wang, Jie Liu, Yu Zhao, Yiheng Shen, Shuo Wang, Qiang Sun, and Qian Wang
Phys. Rev. A 99, 022110 – Published 8 February 2019

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.

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  • Received 4 December 2018

DOI:https://doi.org/10.1103/PhysRevA.99.022110

©2019 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied PhysicsAtomic, Molecular & Optical

Authors & Affiliations

Sheng Gong3,2, Wei Wu2, Fancy Qian Wang1,2, Jie Liu1,2, Yu Zhao2, Yiheng Shen1,2, Shuo Wang2, Qiang Sun2,1,*, and Qian Wang1,2,†

  • 1Center for Applied Physics and Technology, BKL-MEMD, College of Engineering, Peking University, Beijing 100871, China
  • 2Department of Materials Science and Engineering, College of Engineering, Peking University, Beijing 100871, China
  • 3Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

  • *sunqiang@pku.edu.cn
  • qianwang2@pku.edu.cn

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Issue

Vol. 99, Iss. 2 — February 2019

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