Classifying superheavy elements by machine learning
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 op...
| Main Authors: | , , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2019
|
| Online Access: | http://hdl.handle.net/1721.1/120709 https://orcid.org/0000-0002-7457-7959 |
| _version_ | 1826192030606295040 |
|---|---|
| author | Gong, Sheng Wu, Wei Wang, Fancy Qian Liu, Jie Zhao, Yu Shen, Yiheng Wang, Shuo Sun, Qiang Wang, Qian |
| author2 | Massachusetts Institute of Technology. Department of Materials Science and Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Materials Science and Engineering Gong, Sheng Wu, Wei Wang, Fancy Qian Liu, Jie Zhao, Yu Shen, Yiheng Wang, Shuo Sun, Qiang Wang, Qian |
| author_sort | Gong, Sheng |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T09:05:14Z |
| format | Article |
| id | mit-1721.1/120709 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T09:05:14Z |
| publishDate | 2019 |
| publisher | American Physical Society |
| record_format | dspace |
| spelling | mit-1721.1/1207092022-09-30T13:20:59Z Classifying superheavy elements by machine learning Gong, Sheng Wu, Wei Wang, Fancy Qian Liu, Jie Zhao, Yu Shen, Yiheng Wang, Shuo Sun, Qiang Wang, Qian Massachusetts Institute of Technology. Department of Materials Science and Engineering Gong, Sheng 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. 2019-03-04T19:59:32Z 2019-03-04T19:59:32Z 2019-02 2018-12 2019-02-08T18:00:30Z Article http://purl.org/eprint/type/JournalArticle 2469-9926 2469-9934 http://hdl.handle.net/1721.1/120709 Gong, Sheng et al. "Classifying superheavy elements by machine learning." Physical Review A 99, 2 (February 2019): 022110 © 2019 American Physical Society https://orcid.org/0000-0002-7457-7959 en http://dx.doi.org/10.1103/PhysRevA.99.022110 Physical Review A Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. American Physical Society application/pdf American Physical Society American Physical Society |
| spellingShingle | Gong, Sheng Wu, Wei Wang, Fancy Qian Liu, Jie Zhao, Yu Shen, Yiheng Wang, Shuo Sun, Qiang Wang, Qian Classifying superheavy elements by machine learning |
| title | Classifying superheavy elements by machine learning |
| title_full | Classifying superheavy elements by machine learning |
| title_fullStr | Classifying superheavy elements by machine learning |
| title_full_unstemmed | Classifying superheavy elements by machine learning |
| title_short | Classifying superheavy elements by machine learning |
| title_sort | classifying superheavy elements by machine learning |
| url | http://hdl.handle.net/1721.1/120709 https://orcid.org/0000-0002-7457-7959 |
| work_keys_str_mv | AT gongsheng classifyingsuperheavyelementsbymachinelearning AT wuwei classifyingsuperheavyelementsbymachinelearning AT wangfancyqian classifyingsuperheavyelementsbymachinelearning AT liujie classifyingsuperheavyelementsbymachinelearning AT zhaoyu classifyingsuperheavyelementsbymachinelearning AT shenyiheng classifyingsuperheavyelementsbymachinelearning AT wangshuo classifyingsuperheavyelementsbymachinelearning AT sunqiang classifyingsuperheavyelementsbymachinelearning AT wangqian classifyingsuperheavyelementsbymachinelearning |