4.6 Article

A novel superhard tungsten nitride predicted by machine-learning accelerated crystal structure search

Journal

SCIENCE BULLETIN
Volume 63, Issue 13, Pages 817-824

Publisher

ELSEVIER
DOI: 10.1016/j.scib.2018.05.027

Keywords

Tungsten nitride; Transition metal nitrides; Machine-learning accelerated crystal; structure searching method; Superhard tungsten nitride

Funding

  1. Ministry of Science and Technology of the People's Republic of China [2016YFA0300404, 2015CB921202]
  2. National Natural Science Foundation of China [51372112, 11574133]
  3. NSF of Jiangsu Province [BK20150012]
  4. Fundamental Research Funds for the Central Universities
  5. Science Challenge Project [TZ2016001]
  6. Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund [U1501501]

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Transition metal nitrides have been suggested to have both high hardness and good thermal stability with large potential application value, but so far stable superhard transition metal nitrides have not been synthesized. Here, with our newly developed machine-learning accelerated crystal structure searching method, we designed a superhard tungsten nitride, h-WN6, which can be synthesized at pressure around 65 GPa and quenchable to ambient pressure. This h-WN6 is constructed with single-bonded armchair-like N-6 rings and presents ionic-like features, which can be formulated as W2.4+ N-6(2.4) . It has a band gap of 1.6 eV at 0 GPa and exhibits an abnormal gap broadening behavior under pressure. Excitingly, this h-WN6 is found to be the hardest among transition metal nitrides known so far (Vickers hardness around 57 GPa) and also has a very high melting temperature (around 1,900 K). Additionally, the good gravimetric (3.1 kJ/g) and volumetric (28.0 kJ/cm(3)) energy densities make this nitrogen-rich compound a potential high-energy-density material. These predictions support the designing rules and may stimulate future experiments to synthesize superhard and high-energy-density material. (C) 2018 Science China Press. Published by Elsevier B.V. and Science China Press. All rights reserved.

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