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

[Display omitted] 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 c...

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Veröffentlicht in:Science bulletin 2018-07, Vol.63 (13), p.817-824
Hauptverfasser: Xia, Kang, Gao, Hao, Liu, Cong, Yuan, Jianan, Sun, Jian, Wang, Hui-Tian, Xing, Dingyu
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container_end_page 824
container_issue 13
container_start_page 817
container_title Science bulletin
container_volume 63
creator Xia, Kang
Gao, Hao
Liu, Cong
Yuan, Jianan
Sun, Jian
Wang, Hui-Tian
Xing, Dingyu
description [Display omitted] 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 N6 rings and presents ionic-like features, which can be formulated as W2.4+N62.4−. 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/cm3) 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.
doi_str_mv 10.1016/j.scib.2018.05.027
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subjects Machine-learning accelerated crystal structure searching method
Superhard tungsten nitride
Transition metal nitrides
Tungsten nitride
title A novel superhard tungsten nitride predicted by machine-learning accelerated crystal structure search
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