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

TL;DR

This paper introduces a machine-learning accelerated method to predict a new superhard tungsten nitride, h-WN6, with exceptional hardness and thermal stability, potentially guiding future synthesis of superhard materials.

Contribution

A novel machine-learning based crystal structure search method led to the prediction of a new superhard tungsten nitride, h-WN6, with unique structural and electronic properties.

Findings

h-WN6 can be synthesized at around 65 GPa and is quenchable to ambient conditions.

h-WN6 has a Vickers hardness of approximately 57 GPa, making it the hardest transition metal nitride known.

It exhibits a high melting temperature of around 1900 K and unique electronic features.

Abstract

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 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 1900 K). These predictions support the designing rules and may stimulate future experiments to synthesize superhard material.