Machine learning predicted a superhard and high-energy-density tungsten nitride

July 18, 2018, Science China Press
Crystal structrure and theoretical electronic structures of new W-N phases. Credit: ©Science China Press

Although machine learning has been successful in many aspects, its application in crystal structure predictions and materials design is still under development. Recently, Prof. Jian Sun's group at the Department of Physics, Nanjing University, implemented a machine-learning algorithm into the crystal structure search method. They used a machine learning algorithm to describe the potential energy surface and used it to filter the crystal structures, enhancing the search efficiency of crystal structure prediction.

Hybrid compounds of transition metals and , especially transition metal nitrides, have been widely studied for their high incompressibility and bulk modulus. However, superhard tungsten nitrides (Vickers hardness over 40 GPa) have not yet been found. The energy bands contributed by d valence electrons of tungsten atoms can easily cross the fermi energy level, and the metallicity leads to great reduction of their hardness. Therefore, designing non-metallic tungsten nitride crystal structures seems be a promising way to reach outstanding mechanical properties such as super-hardness.

Based on previous research, a collaboration led by Prof. Jian Sun and Prof. Hui-Tian Wang at Department of Physics, Nanjing University, summarized three traits for designing superhard hybrid compounds of transition metal and light elements: high-pressure stable and ambient-pressure metastable crystal structure, non-metallic electronic structures, and a large ratio of light elements. These characteristics inspired them to design nitrogen-rich tungsten nitrides containing special nitrogen-based basic configurations, such as rings, chains, networks and frameworks, etc. Based on these design rules and the newly developed machine-learning crystal structure search method, they have successfully predicted a non-metallic nitrogen-rich tungsten nitride h-WN6. It has a sandwich-like structure formed by nitrogen six-membered ring and tungsten atoms.

The electron localization function and Bader charge analysis indicate that h-WN6 is an ionic crystal containing strong N-N covalent bonds. It can be stable at high pressures and metastable at ambient pressure. Moreover, it has a small, indirect energy-gap and abnormal gap broadening behavior under compression. (see the crystal , electronic structures and the high pressure behaviors in the attached figure). More interestingly, h-WN6 is estimated to be the hardest among transition metal nitrides known so far, with a Vickers hardness around 57 GPa and also has a pretty high melting temperature of around 1,900 K. Moreover, their calculations also show that this nitrogen-rich compound can be considered as a potential high-energy-density material because of the good gravimetric (3.1 kJ/g) and volumetric (28.0 kJ/cm3) energy densities.

A collaborated research team from China implemented a machine-learning algorithm into the crystal structure search method and found a superhard tungsten nitride by using their new method. Their calculations show that this compound is the hardest transition metal nitride known so far, and it also has other extraordinary properties, such as high melting temperature and high-energy-density. Credit: ©Science China Press

The researchers developed a machine learning accelerated search method, summarized the design rules of superhard transition metal light elements compounds, and predicted a superhard and high-energy-density nitride with good thermal stability. The study will stimulate the theoretical design and experimental synthesis of this kind of material with potential application value. This will also enrich the family of superhard materials and may be used a reference for understanding the origin of hardness.

Explore further: Scientists predict new high-energy compounds

More information: Kang Xia et al, A novel superhard tungsten nitride predicted by machine-learning accelerated crystal structure search, Science Bulletin (2018). DOI: 10.1016/j.scib.2018.05.027

Related Stories

Scientists predict new high-energy compounds

February 14, 2017

Using theoretical methods, an international group of scientists led by Artem R. Oganov, Professor of Skoltech, Stony Brook University and Moscow Institute of Physics and Technology predicted unusual nitrides of hafnium and ...

A hydrogen sensor that works at room temperature

July 6, 2018

Researchers at TU Delft have developed a highly sensitive and versatile hydrogen sensor that works at room temperature. The sensor is made of a thin layer of a material called tungsten trioxide.

New extremely hard carbon nitride compound created

October 14, 2016

New work from a team led by Carnegie's Alexander Goncharov has created a new extremely incompressible carbon nitride compound. They say it could be the prototype for a whole new family of superhard materials, due to the unexpected ...

Superhard carbon material could crack diamond

December 7, 2011

( -- By applying extreme pressure to compress and flatten carbon nanotubes, scientists have discovered that they can create a new carbon polymer that simulations show is hard enough to crack diamond. The pressure-induced ...

Recommended for you

In colliding galaxies, a pipsqueak shines bright

February 20, 2019

In the nearby Whirlpool galaxy and its companion galaxy, M51b, two supermassive black holes heat up and devour surrounding material. These two monsters should be the most luminous X-ray sources in sight, but a new study using ...

Research reveals why the zebra got its stripes

February 20, 2019

Why do zebras have stripes? A study published in PLOS ONE today takes us another step closer to answering this puzzling question and to understanding how stripes actually work.

When does one of the central ideas in economics work?

February 20, 2019

The concept of equilibrium is one of the most central ideas in economics. It is one of the core assumptions in the vast majority of economic models, including models used by policymakers on issues ranging from monetary policy ...

Correlated nucleons may solve 35-year-old mystery

February 20, 2019

A careful re-analysis of data taken at the Department of Energy's Thomas Jefferson National Accelerator Facility has revealed a possible link between correlated protons and neutrons in the nucleus and a 35-year-old mystery. ...


Please sign in to add a comment. Registration is free, and takes less than a minute. Read more

Click here to reset your password.
Sign in to get notified via email when new comments are made.