Machine learning predicted a superhard and high-energy-density tungsten nitride
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 light elements, 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.
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