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A novel superhard tungsten nitride predicted by machine-learning accelerated crystal structure search
Kang Xia1, Hao Gao1, Cong Liu1
1National Laboratory of Solid State Microstructures, School of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China.
Science Bulletin
|January 20, 2023
Summary
Researchers designed a superhard tungsten nitride (h-WN6) using machine learning. This material exhibits exceptional hardness and thermal stability, showing potential for high-energy-density applications.
Area of Science:
- Materials Science
- Solid State Chemistry
- Computational Materials Design
Background:
- Transition metal nitrides are known for hardness and thermal stability.
- Synthesizing stable superhard transition metal nitrides remains a challenge.
Purpose of the Study:
- To design a novel superhard transition metal nitride using advanced computational methods.
- To explore the properties and potential applications of the designed material.
Main Methods:
- Employed a machine-learning accelerated crystal structure searching method.
- Investigated the structural, electronic, and mechanical properties of tungsten nitride (h-WN6) under pressure.
Main Results:
- Designed a superhard tungsten nitride, h-WN6, synthesizable at 65 GPa and stable at ambient pressure.
- h-WN6 exhibits a Vickers hardness of ~57 GPa, a melting point of ~1900 K, and a band gap of 1.6 eV.
- The material shows high gravimetric and volumetric energy densities, suggesting potential as an energetic material.
Conclusions:
- h-WN6 represents the hardest known transition metal nitride.
- The findings provide design principles for future superhard and high-energy-density materials.
- The study highlights the efficacy of machine learning in accelerating materials discovery.
