What is it about?

Titanium nitride (TiN) is a solid with a rock salt like structure. TiN based thin films are often coated on steel cutting tools due to their high hardness and excellent heat and chemical stability. However, the effectiveness of cutting tools starts to decrease under high temperature and stress conditions due to peeling off of TiN coatings. The reason behind this peeling behavior is not well known. To get to the bottom of such coating failures, the authors designed a new deep neural network (DNN) potential, which is a computer tool that can predict how atoms in TiN systems behave under certain conditions. They successfully used the DNN potential to understand the fracture failure behavior of TiN and its mechanisms. This study finds that TiN starts to deform and develop brittle fractures when it is stretched beyond its tensile yield point.

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Why is it important?

The knowledge of how TiN based thin films react to high stress and temperatures can be used to make new coatings that are better at handling extreme conditions. Most experimental methods fail to understand the complex atomic interactions in TiN coatings that affect its toughness and strength. The DNN potential overcomes this problem by recreating real life atomic behavior of TiN systems on a computer. This allows it to predict TiN properties under various conditions with great accuracy. KEY TAKEAWAY: The DNN potential effectively reveals the fracture behavior in TiN coatings. It also opens a new way of looking into the structures and properties of coating materials on an atomic scale. Keywords/meta tags:

Read the Original

This page is a summary of: Microstructure evolution under thermo-mechanical operating of rocksalt-structure TiN via neural network potential, The Journal of Chemical Physics, November 2023, American Institute of Physics,
DOI: 10.1063/5.0171528.
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