Efficient prediction of elastic properties of Ti0.5Al0.5N at elevated temperature using machine learning interatomic potential
Author:
Publisher
Elsevier BV
Subject
Materials Chemistry,Metals and Alloys,Surfaces, Coatings and Films,Surfaces and Interfaces,Electronic, Optical and Magnetic Materials
Reference34 articles.
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4. Thermal stability of arc evaporated high aluminum-content Ti(1-x)AlxN thin films;Hörling;Journal of Vacuum Science & Technology A,2002
5. Self-organized nanostructures in the ti-al-n system;Mayrhofer;Appl Phys Lett,2003
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1. Machine-learning potentials for nanoscale simulations of tensile deformation and fracture in ceramics;npj Computational Materials;2024-04-02
2. High temperature decomposition and age hardening of single-phase wurtzite Ti1−xAlxN thin films grown by cathodic arc deposition;Physical Review Materials;2024-01-09
3. Active learning with moment tensor potentials to predict material properties: Ti0.5Al0.5N at elevated temperature;Journal of Vacuum Science & Technology A;2024-01-01
4. Atomic-scale simulations in multi-component alloys and compounds: A review on advances in interatomic potential;Journal of Materials Science & Technology;2023-12
5. HADB: A materials-property database for hard-coating alloys;Thin Solid Films;2023-02
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