Machine learning interatomic potential for the low-modulus Ti-Nb-Zr alloys in the vicinity of dynamical instability
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| Format: | Preprint |
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2024
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| _version_ | 1866910898731352064 |
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| author | Mukhamedov, Boburjon Tasnadi, Ferenc Abrikosov, Igor A. |
| author_facet | Mukhamedov, Boburjon Tasnadi, Ferenc Abrikosov, Igor A. |
| contents | Traditionally, alloying and thermal treatment are considered as the main tools for design of new materials. Application of first-principles simulations can significantly accelerate the process of materials design, however, to account for both, multicomponent chemical disorder and finite temperature effects in theoretical simulations is a challenging task. In this work we have trained machine learning interatomic potential to effectively simulate finite temperature elastic properties of multicomponent β-Ti94-xNbxZr6 alloys. Our simulations predict the presence of the elinvar effect for the wide range of temperatures. Importantly, we predict that in a vicinity of dynamical and mechanical instability, the β-Ti94-xNbxZr6 alloys demonstrate strongly non-linear concentration-dependence of elastic moduli, which leads to low values of moduli comparable to that of human bone. Moreover, these alloys demonstrate a strong anisotropy of directional Young's modulus which can be helpful for microstructure tailoring and design of materials with desired elastic properties. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_06270 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine learning interatomic potential for the low-modulus Ti-Nb-Zr alloys in the vicinity of dynamical instability Mukhamedov, Boburjon Tasnadi, Ferenc Abrikosov, Igor A. Materials Science Computational Physics Traditionally, alloying and thermal treatment are considered as the main tools for design of new materials. Application of first-principles simulations can significantly accelerate the process of materials design, however, to account for both, multicomponent chemical disorder and finite temperature effects in theoretical simulations is a challenging task. In this work we have trained machine learning interatomic potential to effectively simulate finite temperature elastic properties of multicomponent β-Ti94-xNbxZr6 alloys. Our simulations predict the presence of the elinvar effect for the wide range of temperatures. Importantly, we predict that in a vicinity of dynamical and mechanical instability, the β-Ti94-xNbxZr6 alloys demonstrate strongly non-linear concentration-dependence of elastic moduli, which leads to low values of moduli comparable to that of human bone. Moreover, these alloys demonstrate a strong anisotropy of directional Young's modulus which can be helpful for microstructure tailoring and design of materials with desired elastic properties. |
| title | Machine learning interatomic potential for the low-modulus Ti-Nb-Zr alloys in the vicinity of dynamical instability |
| topic | Materials Science Computational Physics |
| url | https://arxiv.org/abs/2412.06270 |