Machine learning interatomic potential for the low-modulus Ti-Nb-Zr alloys in the vicinity of dynamical instability
Machine learning-augmented first-principles simulations facilitate the exploration of alloying and thermal treatments for tailoring material properties in industrial applications. However, addressing challenges near dynamical instabilities requires rigorous validation of machine-learned interatomic...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-05-01
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| Series: | Materials & Design |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0264127525002850 |
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| Summary: | Machine learning-augmented first-principles simulations facilitate the exploration of alloying and thermal treatments for tailoring material properties in industrial applications. However, addressing challenges near dynamical instabilities requires rigorous validation of machine-learned interatomic potentials (MLIP) to ensure their reliable applicability. In this study we have trained MLIP using moment tensor potentials to 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. |
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| ISSN: | 0264-1275 |