Machine learning interatomic potential for the low-modulus Ti-Nb-Zr alloys in the vicinity of dynamical instability

Abstract

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.

21 pages, 9 figures

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Cited by in corpus (1)

• Machine learning interatomic potential for the low-modulus Ti-Nb-Zr alloys in the vicinity of dynamical instability