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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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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| author | Boburjon Mukhamedov Ferenc Tasnádi Igor A. Abrikosov |
| author_facet | Boburjon Mukhamedov Ferenc Tasnádi Igor A. Abrikosov |
| author_sort | Boburjon Mukhamedov |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-aa4eba890c8546e38ae6973f898f6c2e |
| institution | OA Journals |
| issn | 0264-1275 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Materials & Design |
| spelling | doaj-art-aa4eba890c8546e38ae6973f898f6c2e2025-08-20T02:00:50ZengElsevierMaterials & Design0264-12752025-05-0125311386510.1016/j.matdes.2025.113865Machine learning interatomic potential for the low-modulus Ti-Nb-Zr alloys in the vicinity of dynamical instabilityBoburjon Mukhamedov0Ferenc Tasnádi1Igor A. Abrikosov2Corresponding author.; Theoretical Physics Division, Department of Physics, Chemistry and Biology (IFM), Linköping University, SE-581 83 Linköping, SwedenTheoretical Physics Division, Department of Physics, Chemistry and Biology (IFM), Linköping University, SE-581 83 Linköping, SwedenTheoretical Physics Division, Department of Physics, Chemistry and Biology (IFM), Linköping University, SE-581 83 Linköping, SwedenMachine 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.http://www.sciencedirect.com/science/article/pii/S0264127525002850 |
| spellingShingle | Boburjon Mukhamedov Ferenc Tasnádi Igor A. Abrikosov Machine learning interatomic potential for the low-modulus Ti-Nb-Zr alloys in the vicinity of dynamical instability Materials & Design |
| title | Machine learning interatomic potential for the low-modulus Ti-Nb-Zr alloys in the vicinity of dynamical instability |
| title_full | Machine learning interatomic potential for the low-modulus Ti-Nb-Zr alloys in the vicinity of dynamical instability |
| title_fullStr | Machine learning interatomic potential for the low-modulus Ti-Nb-Zr alloys in the vicinity of dynamical instability |
| title_full_unstemmed | Machine learning interatomic potential for the low-modulus Ti-Nb-Zr alloys in the vicinity of dynamical instability |
| title_short | Machine learning interatomic potential for the low-modulus Ti-Nb-Zr alloys in the vicinity of dynamical instability |
| title_sort | machine learning interatomic potential for the low modulus ti nb zr alloys in the vicinity of dynamical instability |
| url | http://www.sciencedirect.com/science/article/pii/S0264127525002850 |
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