Clarifying the Ti-V Phase Diagram Using First-Principles Calculations and Bayesian Learning
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| Format: | Preprint |
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2025
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| _version_ | 1866912653382778880 |
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| author | Miryashkin, Timofei Klimanova, Olga Shapeev, Alexander |
| author_facet | Miryashkin, Timofei Klimanova, Olga Shapeev, Alexander |
| contents | Conflicting experiments disagree on whether the titanium-vanadium (Ti-V) binary alloy exhibits a body-centred cubic (BCC) miscibility gap or remains completely soluble. A leading hypothesis attributes the miscibility gap to oxygen contamination during alloy preparation. To resolve this disagreement, we use an ab initio + machine-learning workflow that couples an actively-trained Moment Tensor Potential with Bayesian inference of free energy surface. This workflow enables construction of the Ti-V phase diagram across the full composition range with systematically reduced statistical and finite-size errors. The resulting diagram reproduces all experimental features, demonstrating the robustness of our approach, and clearly favors the variant with a BCC miscibility gap terminating at T = 980 K and c = 0.67. Because our simulations model a perfectly oxygen-free Ti-V system, the observed gap cannot originate from impurity effects, in contrast to recent CALPHAD reassessments. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_17719 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Clarifying the Ti-V Phase Diagram Using First-Principles Calculations and Bayesian Learning Miryashkin, Timofei Klimanova, Olga Shapeev, Alexander Materials Science Artificial Intelligence Computational Physics Conflicting experiments disagree on whether the titanium-vanadium (Ti-V) binary alloy exhibits a body-centred cubic (BCC) miscibility gap or remains completely soluble. A leading hypothesis attributes the miscibility gap to oxygen contamination during alloy preparation. To resolve this disagreement, we use an ab initio + machine-learning workflow that couples an actively-trained Moment Tensor Potential with Bayesian inference of free energy surface. This workflow enables construction of the Ti-V phase diagram across the full composition range with systematically reduced statistical and finite-size errors. The resulting diagram reproduces all experimental features, demonstrating the robustness of our approach, and clearly favors the variant with a BCC miscibility gap terminating at T = 980 K and c = 0.67. Because our simulations model a perfectly oxygen-free Ti-V system, the observed gap cannot originate from impurity effects, in contrast to recent CALPHAD reassessments. |
| title | Clarifying the Ti-V Phase Diagram Using First-Principles Calculations and Bayesian Learning |
| topic | Materials Science Artificial Intelligence Computational Physics |
| url | https://arxiv.org/abs/2506.17719 |