Clarifying the Ti-V Phase Diagram Using First-Principles Calculations and Bayesian Learning

2025/06/21 by Timofei Miryashkin, Olga Klimanova, Miryashkin, Timofei +3
Materials Science · Physics and Astronomy · #Artificial Intelligence (cs.AI) #Computational Physics (physics.comp-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Model Reduction and Neural Networks #Titanium Alloys Microstructure and Properties

paper · pdf · doi:10.48550/arxiv.2506.17719

openalex publication_date 2025/06/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

Abstract

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.

Citations

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