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

TL;DR

This study uses advanced first-principles calculations combined with machine learning to accurately determine the Ti-V alloy phase diagram, resolving previous experimental disagreements and confirming the existence of a BCC miscibility gap.

Contribution

The paper introduces a novel ab initio and Bayesian inference workflow that accurately constructs the Ti-V phase diagram, reducing errors and clarifying the nature of the miscibility gap.

Findings

The phase diagram shows a BCC miscibility gap ending at 980 K and 0.67 composition.

The approach reproduces all experimental features of the Ti-V system.

The observed miscibility gap is intrinsic, not due to oxygen contamination.

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.