mlsanityData to 14 Oct 2026

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

arXiv 2506.17719 ↗First posted 21 Jun 2025cond-mat.mtrl-sci cs.AI physics.comp-phcited by 0

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Abstract (arXiv)

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.

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References · 6

  1. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 708
  2. 2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 474
  3. 2022Learning Local Equivariant Representations for Large-Scale Atomistic Dynamicscited by 256
  4. 2017DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamicscited by 79
  5. 2023Accurate melting point prediction through autonomous physics-informed learningcited by 2
  6. 2023Bayesian inference of composition-dependent phase diagramscited by 1

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