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arXiv · 2609.14925

Machine Learning Guided CALPHAD Design of Ru-Stabilized BCC B2 Refractory Alloys

Nathan Peterson·Avik Mahata·Nick Beaver·Mohsen Kivy
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Abstract

Refractory alloys with a ductile body-centered-cubic (BCC) matrix strengthened by ordered B2 precipitates offer a high-temperature analogue to the gamma/gamma-prime architecture of Ni-based superalloys. Ruthenium is particularly attractive as a B2 stabilizer because RuHf, RuZr, and RuTi can retain ordered phases well above 1300 C. In this work, equilibrium CALPHAD calculations were coupled with random-forest-guided active learning to explore a ten-element Nb-based, Ru-bearing composition space containing Nb, Ta, Mo, V, Ru, Ti, Zr, Hf, Al, and Y at 1 at.% resolution. Across 500 CALPHAD-evaluated alloys, the calculations reproduced the principal trends reported for the Ru-B2 design space. RuHf and RuZr remained stable to the solidus, RuTi commonly exhibited a solutionizing window, and Al-containing alloys preferentially formed competing sigma and A15 phases. The upper bound of the BCC+B2 field increased from a median of approximately 1570 C at 5 at.% Ru to approximately 1980 C near 9-10 at.% Ru. Among the group-IV additions, Hf, Zr, and Ti produced progressively lower two-phase stability. Re-screening using physically motivated criteria identified 100 alloys satisfying requirements for high-temperature BCC+B2 stability, absence of liquid, phase purity, and appropriate secondary-phase fraction, including 19 Ru-lean compositions and two independently reported HfRu-B2 alloys. The results establish practical compositional design rules for Ru-stabilized dual-phase refractory alloys and identify phase-specific BCC/B2 lattice misfit as a key target for future design cycles.

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Nathan Peterson, Avik Mahata, Nick Beaver, Mohsen Kivy. 2026-09-14. Machine Learning Guided CALPHAD Design of Ru-Stabilized BCC B2 Refractory Alloys. https://arxiv.org/abs/2609.14925

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