Revealing interstitial energetics in Ti-23Nb-0.7Ta-2Zr gum metal base alloy via universal machine learning interatomic potentials

Abstract: Understanding the behavior of light interstitial elements in multicomponent alloys remains challenging due to the complexity of local chemical environments and the high computational cost of first-principles calculations. Here we demonstrate that three universal machine-learning interatomic potentials (uMLIPs) - MACE-MATPES-PBE-0, Orb-v3, and SevenNet-0 can efficiently map the energetics of C, N, O, and H interstitials in a Ti-23Nb-0.7Ta-2Zr gum metal base alloy while being several orders of magnitude faster than density functional theory (DFT). All uMLIPs predict broad energy distributions (1-3 eV) for all four interstitial elements, reflecting their strong sensitivity to local lattice chemistry. Despite alloy disorder, MACE-MATPES-PBE-0 and Orb-v3 reproduce the expected site preferences of the bcc structure: C, N, and O relax into octahedral sites, whereas H stabilizes in tetrahedral positions. In contrast, SevenNet-0 predicts H to be most stable in octahedral coordination, indicating a limitation of this model. Correlation analysis reveals two dominant chemical trends: Ti-rich environments strongly stabilize interstitials, whereas close proximity to Nb is destabilizing; Zr and Ta show no statistically significant influence, likely due to their low concentrations. Benchmarking representative O interstitial configurations against DFT confirms that the uMLIPs reasonably reproduce the energetic ordering of chemically distinct environments. Overall, these results demonstrate that uMLIPs enable computationally efficient, statistically converged characterization of defect energetics in gum metal base alloy and provide insight into how local chemical environments govern interstitial stability.