Chandran, AnjuAnjuChandranSanthosh, ArchaArchaSanthoshPistidda, ClaudioClaudioPistiddaJerabek, PaulPaulJerabekAydin, RolandRolandAydinCyron, Christian J.Christian J.Cyron2025-08-182025-08-182025-07-30Frontiers in materials 12: 1591955 (2025)https://hdl.handle.net/11420/56974Intermetallic titanium aluminides are interesting for aerospace and automotive applications due to their superior high-temperature mechanical properties. In particular, γ-TiAl-based alloys containing 5–10 at.% Niobium (Nb) have attracted significant attention. Molecular dynamics (MD) simulations can elucidate and optimize these materials, provided that accurate interatomic potentials are available. In this work, we compare active and passive machine learning approaches for developing TiAlNb interatomic potentials using both deep potential molecular dynamics (DeePMD) and the moment tensor potential (MTP) methods. Our comprehensive evaluation encompasses elastic constants, equilibrium volume, lattice parameters, and finite-temperature behavior, as well as simulated tension tests and generalized stacking fault energy calculations to assess the impact of Nb on the thermo-mechanical properties of γ-TiAl and α2-Ti3Al phases. Active learning consistently outperformed passive learning for both methods while requiring only a fraction of the training samples. Notably, active learning with DeePMD yielded a single potential capable of predicting the properties of both phases, whereas MTP exhibited limitations that necessitated separate training for each phase. Although active learning potentials excelled in predicting high-temperature behavior, their room-temperature property predictions were less accurate due to a sample selection bias toward higher temperatures. Overall, our thermomechanical analysis demonstrates that Nb incorporation enhances ductility while simultaneously reducing strength.en2296-80162025Frontiers Media S.A.https://creativecommons.org/licenses/by/4.0/TiAlNb alloymachine-learning interatomic potentialsdeep learningmoment tensoractive learningmolecular dynamicsdensity functional theoryTechnology::620: Engineering::620.1: Engineering Mechanics and Materials Science::620.11: Engineering MaterialsComputer Science, Information and General Works::006: Special computer methods::006.3: Artificial IntelligenceTechnology::660: Chemistry; Chemical EngineeringJournal Article2025-08-13https://doi.org/10.15480/882.1577210.3389/fmats.2025.159195510.15480/882.15772Journal Article