ORIGINAL RESEARCH article
Front. Mater.
Sec. Computational Materials Science
Volume 12 - 2025 | doi: 10.3389/fmats.2025.1591955
TiAlNb alloy interatomic potentials: comparing passive and active machine learning techniques with MTP and DeepMD
Provisionally accepted
- 1Institute of Material Systems Modeling, Helmholtz-Zentrum Hereon, Geesthacht, Germany
- 2Institute of Hydrogen Technoloy, Helmholtz-Zentrum Hereon, Geesthacht, Germany
- 3Institute for Continuum and Material Mechanics, Hamburg University of Technology, Hamburg, Germany
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Intermetallic 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 finitetemperature 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 α 2 -Ti 3 Al 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.
Keywords: Tialnb alloy, Machine-learning interatomic potentials, deep learning, Moment tensor, Active Learning, molecular dynamics, Density Functional Theory
Received: 11 Mar 2025; Accepted: 08 Jul 2025.
Copyright: © 2025 Chandran, Santhosh, Pistidda, Jerabek, Aydin and Cyron. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Anju Chandran, Institute of Material Systems Modeling, Helmholtz-Zentrum Hereon, Geesthacht, Germany
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