Impact of lattice relaxations on phase transitions in a high-entropy alloy studied by machine-learning potentials
By Tatiana Kostiuchenko, Fritz Körmann, Jörg Neugebauer, Alexander V. Shapeev. npj Computational Materials, 2018
188 citations recorded by the source corpus.
Recently, high-entropy alloys (HEAs) have attracted wide attention due to their extraordinary materials properties. A main challenge in identifying new HEAs is the lack of efficient approaches for exploring their huge compositional space. Ab initio calculations have emerged as a powerful approach that complements experiment. However, for multicomponent alloys existing approaches suffer from the chemical complexity involved. In this work we propose a method for studying HEAs computationally. Our approach is based on the application of machine-learning potentials based on ab initio data in combination with Monte Carlo simulations. The high efficiency and performance of the approach are demonstrated on the prototype bcc NbMoTaW HEA. The approach is employed to study phase stability, phase transitions, and chemical short-range order. The importance of including local relaxation effects is revealed: they significantly stabilize single-phase formation of bcc NbMoTaW down to room temperature. Finally, a so-far unknown mechanism that drives chemical order due to atomic relaxation at ambient temperatures is discovered.
Subfields: Biomedical Engineering, Mechanical Engineering, Mechanics of Materials
View Paper: https://www.semanticscholar.org/paper/f558c613651271f851892802039e3158a4788809
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Citations in the corpus
- Machine-learning informed prediction of high-entropy solid solution formation: Beyond the Hume-Rothery rules (2020, unknown): “For convenience, an equivalent new parameter γ is defined to replace ΔGN: γ :1⁄4 ΔGN=min ΔG2 ð Þif minðΔG2Þ< 0; ΔGN=min ΔG2 ð Þ if ΔGN < 0 andminðΔG2Þ> 0 (10)”
- Machine learning: Accelerating materials development for energy storage and conversion (2020, methodology): “Other alloys, such as NbMoTaW, were investigated by ML potentials in combination with Monte Carlo simulations.(123) For oxides, Jacobsen et al(124) investigated the surface reconstruction of SnO2(110)-(4 × 1) based on ML potentials.”
- Machine Learning for High-entropy Alloys: Progress, Challenges and Opportunities (2022, background): “Therefore, it is expected that in the range between 1000 K and 2000 K, MoNbTaW is primarily a solid solution, while MoNbTaTiW should contain a large fraction of second-phase precipitates.”