Berlin 2024 – wissenschaftliches Programm
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MM: Fachverband Metall- und Materialphysik
MM 32: Developement of Calculation Methods I
MM 32.3: Vortrag
Mittwoch, 20. März 2024, 10:45–11:00, C 243
Full anharmonicity of transition states via ab initio machine-learning: Self-diffusion in tungsten — •Blazej Grabowski1, Xi Zhang1, and Sergiy Divinski2 — 1Institute for Materials Science, University of Stuttgart, D-70569 Stuttgart, Germany — 2Institute of Materials Physics, University of Münster, 48149 Münster, Germany
We propose an efficient ab initio framework to compute the Gibbs energy of the transition state in vacancy-mediated diffusion including the relevant thermal excitations at density-functional-theory level. With the aid of a bespoke machine-learning interatomic potential, the temperature-dependent vacancy formation and migration Gibbs energies of the prototype system body-centered cubic (BCC) tungsten are shown to be strongly affected by anharmonicity. This finding explains the physical origin of the experimentally observed non-Arrhenius behavior of tungsten self-diffusion. A remarkable agreement between the calculated and experimental curvature of the temperature-dependent self-diffusivity is revealed. The proposed computational framework is robust and broadly applicable, as evidenced by first tests for a hexagonal close-packed (HCP) multicomponent high-entropy alloy. The successful applications underscore the attainability of an accurate ab initio diffusion database.
Zhang, Divinski, and Grabowski, arXiv:2311.00633 (2023).
Keywords: ab initio simulations; machine learning potentials; transition state; Gibbs energy; anharmonicity