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Subject: search.filters.subject.660Institute: search.filters.UBSInstitut.Institut für MaterialwissenschaftStart date: 2025End date: 2025Author: search.filters.author.Grabowski, Blazej

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    Ab initio machine-learning unveils strong anharmonicity in non-Arrhenius self-diffusion of tungsten
    (2025) Zhang, Xi; Divinski, Sergiy V.; Grabowski, Blazej
    The knowledge of diffusion mechanisms in materials is crucial for predicting their high-temperature performance and stability, yet accurately capturing the underlying physics like thermal effects remains challenging. In particular, the origin of the experimentally observed non-Arrhenius diffusion behavior has remained elusive, largely due to the lack of effective computational tools. Here 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 the 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 temperature-dependent self-diffusivity and, in particular, its curvature 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.
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    Free-energy perturbation in the exchange-correlation space accelerated by machine learning : application to silica polymorphs
    (2025) Forslund, Axel; Jung, Jong Hyun; Ikeda, Yuji; Grabowski, Blazej
    We propose a free-energy-perturbation approach accelerated by machine-learning potentials to efficiently compute transition temperatures and entropies for all rungs of Jacob’s ladder. We apply the approach to the dynamically stabilized phases of SiO2, which are characterized by challengingly small transition entropies. All investigated functionals from rungs 1-4 fail to predict an accurate transition temperature by 25-200%. Only by ascending to the fifth rung, within the random phase approximation, an accurate prediction is possible, giving a relative error of 5%. We provide a clear-cut procedure and relevant data to the community for, e.g., developing and evaluating new functionals.