Universität Stuttgart

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Subject: search.filters.subject.660Institute: search.filters.UBSInstitut.Institut für MaterialwissenschaftInstitute: search.filters.UBSInstitut.Fakultätsübergreifend / Sonstige EinrichtungStart date: 2020Author: search.filters.author.Grabowski, Blazej

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Now showing 1 - 5 of 5
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    Accelerating ab initio melting property calculations with machine learning : application to the high entropy alloy TaVCrW
    (2024) Zhu, Li-Fang; Körmann, Fritz; Chen, Qing; Selleby, Malin; Neugebauer, Jörg; Grabowski, Blazej
    Melting properties are critical for designing novel materials, especially for discovering high-performance, high-melting refractory materials. Experimental measurements of these properties are extremely challenging due to their high melting temperatures. Complementary theoretical predictions are, therefore, indispensable. One of the most accurate approaches for this purpose is the ab initio free-energy approach based on density functional theory (DFT). However, it generally involves expensive thermodynamic integration using ab initio molecular dynamic simulations. The high computational cost makes high-throughput calculations infeasible. Here, we propose a highly efficient DFT-based method aided by a specially designed machine learning potential. As the machine learning potential can closely reproduce the ab initio phase-space distribution, even for multi-component alloys, the costly thermodynamic integration can be fully substituted with more efficient free energy perturbation calculations. The method achieves overall savings of computational resources by 80% compared to current alternatives. We apply the method to the high-entropy alloy TaVCrW and calculate its melting properties, including the melting temperature, entropy and enthalpy of fusion, and volume change at the melting point. Additionally, the heat capacities of solid and liquid TaVCrW are calculated. The results agree reasonably with the CALPHAD extrapolated values.
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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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    Electronic moment tensor potentials include both electronic and vibrational degrees of freedom
    (2024) Srinivasan, Prashanth; Demuriya, David; Grabowski, Blazej; Shapeev, Alexander
    We present the electronic moment tensor potentials (eMTPs), a class of machine-learning interatomic models and a generalization of the classical MTPs, reproducing both the electronic and vibrational degrees of freedom, up to the accuracy of ab initio calculations. Following the original polynomial interpolation idea of the MTPs, the eMTPs are defined as polynomials of vibrational and electronic degrees of freedom, corrected to have a finite interatomic cutoff. Practically, an eMTP is constructed from the classical MTPs fitted to a training set, whose energies and forces are calculated with electronic temperatures corresponding to the Chebyshev nodes on a given temperature interval. The eMTP energy is hence a Chebyshev interpolation of the classical MTPs. Using the eMTP, one can obtain the temperature-dependent vibrational free energy including anharmonicity coming from phonon interactions, the electronic free energy coming from electron interactions, and the coupling of atomic vibrations and electronic excitations. Each of the contributions can be accessed individually using the proposed formalism. The performance of eMTPs is demonstrated for two refractory systems which have a significant electronic, vibrational and coupling contribution up to the melting point-unary Nb, and a disordered TaVCrW high-entropy alloy. Highly accurate thermodynamic and kinetic quantities can now be obtained just by using eMTPs, without any further ab initio calculations. The proposed construction to include the electronic degree of freedom can also be applied to other machine-learning models.
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    Synthesis of high-entropy hydride from the cantor alloy (fcc–CoCrFeNiMn) at extreme conditions
    (2026) Glazyrin, Konstantin; Spektor, Kristina; Bykov, Maxim; Carvalho, Paulo H. B.; Dong, Weiwei; Körmann, Fritz; Sano-Furukawa, Asami; Hattori, Takanori; Beyer, Doreen C.; Sahlberg, Martin; Ikeda, Yuji; Yu, Ji Hun; Sangsun, Yang; Lee, Jai-Sung; Bhat, Shrikant; Hanfland, Michael; Grabowski, Blazej; Divinski, Sergiy; Yusenko, Kirill V.
    Studies of high-entropy materials contribute to various fields of science and reveal ever more exciting properties of applied interest. Here, we perform a study of the resistance of a Cantor alloy (CoCrFeNiMn) to hydrogen through high-pressure experiments at elevated temperatures by X-ray and neutron time-of-flight experiments and ab initio calculations. We report formation of an fcc hydride based on the Cantor alloy composition. We also provide its characterization, including an estimate of hydrogen content. These findings contribute to the growing body of knowledge on the complex chemistry of high-entropy alloys and high-entropy hydrides.
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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.