Berlin 2018 – wissenschaftliches Programm

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MM: Fachverband Metall- und Materialphysik

MM 40: Topical Session (Symposium MM): Big Data in Materials Science - Managing and exploiting the raw material of the 21st century

MM 40.2: Vortrag

Mittwoch, 14. März 2018, 15:45–16:00, TC 006

First-Principles Thermodynamics of ZrO₂ at a Hybrid Level Using a Machine-Learned Potential — •Emre Ahmetcik, Angelo Ziletti, Matthias Scheffler, Christian Carbogno, and Luca M. Ghiringhelli — Fritz-Haber-Institut der Max-Planck-Gesellschaft, 14195 Berlin-Dahlem, Germany

Due to their outstanding electronic and thermal properties, zirconia-based materials are used in a wide range of industrial applications, e.g., as catalyst support, as ionic conductor, and as thermal barrier coating [1]. Computational studies of its thermodynamic properties have hitherto relied on LDA/GGA-type functionals. However, it is well known that the exchange-correlation functional significantly affects the outcome of the calculations for this material [2]. We overcome this limitation by building a machine-learned Gaussian Approximation Potential [3] from a small number of first-principles calculations performed with a hybrid exchange-correlation functional. This allows us to simulate the dynamics of zirconia in supercells containing several hundreds of atoms and for several nanoseconds. By this means, we are able to obtain the phase diagram of ZrO₂ and to understand the mechanism that drive the monoclinic-tetragonal phase-transition.

[1] A. Evans, D. Clarke, and C. Levi, J. Eur. Ceram. Soc. 28, 1405 (2008)

[2] C. Carbogno et al., Phys. Rev. B 90, 1441 (2014)

[3] A. P. Bartok et al., Phys. Rev. Lett. 104, 136403 (2010)