3 years ago

Predicting displacements of octahedral cations in ferroelectric perovskites using machine learning

Predicting displacements of octahedral cations in ferroelectric perovskites using machine learning
Turab Lookman, Toby Shearman, James Theiler, Prasanna V. Balachandran
In ferroelectric perovskites, displacements of cations from the high-symmetry lattice positions in the paraelectric phase break the spatial inversion symmetry. Furthermore, the relative magnitude of ionic displacements correlate strongly with ferroelectric properties such as the Curie temperature. As a result, there is interest in predicting the relative displacements of cations prior to experiments. Here, machine learning is used to predict the average displacement of octahedral cations from its high-symmetry position in ferroelectric perovskites. Published octahedral cation displacements data from density functional theory (DFT) calculations are used to train machine learning models, where each cation is represented by features such as Pauling electronegativity, Martynov–Batsanov electronegativity and the ratio of valence electron number to nominal charge. Average displacements for ten new octahedral cations for which DFT data do not exist are predicted. Predictions are validated by comparing them with new DFT calculations and existing experimental data. The outcome of this work has implications in the design and discovery of novel ferroelectric perovskites.A machine learning approach is developed to rapidly predict the displacements of previously unexplored octahedral cations in PbTiO3-based ferroelectric perovskites. Thus, expanding the knowledge base on ionic displacement data, which are important for the rational design of novel ferroelectric perovskites.

Publisher URL: http://onlinelibrary.wiley.com/resolve/doi

DOI: 10.1107/S2052520617011945

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