An Exchange-Correlation Functional for Fast and Accurate Modeling of Ferroelectric Perovskites

Abstract

We present a novel exchange correlation functional, C09x-PBEc, which combines C09 exchange with PBE correlation, to accurately model the ferroelectric properties of perovskites while retaining the computational efficiency of GGA functionals. With a growing interest in developing machine learning interatomic potentials (MLIPs) to model large-scale ferroelectric systems of technological relevance, it is important to scrutinise the density functional theory exchange-correlation functionals which are used to compute the forces, energies and stresses the MLIP is trained on. Using the example of the prototypical ferroelectrics lead titanate, PbTiO3, and barium titanate, BaTiO3 we show that many widely used functionals tend to overestimate their lattice constants and spontaneous polarization. Conversely, non-local van der Waals functionals with C09 exchange accurately capture these properties compared to experiment, but with a larger computational overhead than, for example, GGA. We show that C09x-PBEc combines the accuracy provided by the C09 exchange with the computational affordability of GGA, making it an excellent candidate to be used in the training of MLIPs for ferroelectric perovskites. We also demonstrate that an MLIP trained using C09x-PBEc accurately reproduces the ferroelectric-to-paraelectric phase transition temperature of PbTiO3 with respect to experiment, showing a marked improvement on MLIPs trained using GGA.