Machine-Learning-enabled ab initio study of quantum phase transitions in SrTiO${}_3$

Abstract

We use the self-consistent harmonic approximation (SSCHA) with machine learning interatomic potentials to calculate the effect of ${}^{18}$O substitution on the properties of quantum paraelectric SrTiO${}_3$ (STO). We find that calculations including both quantum and anharmonic effects are able to reproduce the experimentally observed isotope effect, in which replacement of ${}^{16}$O by ${}^{18}$O induces the ferroelectric state, and demonstrate that the ferroelectric phase transition in ST${}^{18}$O can be reproduced in a purely displacive manner. We calculate the ferroelectric soft mode frequency as a function of volume, lattice parameters and temperature for ST${}^{16}$O and ST${}^{18}$O, and find that the phase space in which ST${}^{16}$O shows quantum paraelectric behaviour, while ST${}^{18}$O becomes ferroelectric is narrow. Our study shows that machine learning interatomic potentials enable temperature-dependent simulations that include quantum and anharmonic phonon effects, however quantitative prediction of phase diagrams remains challenging due to a lack of universally accurate electronic structure methods.