Machine-learning-enabled ab initio study of quantum phase transitions in SrTiO3

We find that calculations including both quantum and anharmonic effects are able to reproduce the experimentally observed isotope effect, in which the replacement of 16 O by 18 O induces the ferroelectric state, and we demonstrate that the ferroelectric phase transition in ST 18 O can be reproduced in a purely displacive description.

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 behavior, while ST 18 O becomes ferroelectric, is narrow. Our Letter shows that machine learning interatomic potentials enable temperature-dependent simulations that include quantum and anharmonic phonon effects and that quantitative prediction of the phase diagram in the case of STO is limited by the accuracy of the underlying electronic structure method.