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

We use the self-consistent harmonic approximation (SSCHA) with machine learning interatomic potentials to calculate the effect of 18O substitution on the properties of quantum paraelectric SrTiO3 (STO). We find that calculations including both quantum and anharmonic effects are able to reproduce the experimentally observed isotope effect, in which the replacement of 16O by 18O induces the ferroelectric state, and we demonstrate that the ferroelectric phase transition in ST18O 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 ST16O and ST18O and find that the phase space in which ST16O shows quantumparaelectric behavior, while ST18O 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.

This dataset contains the inputs/and outputs from the SSCHA calculations.