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 quantum
paraelectric 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.