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    Machine Learning-Enabled Ab Initio Study of the Isotope Effect in SrTi18O3

    Jonathan Schmidt and Nicola A. Spaldin

    • Department of Materials, ETH Zürich, Zürich, CH-8093, Switzerland

    Phys. Rev. Lett. 136, 106404 – Published 12 March, 2026

    DOI: https://doi.org/10.1103/4293-1g2r

    Abstract

    We use the self-consistent harmonic approximation with machine learning interatomic potentials to calculate the effect of O18 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 O16 by O18 induces the ferroelectric state, and we demonstrate that the ferroelectric phase transition in STO18 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 STO16 and STO18 and find that the phase space in which STO16 shows quantum paraelectric behavior, while STO18 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.

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