Machine-Learning-enabled ab initio study of quantum phase transitions in SrTiO$_3$
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918125134413824 |
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| author | Schmidt, Jonathan Spaldin, Nicola A. |
| author_facet | Schmidt, Jonathan Spaldin, Nicola A. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_10735 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine-Learning-enabled ab initio study of quantum phase transitions in SrTiO$_3$ Schmidt, Jonathan Spaldin, Nicola A. Materials Science 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. |
| title | Machine-Learning-enabled ab initio study of quantum phase transitions in SrTiO$_3$ |
| topic | Materials Science |
| url | https://arxiv.org/abs/2508.10735 |