Hard antiphase domain boundaries in strontium titanate unravelled using machine-learned force fields

0562213 - FZÚ 2023 RIV US eng J - Journal Article
Troester, A. - Verdi, C. - Dellago, C. - Rychetský, Ivan - Kresse, G. - Schranz, W.
Hard antiphase domain boundaries in strontium titanate unravelled using machine-learned force fields.
Physical Review Materials. Roč. 6, č. 9 (2022), č. článku 094408. ISSN 2475-9953. E-ISSN 2475-9953
Institutional support: RVO:68378271
Keywords : domain walls * ferroelectrics * functional materials * perovskites * machine learning
OECD category: Condensed matter physics (including formerly solid state physics, supercond.)
Impact factor: 3.4, year: 2022
Method of publishing: Limited access
https://doi.org/10.1103/PhysRevMaterials.6.094408

We investigate the properties of hard antiphase boundaries in SrTiO3 using machine-learned force fields. In contrast to earlier findings based on standard ab initio methods, for all pressures up to 120kbar the observed domain wall pattern maintains an almost perfect Néel character in quantitative agreement with Landau-Ginzburg-Devonshire theory, and the in-plane polarization P3 shows no tendency to decay to zero. Together with the switching properties of P3 under reversal of the Néel order parameter component, this provides hard evidence for the presence of rotopolar couplings. The present approach overcomes the severe limitations of ab initio simulations of wide domain walls and opens avenues toward concise atomistic predictions of domain-wall properties even at finite temperatures.
Permanent Link: https://hdl.handle.net/11104/0334995