Phase transitions of LaMnO$_3$ and SrRuO$_3$ from DFT + U based machine learning force fields simulations

Abstract: Perovskite oxides are known to exhibit many magnetic, electronic and structural phases as function of doping and temperature. These materials are theoretically frequently investigated by the DFT+U method, typically in their ground state structure at $T=0$. We show that by combining machine learning force fields (MLFFs) and DFT+U based molecular dynamics, it becomes possible to investigate the crystal structure of complex oxides as function of temperature and $U$. Here, we apply this method to the magnetic transition metal compounds LaMnO$_3$ and SrRuO$_3$. We show that the structural phase transition from orthorhombic to cubic in LaMnO$_3$, which is accompanied by the suppression of a Jahn-Teller distortion, can be simulated with an appropriate choice of $U$. For SrRuO$_3$, we show that the sequence of orthorhombic to tetragonal to cubic crystal phase transitions can be described with great accuracy. We propose that the $U$ values that correctly capture the temperature-dependent structures of these complex oxides, can be identified by comparison of the MLFF simulated and experimentally determined structures.