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Perovskite oxides are known to exhibit many magnetic, electronic andstructural phases as function of doping and temperature. These materials aretheoretically frequently investigated by the DFT+U method, typically in theirground state structure at $T=0$. We show that by combining machine learningforce fields (MLFFs) and DFT+U based molecular dynamics, it becomes possible toinvestigate the crystal structure of complex oxides as function of temperatureand $U$. Here, we apply this method to the magnetic transition metal compoundsLaMnO$_3$ and SrRuO$_3$. We show that the structural phase transition fromorthorhombic to cubic in LaMnO$_3$, which is accompanied by the suppression ofa 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 cubiccrystal phase transitions can be described with great accuracy. We propose thatthe $U$ values that correctly capture the temperature-dependent structures ofthese complex oxides, can be identified by comparison of the MLFF simulated andexperimentally determined structures.

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