Manganite*

Perovskite multi-ferroic manganite materials possess a unique Mexican hat-shaped potential energy surface, which makes them an ideal candidate for investigation of topological-defect formation under rapid quenching. Quantum-mechanical methods based on density functional theory (DFT) can accurately describe atomic interactions but fail, due to limitations in system size, in simulating large-scale topological defect formation. Recent developments in machine learning (ML) have made it possible to investigate the kinetics of complex phase transitions. In this work, we train an equivariant Allegro-Legato neural network quantum molecular dynamics (NNQMD) model using DFT training data for YMnO₃, and thereby investigate dependence of topological defect formation on the quenching rate. We have also performed diffuse neutron scattering experiments on the quenched YMnO₃ to quantify topological defects for validating our NNQMD simulations.