Kinetics of orbital ordering in cooperative Jahn-Teller models: Machine-learning enabled large-scale simulations

• DOI: 10.48550/arxiv.2405.14776
• Publication Date: 2024-05-23
• Authors: Supriyo Ghosh; Sheng Zhang; Cheng Chen; Gia-Wei Chern
• Keywords: Jahn–Teller effect

We present a scalable machine learning (ML) force-field model for the adiabatic dynamics of cooperative Jahn-Teller (JT) systems. Large scale dynamical simulations JT also shed light on orbital ordering in colossal magnetoresistance manganites. The effect these materials describes distortion local oxygen octahedra driven by coupling to degrees freedom $e_g$ electrons. An effective electron-mediated interaction between modes leads structural transition and emergence long-range order at low temperatures. Assuming principle locality, deep-learning neural-network is developed accurately efficiently predict electron-induced forces that drive evolution phonons. A group-theoretical method utilized develop descriptor incorporates combined lattice symmetry into ML model. Large-scale Langevin simulations, enabled models, are performed investigate coarsening composite after thermal quench. late-stage domains exhibits pronounced freezing behaviors which likely related unusual morphology domain structures. Our work highlights promising avenue multi-scale modeling correlated electron