Arrested Phase Separation in Double-Exchange Models: Large-Scale Simulation Enabled by Machine Learning

We present large-scale dynamical simulations of electronic phase separation in the single-band double-exchange model based on deep-learning neural-network potentials trained from small-size exact diagonalization solutions. We uncover an intriguing correlation-induced freezing behavior as doped holes are segregated from half filled insulating background during equilibration. While the aggregation of holes is stabilized by the formation of ferromagnetic clusters through Hund's coupling between charge carriers and local magnetic moments, this stabilization also creates confining potentials for holes when antiferromagnetic spin-spin correlation is well developed in the background. The dramatically reduced mobility of the self-trapped holes prematurely disrupts further growth of the ferromagnetic clusters, leading to an arrested phase separation. Implications of our findings for phase separation dynamics in materials that exhibit colossal magnetoresistance effect are discussed.

Automated summary

In this study, large-scale dynamical simulations of electronic phase separation in the single-band double-exchange model were conducted based on deep-learning neural-network potentials trained from small-size exact diagonalization solutions. An intriguing correlation-induced freezing behavior was uncovered during equilibration, leading to premature trapping of holes and disruption of further growth of ferromagnetic clusters. The implications of these findings for phase separation dynamics in materials exhibiting colossal magnetoresistance effect are discussed.