Arrested phase separation in double-exchange models: machine-learning
enabled large-scale simulation
- URL: http://arxiv.org/abs/2105.08221v1
- Date: Tue, 18 May 2021 01:30:23 GMT
- Title: Arrested phase separation in double-exchange models: machine-learning
enabled large-scale simulation
- Authors: Puhan Zhang, Gia-Wei Chern
- Abstract summary: We present large-scale dynamical simulations of electronic phase separation in the single-band double-exchange model.
We uncover an intriguing correlation-induced freezing behavior as doped holes are segregated from half-filled insulating background during equilibration.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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