Anomalous phase separation in a correlated electron system: Machine-learning-enabled large-scale kinetic Monte Carlo...

Area of Science:

• Condensed Matter Physics
• Materials Science
• Computational Physics

Background:

• Phase separation is vital for correlated electron materials, underpinning phenomena like colossal magnetoresistance and high-temperature superconductivity.
• Theoretical understanding of non-equilibrium phase separation dynamics is limited due to computational demands of multiscale modeling.

Purpose of the Study:

• To investigate the non-equilibrium phase separation dynamics in correlated electron systems using advanced computational methods.
• To develop a theoretical framework that accounts for the complexities of phase separation in these materials.

Main Methods:

• Employed machine learning techniques to enable large-scale dynamical simulations.
• Utilized a representative correlated electron system for the simulations.

Main Results:

• Observed an anomalous relaxation process that deviates from classical phase-ordering theories.
• Uncovered a novel correlation-induced freezing behavior during phase separation.

Conclusions:

• The findings suggest that correlation-induced freezing may be a general characteristic of phase separation in correlated electron systems.
• This study provides new theoretical insights into the complex dynamics of phase separation, potentially impacting the design of novel electronic materials.