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

ABSTRACT: SignificancePhase separation is crucial to the functionalities of many correlated electron materials with notable examples including colossal magnetoresistance in manganites and high-T_c superconductivity in cuprates. However, the nonequilibrium phase-separation dynamics in such systems are poorly understood theoretically, partly because the required multiscale modeling is computationally very demanding. With the aid of machine-learning methods, we have achieved large-scale dynamical simulations in a representative correlated electron system. We observe an unusual relaxation process that is beyond the framework of classical phase-ordering theories. We also uncover a correlation-induced freezing behavior, which could be a generic feature of phase separation in correlated electron systems.

SUBMITTER: Zhang S

PROVIDER: S-EPMC9170136 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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

Zhang Sheng S Zhang Puhan P Chern Gia-Wei GW

Proceedings of the National Academy of Sciences of the United States of America 20220429 18

SignificancePhase separation is crucial to the functionalities of many correlated electron materials with notable examples including colossal magnetoresistance in manganites and high-Tc superconductivity in cuprates. However, the nonequilibrium phase-separation dynamics in such systems are poorly understood theoretically, partly because the required multiscale modeling is computationally very demanding. With the aid of machine-learning methods, we have achieved large-scale dyna ...[more]

PMID: 35486688

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