Corpus ID: 236493391

Data-Driven Dynamical Mean-Field Theory: an error-correction approach to solve the quantum many-body problem using machine learning

@inproceedings{Sheridan2021DataDrivenDM,
  title={Data-Driven Dynamical Mean-Field Theory: an error-correction approach to solve the quantum many-body problem using machine learning},
  author={Evan Sheridan and Christophe Rhodes and François Jamet and Ivan Rungger and Cedric Weber},
  year={2021}
}
Machine learning opens new avenues for modelling correlated materials. Quantum embedding approaches, such as the dynamical mean-field theory (DMFT), provide corrections to first-principles calculations for strongly correlated materials, which are poorly described at lower levels of theory. Such embedding approaches are computationally demanding on classical computing architectures, and hence remain restricted to small systems, which limits the scope of applicability without exceptional… Expand

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