Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data-Driven Dynamical Mean-Field Theory: an error-correction approach to
  solve the quantum many-body problem using machine learning   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 computational resources. Here we outline a
data-driven machine learning process for solving the Anderson Impurity Model
(AIM) - the central component of DMFT calculations. The key advance is the use
of an ensemble error-correction approach to generate fast and accurate
solutions of AIM. An example calculation of the Mott transition using DMFT in
the single band Hubbard model is given as an example of the technique, and is
validated against the most accurate available method. This new approach is
called data-driven dynamical mean-field theory (\textbf{d$^{3}$MFT}).

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The categories of this paper are: stat
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-ex
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The categories of this paper are: solv-int
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The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
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The categories of this paper are: math-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: gr-qc
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The categories of this paper are: atom-ph
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The categories of this paper are: funct-an
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The categories of this paper are: hep-lat
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The categories of this paper are: mtrl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: quant-ph