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Machine learning for many-body physics: The case of the Anderson impurity model

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Louis-François Arsenault Alejandro Lopez-Bezanilla O. Anatole von Lilienfeld and Andrew J. Millis,
Project From atomistic exploration of chemical compound space towards bio-molecular design: Quantum mechanical rational compound design (QM-RCD)
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Original article (peer-reviewed)

Journal Physical Review B
Volume (Issue) 90
Page(s) 155136
Title of proceedings Physical Review B
DOI 10.1103/physrevb.90.155136

Abstract

Machine learning methods are applied to finding the Green's function of the Anderson impurity model, a basic model system of quantum many-body condensed-matter physics. Different methods of parametrizing the Green's function are investigated; a representation in terms of Legendre polynomials is found to be superior due to its limited number of coefficients and its applicability to state of the art methods of solution. The dependence of the errors on the size of the training set is determined. The results indicate that a machine learning approach to dynamical mean-field theory may be feasible.
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