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

被引：116

作者：

Arsenault, Louis-Francois ^{[1
]}

Lopez-Bezanilla, Alejandro ^{[2
]}

von Lilienfeld, O. Anatole ^{[3
,4
]}

Millis, Andrew J. ^{[1
]}

机构：

[1] Columbia Univ, Dept Phys, New York, NY 10027 USA

[2] Argonne Natl Lab, Div Mat Sci, Argonne, IL 60439 USA

[3] Univ Basel, Dept Chem, Inst Phys Chem, CH-4056 Basel, Switzerland

[4] Argonne Natl Lab, Argonne Leadership Comp Facil, Argonne, IL 60439 USA

来源：

PHYSICAL REVIEW B | 2014年 / 90卷 / 15期

基金：

瑞士国家科学基金会;

关键词：

POTENTIAL-ENERGY SURFACES; INFINITE DIMENSIONS; HUBBARD-MODEL; FERMIONS; LIMIT;

D O I：

10.1103/PhysRevB.90.155136

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页数：16

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