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

Arsenault, Louis-Francois and Lopez-Bezanilla, Alejandro and von Lilienfeld, O. Anatole and Millis, Andrew J.. (2014) Machine learning for many-body physics: The case of the Anderson impurity model. Physical Review B, 90 (15). p. 155136.

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Official URL: http://edoc.unibas.ch/53262/

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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.
Faculties and Departments:05 Faculty of Science > Departement Chemie > Chemie > Physikalische Chemie (Lilienfeld)
UniBasel Contributors:von Lilienfeld, Anatole
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:American Physical Society
ISSN:2469-9950
e-ISSN:2469-9969
Note:Publication type according to Uni Basel Research Database: Journal article
Language:English
Identification Number:
Last Modified:10 May 2017 10:36
Deposited On:25 Jan 2017 14:06

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