Artificial neural network for the single-particle localization problem in quasiperiodic one-dimensional lattices

Research output: Contribution to journalArticleResearchpeer review

Authors

  • G. A. Domínguez-Castro
  • R. Paredes

External Research Organisations

  • Universidad Nacional Autónoma de México (UNAM)
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Details

Original languageEnglish
Article number020502
JournalRevista Mexicana de Fisica
Volume69
Issue number2
Publication statusPublished - 1 Mar 2023
Externally publishedYes

Abstract

The use of machine learning algorithms to address classification problems in several scientific branches has increased over the past years. In particular, the supervised learning technique with artificial neural networks has been successfully employed in classifying phases of matter. In this article, we use a fully connected feed-forward neural network to classify extended and localized single-particle states that arise from quasiperiodic one-dimensional lattices.

Keywords

    Algorithms, Hamiltonian, single-particle states

ASJC Scopus subject areas

Cite this

Artificial neural network for the single-particle localization problem in quasiperiodic one-dimensional lattices. / Domínguez-Castro, G. A.; Paredes, R.
In: Revista Mexicana de Fisica, Vol. 69, No. 2, 020502, 01.03.2023.

Research output: Contribution to journalArticleResearchpeer review

Domínguez-Castro GA, Paredes R. Artificial neural network for the single-particle localization problem in quasiperiodic one-dimensional lattices. Revista Mexicana de Fisica. 2023 Mar 1;69(2):020502. doi: 10.31349/RevMexFis.69.020502
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