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

IF 1.2 4区 物理与天体物理 Q3 PHYSICS, MULTIDISCIPLINARY Revista Mexicana De Fisica Pub Date : 2023-03-01 DOI:10.31349/revmexfis.69.020502
Gustavo Alexis Dominguez Castro, Rosario Paredes Gutiérrez
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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. We demonstrate that our neural network achieves to correctly uncover the nature of the single-particle states even when the wave functions come from a more complex Hamiltonian than the one used to train the network.
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拟周期一维晶格中单粒子局部化问题的人工神经网络
在过去的几年里,在几个科学分支中,使用机器学习算法来解决分类问题的情况有所增加。特别地,人工神经网络的监督学习技术已经成功地应用于物质的相分类。在本文中,我们使用一个完全连接的前馈神经网络对准周期一维晶格中产生的扩展和局部单粒子态进行分类。我们证明,即使波函数来自比用于训练网络的哈密顿函数更复杂的哈密顿函数,我们的神经网络也能正确地揭示单粒子状态的本质。
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来源期刊
Revista Mexicana De Fisica
Revista Mexicana De Fisica 物理-物理:综合
CiteScore
2.20
自引率
11.80%
发文量
87
审稿时长
4-8 weeks
期刊介绍: Durante los últimos años, los responsables de la Revista Mexicana de Física, la Revista Mexicana de Física E y la Revista Mexicana de Física S, hemos realizado esfuerzos para fortalecer la presencia de estas publicaciones en nuestra página Web ( http://rmf.smf.mx).
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