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arXiv 2305.04035cond-mat.str-elcond-mat.dis-nn
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利用关联函数对弱拓扑绝缘体的二维相互作用硬芯玻色子模型进行监督学习

Supervised learning of an interacting 2D hard-core boson model of a weak topological insulator using correlation functions

Amrita Ghosh, Mugdha Sarkar

AI总结:

本文用基于量子 Monte Carlo 关联函数训练的简单机器学习模型,识别蜂窝晶格硬芯玻色子的多相边界,并预测最近邻相互作用下弱拓扑绝缘相及其拓扑不变量。

摘要:

我们使用机器学习(ML)技术,研究了二维周期性蜂窝晶格上的硬芯玻色子系统,该系统受到沿 y 方向交替变号的在位势作用。该模型具有包含六种不同相的丰富相图,包括电荷密度波、超流相和两种二聚体绝缘相;其中一种绝缘相同时也是弱拓扑绝缘体,具有零 Chern 数但非平庸的 Berry 相 [SciPost Phys. 10, 059 (2021)]。利用通过量子 Monte Carlo 方法计算得到的两种不同关联函数,一个相对简单的 ML 模型能够同时从各种相中学习信息,并准确预测它们的相边界。通过采用在无显式相互作用的 Hamilton 量所得数据集上训练的 ML 模型,我们确定了系统在存在最近邻相互作用时的相结构。此外,我们通过预测拓扑不变量,研究了弱拓扑绝缘相针对相互作用的鲁棒性;否则该拓扑不变量很难获得。

英文摘要:

We study a system of hard-core bosons on a two-dimensional periodic honeycomb lattice subjected to an on-site potential with alternating signs along $y$-direction, using machine learning (ML) techniques. The model hosts a rich phase diagram consisting of six different phases including a charge density wave, a superfluid phase and two dimer insulator phases, one of which is also a weak topological insulator with zero Chern number but a non-trivial Berry phase [SciPost Phys. 10, 059 (2021)]. Using two distinct correlation functions computed via quantum Monte Carlo method, a relatively simple ML model is able to learn information from the various phases simultaneously and accurately predict their phase boundaries. By employing our ML model trained on the dataset obtained from the Hamiltonian without explicit interactions, we determine the phase structure of the system in the presence of nearest-neighbor interactions. Additionally, we investigate the robustness of the weak topological insulator phase against interactions by predicting the topological invariant, which is otherwise difficult to obtain.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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