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arXiv 1804.02709cond-mat.stat-mechcond-mat.dis-nncond-mat.quant-gas
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渗流和XY模型中相变的机器学习

Machine learning of phase transitions in the percolation and XY models

Wanzhou Zhang, Jiayu Liu, Tzu-Chieh Wei

摘要:

本文应用机器学习方法研究了二维晶格上某些统计力学模型的相变,这些模型的相变涉及非局部或拓扑性质,包括位标和键标,XY模型和广义XY模型.我们发现,在一个全连接的神经网络中,只使用一个隐藏层,可以学习渗流过渡和数据崩溃,通过使用平均输出层给出正确的估计的临界指数$ν$。我们还研究了经典XY模型中的Berezinskiii-Kosterlitz-numerless跃迁,它涉及到拓扑缺陷--涡旋和反涡旋的结合和解结合。广义XY模型包含了更丰富的相,如顺磁相、顺磁相和准长程铁磁相,我们还将机器学习方法应用于其中,在两个特定的参数$Δ$值下,我们从仅用沿温度轴的数据训练的网络获得了一致的相图,其中$Δ$是纯XY耦合的相对权重。除了使用自旋配置(角度或自旋分量)作为卷积神经网络的输入信息之外,我们还设计了一种使用自旋方向直方图的特征工程方法,以便训练网络学习广义XY模型中的三个阶段,并证明它确实有效。通过使用系统尺寸$L\times L$训练的网络可以用于其他尺寸($L'\times L'$,其中$L'\ne L$)的相图,而无需任何进一步的训练。

英文摘要:

In this paper, we apply machine learning methods to study phase transitions in certain statistical mechanical models on the two dimensional lattices, whose transitions involve non-local or topological properties, including site and bond percolations, the XY model and the generalized XY model. We find that using just one hidden layer in a fully-connected neural network, the percolation transition can be learned and the data collapse by using the average output layer gives correct estimate of the critical exponent $ν$. We also study the Berezinskii-Kosterlitz-Thouless transition, which involves binding and unbinding of topological defects---vortices and anti-vortices, in the classical XY model. The generalized XY model contains richer phases, such as the nematic phase, the paramagnetic and the quasi-long-range ferromagnetic phases, and we also apply machine learning method to it. We obtain a consistent phase diagram from the network trained with only data along the temperature axis at two particular parameter $Δ$ values, where $Δ$ is the relative weight of pure XY coupling. Besides using the spin configurations (either angles or spin components) as the input information in a convolutional neural network, we devise a feature engineering approach using the histograms of the spin orientations in order to train the network to learn the three phases in the generalized XY model and demonstrate that it indeed works. The trained network by using system size $L\times L$ can be used to the phase diagram for other sizes ($L'\times L'$, where $L'\ne L$) without any further training.

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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