当前位置: X-MOL 学术Chin. Phys. Lett. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Machine Learning for Many-Body Localization Transition
Chinese Physics Letters ( IF 3.5 ) Pub Date : 2020-08-16 , DOI: 10.1088/0256-307x/37/8/080501
Wen-Jia Rao

We employ the methods of machine learning to study the many-body localization (MBL) transition in a 1D random spin system. By using the raw energy spectrum without pre-processing as training data, it is shown that the MBL transition point is correctly predicted by the machine. The structure of the neural network reveals the nature of this dynamical phase transition that involves all energy levels, while the bandwidth of the spectrum and nearest level spacing are the two dominant patterns and the latter stands out to classify phases. We further use a comparative unsupervised learning method, i.e., principal component analysis, to confirm these results.

中文翻译:

多体本地化过渡的机器学习

我们采用机器学习的方法来研究一维随机旋转系统中的多体定位(MBL)过渡。通过使用未经预处理的原始能谱作为训练数据,表明MBL过渡点已由机器正确预测。神经网络的结构揭示了涉及所有能级的动态相变的本质,而频谱的带宽和最接近的能级间隔是两个主要模式,后者突出显示了对相的分类。我们进一步使用一种比较无监督的学习方法,即主成分分析,来确认这些结果。
更新日期:2020-08-18
down
wechat
bug