论文标题
机器学习非热拓扑阶段
Machine learning non-Hermitian topological phases
论文作者
论文摘要
由于没有遗传学对应物的非常规特性，非富裕拓扑阶段已经引起了广泛的兴趣。在这项工作中，我们建议使用机器学习根据其绕组的数量来识别和预测非炎热拓扑阶段。我们考虑了两个示例 - 非热的su-schrieffer-Heeger模型及其在一个维度和非三个维度的非热鼻线半学的广义版本 - 以证明使用神经网络的使用来准确表征拓扑阶段。我们表明，对于一个维度模型，完全连接的神经网络的精度大于99.9 \％，并且对引入疾病是可靠的。对于三维模型，我们发现卷积神经网络可以准确预测不同的拓扑阶段。
Non-Hermitian topological phases have gained widespread interest due to their unconventional properties, which have no Hermitian counterparts. In this work, we propose to use machine learning to identify and predict non-Hermitian topological phases, based on their winding number. We consider two examples -- non-Hermitian Su-Schrieffer-Heeger model and its generalized version in one dimension and non-Hermitian nodal line semimetal in three dimensions -- to demonstrate the use of neural networks to accurately characterize the topological phases. We show that for the one dimensional model, a fully connected neural network gives an accuracy greater than 99.9\%, and is robust to the introduction of disorder. For the three dimensional model, we find that a convolutional neural network accurately predicts the different topological phases.
