We introduce an unsupervised machine learning method based on Siamese Neural Networks (SNN) to detect phase boundaries. This method is applied to Monte-Carlo simulations of Ising-type systems and Rydberg atom arrays. In both cases the SNN reveals phase boundaries consistent with prior research. The combination of leveraging the power of feed-forward neural networks, unsupervised learning and the ability to learn about multiple phases without knowing about their existence provides a powerful method to explore new and unknown phases of matter.
翻译:我们采用了一种基于Siamse神经网络的不受监督的机器学习方法,以探测相位边界,该方法适用于Monte-Carlo的Ising型系统和Rydberg原子阵列的模拟和Rydberg原子阵列,在这两种情况下,SNN都揭示了与先前研究相一致的相位界限。 利用进料推进神经网络的力量、不受监督的学习和在不知道其存在的情况下学习多个阶段的能力相结合,为探索新的和未知的事物阶段提供了有力的方法。