Sun Xiaodong, Yang Huijiong, Wu Nan, Scott T C, Zhang Jie, Zhang Wanzhou
College of Physics and Optoelectronics, Taiyuan University of Technology, Shanxi 030024, China.
College of Data Science, Taiyuan University of Technology, Shanxi 030024, China.
Phys Rev E. 2023 Jun;107(6-2):065303. doi: 10.1103/PhysRevE.107.065303.
Unsupervised machine learning applied to the study of phase transitions is an ongoing and interesting research direction. The active contour model, also called the snake model, was initially proposed for target contour extraction in two-dimensional images. In order to obtain a physical phase diagram, the snake model with an artificial neural network is applied in an unsupervised learning way by the authors of [Phys. Rev. Lett. 120, 176401 (2018)0031-900710.1103/PhysRevLett.120.176401]. It guesses the phase boundary as an initial snake and then drives the snake to convergence with forces estimated by the artificial neural network. In this work we extend this unsupervised learning method with one contour to a snake net with multiple contours for the purpose of obtaining several phase boundaries in a phase diagram. For the classical Blume-Capel model, the phase diagram containing three and four phases is obtained. Moreover, a balloon force is introduced, which helps the snake to leave a wrong initial position and thus may allow for greater freedom in the initialization of the snake. Our method is helpful in determining the phase diagram with multiple phases using just snapshots of configurations from cold atoms or other experiments without knowledge of the phases.
应用于相变研究的无监督机器学习是一个正在进行且有趣的研究方向。活动轮廓模型，也称为蛇形模型，最初是为二维图像中的目标轮廓提取而提出的。为了获得物理相图，[《物理评论快报》120, 176401 (2018)0031 - 900710.1103/PhysRevLett.120.176401]的作者以无监督学习的方式将带有人工神经网络的蛇形模型应用其中。它将相边界猜测为初始蛇形，并通过人工神经网络估计的力驱动蛇形收敛。在这项工作中，我们将这种单轮廓的无监督学习方法扩展为具有多个轮廓的蛇形网络，以便在相图中获得多个相边界。对于经典的布鲁姆 - 卡佩尔模型，得到了包含三相和四相的相图。此外，引入了一种气球力，它有助于蛇形离开错误的初始位置，从而在蛇形的初始化中可能允许更大的自由度。我们的方法有助于仅使用来自冷原子或其他实验的构型快照来确定多相相图，而无需知道相的情况。
