Snake net with a neural network for detecting multiple phases in the phase diagram

doi:10.1103/PhysRevE.107.065303

CORC > 理论物理研究所 > 中国科学院理论物理研究所 > 理论物理所科研产出 > 理论物理所1978-2010年知识产出

	Snake net with a neural network for detecting multiple phases in the phase diagram
	Sun, Xiaodong ; Yang, Huijiong 1; Wu, Nan 2; Scott, T. C.; Zhang, Jie ; Zhang, Wanzhou 4,5,6
刊名	PHYSICAL REVIEW E
	2023
卷号	107 期号:6 页码:65303
关键词	QUANTIZATION
ISSN号	2470-0045
DOI	10.1103/PhysRevE.107.065303
英文摘要	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)]. 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.
学科主题	Physics
语种	英语
内容类型	期刊论文
源URL	[http://ir.itp.ac.cn/handle/311006/28075]
专题	理论物理研究所_理论物理所1978-2010年知识产出
作者单位	1.Taiyuan Univ Technol, Coll Phys & Optoelect, Taiyuan 030024, Shanxi, Peoples R China 2.Taiyuan Univ Technol, Coll Data Sci, Taiyuan 030024, Shanxi, Peoples R China 3.Univ Sci & Technol China, Sch Phys Sci, Hefei 230026, Peoples R China 4.Scott, T. C.] Rhein Westfal TH Aachen, Inst Phys Chem, D-52056 Aachen, Germany 5.Univ Sci & Technol China, Hefei Natl Lab Phys Sci Microscale, Hefei 230026, Peoples R China 6.Univ Sci & Technol China, Dept Modern Phys, Hefei 230026, Peoples R China 7.Chinese Acad Sci, CAS Key Lab Theoret Phys, Inst Theoret Phys, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Sun, Xiaodong,Yang, Huijiong,Wu, Nan,et al. Snake net with a neural network for detecting multiple phases in the phase diagram[J]. PHYSICAL REVIEW E,2023,107(6):65303.
APA	Sun, Xiaodong,Yang, Huijiong,Wu, Nan,Scott, T. C.,Zhang, Jie,&Zhang, Wanzhou.(2023).Snake net with a neural network for detecting multiple phases in the phase diagram.PHYSICAL REVIEW E,107(6),65303.
MLA	Sun, Xiaodong,et al."Snake net with a neural network for detecting multiple phases in the phase diagram".PHYSICAL REVIEW E 107.6(2023):65303.