Spatial distribution order parameter prediction of collective system using graph network

doi:10.1088/1674-1056/acb9fa

中国物理B ›› 2023, Vol. 32 ›› Issue (5): 56402-056402.doi: 10.1088/1674-1056/acb9fa

Spatial distribution order parameter prediction of collective system using graph network

Huimin Zhao(赵慧敏)¹, Rui Wang(王瑞)¹, Cai Zhao(赵偲)², and Wen Zheng(郑文)^1,3,†

1 Institute of Public-Safety and Big Data, College of Data Science, Taiyuan University of Technology, Taiyuan 030024, China;
2 Center of Information Management and Development, Taiyuan University of Technology, Taiyuan 030024, China;
3 Center for Healthy Big Data, Changzhi Medical College, Changzhi 046000, China

收稿日期:2022-10-06 修回日期:2022-12-05 接受日期:2023-02-08 出版日期:2023-04-21 发布日期:2023-04-28
通讯作者: Wen Zheng E-mail:zhengwen@tyut.edu.cn
基金资助:
Project supported by the National Natural Science Foundation of China (Grant No. 11702289), Key core technology and generic technology research and development project of Shanxi Province of China (Grant No. 2020XXX013), and the National Key Research and Development Project of China.

Spatial distribution order parameter prediction of collective system using graph network

Huimin Zhao(赵慧敏)¹, Rui Wang(王瑞)¹, Cai Zhao(赵偲)², and Wen Zheng(郑文)^1,3,†

1 Institute of Public-Safety and Big Data, College of Data Science, Taiyuan University of Technology, Taiyuan 030024, China;
2 Center of Information Management and Development, Taiyuan University of Technology, Taiyuan 030024, China;
3 Center for Healthy Big Data, Changzhi Medical College, Changzhi 046000, China

Received:2022-10-06 Revised:2022-12-05 Accepted:2023-02-08 Online:2023-04-21 Published:2023-04-28
Contact: Wen Zheng E-mail:zhengwen@tyut.edu.cn
Supported by:
Project supported by the National Natural Science Foundation of China (Grant No. 11702289), Key core technology and generic technology research and development project of Shanxi Province of China (Grant No. 2020XXX013), and the National Key Research and Development Project of China.

摘要/Abstract

摘要： In the past few decades, the study of collective motion phase transition process has made great progress. It is also important for the description of the spatial distribution of particles. In this work, we propose a new order parameter φ to quantify the degree of order in the spatial distribution of particles. The results show that the spatial distribution order parameter can effectively describe the transition from a disorderly moving phase to a phase with a coherent motion of the particle distribution and the same conclusion could be obtained for systems with different sizes. Furthermore, we develop a powerful molecular dynamic graph network (MDGNet) model to realize the long-term prediction of the self-propelled collective system solely from the initial particle positions and movement angles. Employing this model, we successfully predict the order parameters of the specified time step. And the model can also be applied to analyze other types of complex systems with local interactions.

关键词: order parameter, graph network, collective system, active matter

Abstract: In the past few decades, the study of collective motion phase transition process has made great progress. It is also important for the description of the spatial distribution of particles. In this work, we propose a new order parameter φ to quantify the degree of order in the spatial distribution of particles. The results show that the spatial distribution order parameter can effectively describe the transition from a disorderly moving phase to a phase with a coherent motion of the particle distribution and the same conclusion could be obtained for systems with different sizes. Furthermore, we develop a powerful molecular dynamic graph network (MDGNet) model to realize the long-term prediction of the self-propelled collective system solely from the initial particle positions and movement angles. Employing this model, we successfully predict the order parameters of the specified time step. And the model can also be applied to analyze other types of complex systems with local interactions.

Key words: order parameter, graph network, collective system, active matter

中图分类号: (Phase equilibria)

64.75.-g

07.05.Mh (Neural networks, fuzzy logic, artificial intelligence) 05.65.+b (Self-organized systems) 87.64.Aa (Computer simulation)

Huimin Zhao(赵慧敏), Rui Wang(王瑞), Cai Zhao(赵偲), and Wen Zheng(郑文). Spatial distribution order parameter prediction of collective system using graph network[J]. 中国物理B, 2023, 32(5): 56402-056402.

Huimin Zhao(赵慧敏), Rui Wang(王瑞), Cai Zhao(赵偲), and Wen Zheng(郑文). Spatial distribution order parameter prediction of collective system using graph network[J]. Chin. Phys. B, 2023, 32(5): 56402-056402.

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Spatial distribution order parameter prediction of collective system using graph network

Spatial distribution order parameter prediction of collective system using graph network

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