Physical information-enhanced graph neural network for predicting phase separation

doi:10.1088/1674-1056/ad4328

中国物理B ›› 2024, Vol. 33 ›› Issue (7): 70702-070702.doi: 10.1088/1674-1056/ad4328

Physical information-enhanced graph neural network for predicting phase separation

Yaqiang Zhang(张亚强)¹, Xuwen Wang(王煦文)¹, Yanan Wang(王雅楠)¹, and Wen Zheng(郑文)^1,2,3,†

1 Institute of Public-Safety and Big Data, College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Jinzhong 030600, China;
2 Shanxi Engineering Research Centre for Intelligent Data Assisted Treatment, Changzhi Medical College, Changzhi 046000, China;
3 Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 200050, China

收稿日期:2024-03-03 修回日期:2024-04-08 接受日期:2024-04-25 出版日期:2024-06-18 发布日期:2024-06-28
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) and the Key Core Technology and Generic Technology Research and Development Project of Shanxi Province, China (Grant No. 2020XXX013).

Physical information-enhanced graph neural network for predicting phase separation

Yaqiang Zhang(张亚强)¹, Xuwen Wang(王煦文)¹, Yanan Wang(王雅楠)¹, and Wen Zheng(郑文)^1,2,3,†

1 Institute of Public-Safety and Big Data, College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Jinzhong 030600, China;
2 Shanxi Engineering Research Centre for Intelligent Data Assisted Treatment, Changzhi Medical College, Changzhi 046000, China;
3 Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 200050, China

Received:2024-03-03 Revised:2024-04-08 Accepted:2024-04-25 Online:2024-06-18 Published:2024-06-28
Contact: Wen Zheng E-mail:zhengwen@tyut.edu.cn
Supported by:
Project supported by the National Natural Science Foundation of China (Grant No. 11702289) and the Key Core Technology and Generic Technology Research and Development Project of Shanxi Province, China (Grant No. 2020XXX013).

摘要/Abstract

摘要： Although phase separation is a ubiquitous phenomenon, the interactions between multiple components make it difficult to accurately model and predict. In recent years, machine learning has been widely used in physics simulations. Here, we present a physical information-enhanced graph neural network (PIENet) to simulate and predict the evolution of phase separation. The accuracy of our model in predicting particle positions is improved by 40.3% and 51.77% compared with CNN and SVM respectively. Moreover, we design an order parameter based on local density to measure the evolution of phase separation and analyze the systematic changes with different repulsion coefficients and different Schmidt numbers. The results demonstrate that our model can achieve long-term accurate predictions of order parameters without requiring complex handcrafted features. These results prove that graph neural networks can become new tools and methods for predicting the structure and properties of complex physical systems.

关键词: graph neural network, phase separation, machine learning, dissipative particle dynamics

Abstract: Although phase separation is a ubiquitous phenomenon, the interactions between multiple components make it difficult to accurately model and predict. In recent years, machine learning has been widely used in physics simulations. Here, we present a physical information-enhanced graph neural network (PIENet) to simulate and predict the evolution of phase separation. The accuracy of our model in predicting particle positions is improved by 40.3% and 51.77% compared with CNN and SVM respectively. Moreover, we design an order parameter based on local density to measure the evolution of phase separation and analyze the systematic changes with different repulsion coefficients and different Schmidt numbers. The results demonstrate that our model can achieve long-term accurate predictions of order parameters without requiring complex handcrafted features. These results prove that graph neural networks can become new tools and methods for predicting the structure and properties of complex physical systems.

Key words: graph neural network, phase separation, machine learning, dissipative particle dynamics

中图分类号: (Neural networks, fuzzy logic, artificial intelligence)

07.05.Mh

64.75.Gh (Phase separation and segregation in model systems (hard spheres, Lennard-Jones, etc.)) 83.10.Rs (Computer simulation of molecular and particle dynamics) 87.64.Aa (Computer simulation)

Yaqiang Zhang(张亚强), Xuwen Wang(王煦文), Yanan Wang(王雅楠), and Wen Zheng(郑文). Physical information-enhanced graph neural network for predicting phase separation[J]. 中国物理B, 2024, 33(7): 70702-070702.

Yaqiang Zhang(张亚强), Xuwen Wang(王煦文), Yanan Wang(王雅楠), and Wen Zheng(郑文). Physical information-enhanced graph neural network for predicting phase separation[J]. Chin. Phys. B, 2024, 33(7): 70702-070702.

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Physical information-enhanced graph neural network for predicting phase separation

Physical information-enhanced graph neural network for predicting phase separation

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