首页|Physical information-enhanced graph neural network for predicting phase separation

Physical information-enhanced graph neural network for predicting phase separation

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Although phase separation is a ubiquitous phenomenon,the interactions between multiple components make it diffi-cult 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.

graph neural networkphase separationmachine learningdissipative particle dynamics

张亚强、王煦文、王雅楠、郑文

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Institute of Public-Safety and Big Data,College of Computer Science and Technology(College of Data Science),Taiyuan University of Technology,Jinzhong 030600,China

Shanxi Engineering Research Centre for Intelligent Data Assisted Treatment,Changzhi Medical College,Changzhi 046000,China

Innovation Academy for Microsatellites of Chinese Academy of Sciences,Shanghai 200050,China

National Natural Science Foundation of ChinaKey Core Technology and Generic Technology Research and Development Project of Shanxi Province,China

117022892020XXX013

2024

10.1088/1674-1056/ad4328

中国物理B(英文版)
中国物理学会和中国科学院物理研究所

中国物理B(英文版)

CSTPCDEI
影响因子：0.995
ISSN：1674-1056
年,卷(期)：2024.33(7)
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