Physical information-enhanced graph neural network for predicting phase separation

doi:10.1088/1674-1056/ad4328

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.

Keywords: graph neural network phase separation machine learning dissipative particle dynamics

Received: 03 March 2024
Revised: 08 April 2024
Accepted manuscript online: 25 April 2024

PACS:	07.05.Mh	(Neural networks, fuzzy logic, artificial intelligence)
	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)

Fund: 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).

Corresponding Authors: Wen Zheng
E-mail: zhengwen@tyut.edu.cn

Cite this article:

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 2024 Chin. Phys. B 33 070702

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