Universality class of machine learning for critical phenomena

Gaoke Hu, Yu Sun, Teng Liu, Yongwen Zhang, Maoxin Liu, Jingfang Fan, Wei Chen, Xiaosong Chen

Research output: Contribution to journalArticlepeer-review


Herein, percolation phase transitions on a two-dimensional lattice were studied using machine learning techniques. Results reveal that different phase transitions belonging to the same universality class can be identified using the same neural networks (NNs), whereas phase transitions of different universality classes require different NNs. Based on this finding, we proposed the universality class of machine learning for critical phenomena. Furthermore, we investigated and discussed the NNs of different universality classes. Our research contributes to machine learning by relating the NNs with the universality class.

Original languageEnglish
Article number120511
JournalScience China: Physics, Mechanics and Astronomy
Issue number12
StatePublished - Dec 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023, Science China Press.


  • machine learning
  • percolation
  • universality class


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