Phase Transition Study Meets Machine Learning

doi:10.1088/0256-307X/40/12/122101

Chin. Phys. Lett.

2023, Vol. 40

Issue (12): 122101 DOI: 10.1088/0256-307X/40/12/122101

NUCLEAR PHYSICS

Phase Transition Study Meets Machine Learning

Yu-Gang Ma^1,2*, Long-Gang Pang^3*, Rui Wang^1,4,5*, and Kai Zhou^6*

¹Key Laboratory of Nuclear Physics and Ion-beam Application (MOE), and Institute of Modern Physics, Fudan University, Shanghai 200433, China
²Shanghai Research Center for Theoretical Nuclear Physics, NSFC and Fudan University, Shanghai 200438, China
³Institute of Particle Physics and Key Laboratory of Quark and Lepton Physics (MOE), Central China Normal University, Wuhan 430079, China
⁴Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
⁵INFN Sezione di Catania, 95123 Catania, Italy
⁶Frankfurt Institute for Advanced Studies (FIAS), D-60438 Frankfurt am Main, Germany

Cite this article:

Yu-Gang Ma, Long-Gang Pang, Rui Wang et al 2023 Chin. Phys. Lett. 40 122101

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Abstract In recent years, machine learning (ML) techniques have emerged as powerful tools for studying many-body complex systems, and encompassing phase transitions in various domains of physics. This mini review provides a concise yet comprehensive examination of the advancements achieved in applying ML to investigate phase transitions, with a primary focus on those involved in nuclear matter studies.

Received: 10 October 2023 Review Published: 06 December 2023

PACS:	21.65.-f	(Nuclear matter)
	21.65.Mn	(Equations of state of nuclear matter)
	21.65.Qr	(Quark matter)
	07.05.Mh	(Neural networks, fuzzy logic, artificial intelligence)
	05.70.Fh	(Phase transitions: general studies)

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https://cpl.iphy.ac.cn/10.1088/0256-307X/40/12/122101 OR https://cpl.iphy.ac.cn/Y2023/V40/I12/122101

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	Long-Gang Pang
	Rui Wang
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-clustering structure on nuclear reaction and relativistic heavy-ion collisions||

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