Phase Transition Study meets Machine Learning

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.

arXiv commentarXiv admin note: text overlap with arXiv:2303.06752

References in corpus (37)

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• The phase diagram of dense QCD
• Learning phase transitions by confusion
• Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders
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• Caloric curves and critical behavior in nuclei
• Flow-based generative models for Markov chain Monte Carlo in lattice field theory
• Equivariant flow-based sampling for lattice gauge theory
• Limiting temperatures and the Equation of State of Nuclear Matter
• Application of Information Theory in Nuclear Liquid Gas Phase Transition
• Machine learning the nuclear mass
• Regressive and generative neural networks for scalar field theory
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• Identifying the nature of the QCD transition in relativistic collision of heavy nuclei with deep learning
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• $\alpha$-clustering effect on flows of direct photons in heavy-ion collisions
• Reconstructing the neutron star equation of state from observational data via automatic differentiation
• QCD Equation of State of Dense Nuclear Matter from a Bayesian Analysis of Heavy-Ion Collision Data
• Determining the temperature in heavy-ion collisions with multiplicity distribution
• Constraining nuclear symmetry energy with the charge radii of mirror-pair nuclei
• System dependence of away-side broadening and $\alpha$-clustering light nuclei structure effect in dihadron azimuthal correlations
• An equation-of-state-meter for CBM using PointNet
• Fourier-Flow model generating Feynman paths
• Impact of fragment formation on shear viscosity in the nuclear liquid-gas phase transition region
• Influence of nuclear structure in relativistic heavy-ion collisions
• Continuous-mixture Autoregressive Networks for efficient variational calculation of many-body systems
• Unsupervised Recognition of Informative Features via Tensor Network Machine Learning and Quantum Entanglement Variations
• Quantum gradient descent algorithms for nonequilibrium steady states and linear algebraic systems
• Digital quantum simulation of an extended Agassi model: Using machine learning to disentangle its phase-diagram
• Effect of initial-state geometric configurations on the nuclear liquid-gas phase transition
• Machine learning phase transitions of the three-dimensional Ising universality class
• Discover the GellMann-Okubo formula with machine learning
• Finding signatures of the nuclear symmetry energy in heavy-ion collisions with deep learning

Cited by in corpus (13)

• Properties of the QCD Matter: A Review of Selected Results from the ALICE Experiment
• Dense Matter in Neutron Stars with eXTP
• Principal components of nuclear mass models
• Bayesian inference of neutron-skin thickness and neutron-star observables based on effective nuclear interactions
• Machine learning study to identify collective flow in small and large colliding systems
• Information entropy for central ${}^{197}$Au+${}^{197}$Au collisions with the ultrarelativistic quantum molecular dynamics model
• Nucleon Short-Range Correlations and High-Momentum Dynamics: Implications on the Equation of State of Dense Matter
• Bayesian inference of nuclear incompressibility from collective flow in mid-central Au+Au collisions at 400--1500 MeV/nucleon
• Cluster Counting Algorithm for the CEPC Drift Chamber using LSTM and DGCNN
• Exploring the Diversity of Nuclear Density through Information Entropy
• Further exploration of the machine-learning-based nuclear mass table
• Extraction of the color dipole amplitude with physics-informed neural networks
• Neutron Star Equation of State with Nucleon Short-Range Correlations: A Concise Review and Open Issues