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Predictive Indicator of Critical Point in Equilibrium and Nonequilibrium Magnetic Systems
Tianyi Zhang1, Caihua Wan1,2, Xiufeng Han1,2,3
1Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing National Laboratory for Condensed Matter Physics, Beijing 100190, China.
Physical Review Letters
|November 21, 2025
Summary
Predicting phase transition critical points is now possible for both equilibrium and nonequilibrium magnetic systems. New indicators based on response functions offer a unified, noise-resilient strategy to avoid system collapses and reduce costs.
Area of Science:
- Physics
- Materials Science
- Statistical Mechanics
Background:
- Predicting critical points in phase transitions is vital for system stability and cost reduction.
- Existing methods struggle with unified approaches for both equilibrium and nonequilibrium transitions due to system complexity.
Purpose of the Study:
- To develop unified predictive indicators for critical points in equilibrium and nonequilibrium magnetic systems.
- To leverage frequency-dependent response functions for robust criticality detection.
Main Methods:
- Analyzing the static magnetization response function for equilibrium transitions.
- Utilizing the dynamic response function at ferromagnetic resonance frequency for nonequilibrium transitions.
- Unifying static and dynamic indicators within a first-order linear differential system framework.
Main Results:
- Static response function diverges at critical fields for equilibrium transitions, indicating transition order and critical exponents.
- Dynamic response function diverges at critical points for nonequilibrium transitions, proving robust against thermal noise.
- A generalizable strategy for predicting criticality in both transition types was demonstrated.
Conclusions:
- Proposed predictive indicators offer a unified approach to determine critical points in diverse magnetic systems.
- The method enhances system stability by predicting abrupt collapses and reduces experimental/computational costs.
- This generalizable strategy advances the understanding and control of phase transitions.
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