Predictive Indicator of Critical Point in Equilibrium and Nonequilibrium Magnetic Systems

Determining critical points of phase transitions from partial data is essential to avoid abrupt system collapses and to reduce experimental or computational costs. However, the complex physical systems and phase transition phenomena have long hindered the development of unified approaches applicable to both equilibrium and nonequilibrium phase transitions. In this Letter, we propose predictive indicators to determine critical points in equilibrium and nonequilibrium magnetic systems based on the frequency-dependent response function. For equilibrium phase transitions, such as magnetization switching under a magnetic field, the static magnetization response function to a perturbing magnetic field diverges at the critical field, serving as a noise-resilient predictive indicator that also reflects the transition order and critical exponents. In contrast, for nonequilibrium phase transitions such as magnetization switching driven by transfer torque, static response fails to signal criticality. Instead, the dynamic response at ferromagnetic resonance frequency diverges at the critical point, which is also robust against thermal noise. We further demonstrate that these static and dynamic indicators can be unified in the frame of first-order linear differential systems, offering a generalizable strategy for predicting criticality in both equilibrium and nonequilibrium transitions.

Authors

Institutions

Publication Details

Journal
Physical Review Letters
Published
2025-11-06
DOI
https://doi.org/10.1103/81nz-k6tb
Primary Topic
Ecosystem dynamics and resilience
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Predictive Indicator of Critical Point in Equilibrium and Nonequilibrium Magnetic Systems

Xiufeng Han, T. Zhang, Caihua Wan
Physical Review Letters
Ecosystem dynamics and resilience
article

Predictive Indicator of Critical Point in Equilibrium and Nonequilibrium Magnetic Systems

Xiufeng Han, T. Zhang, Caihua Wan
article en

Abstract

Determining critical points of phase transitions from partial data is essential to avoid abrupt system collapses and to reduce experimental or computational costs. However, the complex physical systems and phase transition phenomena have long hindered the development of unified approaches applicable to both equilibrium and nonequilibrium phase transitions. In this Letter, we propose predictive indicators to determine critical points in equilibrium and nonequilibrium magnetic systems based on the frequency-dependent response function. For equilibrium phase transitions, such as magnetization switching under a magnetic field, the static magnetization response function to a perturbing magnetic field diverges at the critical field, serving as a noise-resilient predictive indicator that also reflects the transition order and critical exponents. In contrast, for nonequilibrium phase transitions such as magnetization switching driven by transfer torque, static response fails to signal criticality. Instead, the dynamic response at ferromagnetic resonance frequency diverges at the critical point, which is also robust against thermal noise. We further demonstrate that these static and dynamic indicators can be unified in the frame of first-order linear differential systems, offering a generalizable strategy for predicting criticality in both equilibrium and nonequilibrium transitions.

Physical Review LettersVol. 135(19)
Chinese Academy of Sciences (CN), Songshan Lake Materials Laboratory (CN), University of Chinese Academy of Sciences (CN)
Openalex Percentile: Top 69%
Ecosystem dynamics and resilience
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.