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Elucidating Dynamical Behaviors in Kinetically Constrained Models via Energy-Activity Double-Biased Matrix Product
Yoong Hee Lee1, Jay-Hak Lee1, YounJoon Jung1
1Department of Chemistry, Seoul National University, Seoul 08826, Korea.
The Journal of Physical Chemistry Letters
|October 18, 2024
Summary
Researchers explored dynamical phase transitions in kinetically constrained models using an energy-activity double-bias approach and matrix product states. They identified a potential anomalous phase and validated their novel methods for studying complex dynamics.
Area of Science:
- Statistical Mechanics
- Condensed Matter Physics
- Computational Physics
Background:
- Kinetically constrained models exhibit complex dynamics relevant to various physical systems.
- Understanding dynamical phase transitions is crucial for characterizing system behavior.
- Previous methods faced limitations in analyzing large-deviation statistics of dynamics.
Purpose of the Study:
- To investigate dynamical phase transitions in the 1D Fredrickson-Andersen and East Models.
- To apply a novel energy-activity double-bias approach combined with matrix product state (MPS) methods.
- To explore the potential existence of anomalous phases in these systems.
Main Methods:
- Utilizing an energy-activity double-bias approach with 's' and 'g' fields.
- Employing matrix product state (MPS) methods for numerical approximation of large-deviation statistics.
- Calculating eigenvalues of tilted dynamical generators under double-biasing fields.
Main Results:
- Identified a nearly 'half-filled' state at moderate negative 'g' values, suggesting an anomalous phase.
- Obtained dynamical quantities under various 's', 'g', and temperature (T) conditions using tensor networks.
- Demonstrated qualitative consistency with mean-field and path sampling simulation results.
Conclusions:
- The study introduces novel methodologies for examining decoupled dynamical behaviors.
- The approach provides a fresh perspective on theoretical frameworks for dynamical phase transitions.
- Validated MPS methods for analyzing large-deviation statistics in kinetically constrained systems.
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