Unlocking Hidden Topological Multistability via Biphasic Correlated Order Evolution

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Authors: Jin‐Bing Wu, Zhenghao Guo, Baoming Shi, Daoxing Luo, Lei Zhang, Yanqing Lu, Wei Hu
Cited by: 3
References: 31

Abstract

Topological multistability reflects the complexity of structure evolution inside ordered condensed matter. For a given thermodynamic system, the actual attained stable states decline sharply compared with the theoretical anticipation, which severely restricts the diversity of the material structure and properties. Here, a biphasic correlated order evolution strategy is proposed to overcome this constraint and unlock the hidden topological multistability correspondingly. Because of the remarkably varied physical properties across the phase transition point, the energetically unfavorable transformation among topological multistable states in certain phases may become favorable at adjacent phases. The concept is demonstrated in a photopatterned liquid crystal. The sample is pressed by mechanical stress of different intensities in the smectic A phase to obtain several ordered textures of distinct topologies. After reheating to the nematic phase, these textures transform directly to nematic topological multistable states, which are unavailable as predicted by the string method. The modified Landau-de Gennes theory is adopted to disclose the behaviors and mechanisms of biphasic correlated order evolution, where the hidden topological multistability at nematic phase is achieved from corresponding ordered smectic A states due to the topology invariance and orientational order inherence across the phase transition. The different sequences for the free energy levels of multistable states at adjacent phases corresponding to the same series of topologies are further confirmed. This Letter extends our understanding of self-organized condensed matter and may drastically enrich their diverse structures with unprecedented properties.

Fields of Study & Subjects

Nonlinear Dynamics and Pattern Formation Chaos control and synchronization Neural Networks and Reservoir Computing

Publication details

Journal / source
Physical Review Letters
Publisher
American Physical Society (APS)
Volume
136
Issue
6
Pages
068101–068101
Publication date
9 January 2026
Language
EN