Ivan I. Smalyukh

Topological solitons in chiral nematic liquid crystals offer an elegant route to program complex director fields and dynamic surface responses. However, controlling these structures with spatial precision and scalability remains a key challenge. Here, an electro‐templating strategy is introduced to control the relaxation of cholesteric finger loops, enabling the generation of discrete, concentric solitonic ring structures with programmable geometry. This process exploits the structural multistability stemming from the interplay between the tendency to twist at a rate of helical pitch and confinement, allowing for on‐demand formation of multi‐ring architectures governed by relaxation dynamics in response to pre‐designed voltage driving. Upon photopolymerization, the resulting polymer retains the topological configuration of the soliton and exhibits thermally induced, reversible surface modulation, forming castle‐like structures with a central pillar and tunable concentric walls. Supported by theoretical modeling, this approach offers a scalable platform for encoding highly resolved hierarchical topological features into soft materials, opening new opportunities for responsive coatings and adaptive optics.

Knotted fields in classical and quantum systems have long been recognized for their nontrivial topologies and particlelike behavior, but practical applications have been limited by the difficulty of stabilizing them. Recently, stable knotted solitonic textures—heliknotons—were discovered in chiral liquid crystals, forming adaptive crystal assemblies via elastic distortion-mediated interactions. We use machine learning to develop single-site coarse-grained potentials that accurately capture these chiral anisotropic effective interactions. The resulting potentials accurately reproduce experimentally observed heliknoton assemblies and enable simulations at length scales and timescales far beyond the range of fine-grained continuum models. This general framework is readily transferable to other topological solitons, providing a powerful route to understand, predict, and ultimately control their collective behavior and dynamics.

Nanofluidic systems couple confined ion transport to memory and computation, bridging soft, hard and hybrid platforms toward scalable ionic intelligence beyond electron-based hardware.

Time crystals spontaneously break the time translation symmetry, as recently has been frequently reported in quantum systems. Here we describe the observation of classical analogs of both 1 + 1-dimensional and 2 + 1-dimensional discrete space-time crystals in a liquid crystal system driven by a Floquet electrical signal. These classical time crystals comprise particle-like structural features and exist over a wide range of temperatures and electrical driving conditions. The phenomenon-enabling period-doubling effect in 1 + 1-dimensional discrete space-time crystals comes from their topological Majorana-like quasiparticle features, where periodic inter-transformations of co-existing topological solitons and disclinations emerge in response to external stimuli and play pivotal roles. Our discrete space-time crystals exhibit robustness against temporal perturbations and spatial defects. Our findings show that the simultaneous symmetry breaking in time and space can be a widespread occurrence in numerous open systems, not only in quantum but also in a classical soft matter context.

In cholesterics with pitch p , colliding dislocations with Burgers vectors b = p and b = − p form stable twin-like pairs, known as Lehmann clusters, which are immune against annihilation for topological reasons.

Understanding the behavior and evolution of a dynamical many-body system by analyzing patterns in their experimentally captured images is a promising method relevant for a variety of living and nonliving self-assembled systems. The arrays of moving liquid crystal skyrmions studied here are a representative example of hierarchically organized materials that exhibit complex spatiotemporal dynamics driven by multiscale processes. Joint geometric and topological data analysis (TDA) offers a powerful framework for investigating such systems by capturing the underlying structure of the data at multiple scales. In the TDA approach, we introduce the Ψ function, a robust numerical topological descriptor related to both the spatiotemporal changes in the size and shape of individual topological solitons and the emergence of regions with their different spatial organization. The geometric method based on the analysis of vector fields generated from images of skyrmion ensembles offers insights into the nonlinear physical mechanisms of the system's response to external stimuli and provides a basis for comparison with theoretical predictions. The methodology presented here is very general and can provide a characterization of system behavior both at the level of individual pattern-forming agents and as a whole, allowing one to relate the results of image data analysis to processes occurring in a physical, chemical, or biological system in the real world.

We report the modulation of molecular charge transfer in a bay-substituted perylene diimide derivative embedded in a planar distributed Bragg reflector microcavity. Angle-resolved reflectance spectra confirm the formation of upper and lower polaritons with clear Rabi splitting, indicating strong coupling between the cavity mode and molecular excitons. Using broadband transient absorption spectroscopy, we compare the ultrafast dynamics of cavity and non-cavity films. While excited-state absorption and stimulated emission pathways remain largely unchanged, kinetic modeling reveals a moderate increase in the charge transfer rate and yield under strong coupling. This enhancement is attributed to a reduction in the effective driving force via the formation of the lower polariton, placing the system deeper into the Marcus inverted regime. Our results demonstrate a promising non-chemical method leveraging cavity quantum electrodynamics to modulate charge separation processes in molecular semiconductors.