arXiv subjects

Kyogo Kawaguchi

Publications and source records attributed to Kyogo Kawaguchi.

At least 19 recordsLinked to original sources

arXiv2026

Beyond local detailed balance: Microscopic rates reshape nonequilibrium phase behavior

Local detailed balance (LDB) is a central guiding principle for modeling nonequilibrium stochastic dynamics, yet it only constrains the ratio of forward and backward transition rates and does not fix the steady state. Although the functional form of rates under the same LDB has been shown to affect steady-state correlations and transition temperatures, whether it can qualitatively reshape phase behavior in strongly interacting systems, such as the morphology and stability of phase-separated patterns, remains unclear. Here, for a two-dimensional driven lattice gas with attractive nearest-neighbor interactions, we consider hopping rates with a parameter that preserves the same LDB but tunes asymmetry along the driving force. We find that this parameter controls qualitative phase behavior: in the homogeneous phase, it reverses the sign of the structure-factor discontinuity and hence the anisotropy in long-range density correlations; in the phase-separated regime, it switches the orientation of anisotropic patterns and their long-time stability. Both effects are coherently captured by an approximate fluctuating hydrodynamic equation. The results demonstrate that, in contrast to equilibrium systems, qualitative features of nonequilibrium phases are controlled by the specific choice of dynamical rules even under the same LDB.

cond-mat.stat-mech↗

arXiv2026

Controlling Defects and Probing Dynamics in Active Nematics with Deep Reinforcement Learning

Topological defects govern much of the flow behavior and orientational order in active nematics, making their control relevant for active matter physics, smart materials, and microfluidics. Applied activity patterns can induce self-propulsion of active nematic defects, but general-purpose methods for exploiting this effect to control defects remain largely unexplored. Here we use deep reinforcement learning (RL) to perform minimum-time position control of +1/2 defects in hybrid lattice Boltzmann simulations of active nematodynamics. Spatiotemporally patterned activity, implemented as a control field in the active stress, steers defects through microchannel geometries and reveals finite-time reachable regions of defect position space. Reachability is shaped by director anisotropy, homeotropic wall anchoring, and the allowed activity patterns: local patterns steer defects in free domains but fail in junctions, whereas global patterns open otherwise inaccessible channels. In constrained geometries, the original defect may be unable to reach some goals intact, but controlled pair creation enlarges the effective reachable set by transferring control to a newly created +1/2 defect. The trained RL controllers outperform static and rule-based baselines, and controllers trained only on simple junctions can be combined without fine-tuning into a meta-controller that successfully steers defects through a larger test maze. Free energy visualizations show that guided defects write persistent, history-dependent distortions into the director field that can later be partially erased by -1/2 defects. Thus, RL-based control uncovers how confinement, anchoring, actuation geometry, and defect creation determine reachable motion in active nematics, providing a framework for other control tasks in soft and active matter.

cond-mat.soft↗

arXiv2026

Hydrodynamic origins of symmetric swimming strategies

Efficient locomotion is important for the evolution of complex life, yet the physical principles selecting specific swimming strokes often remain entangled with biological constraints. In viscous fluids, the scallop theorem constrains the temporal organization of strokes, but no analogous principle is known for their spatial structure, leaving the prevalence of symmetric gaits across diverse organisms without a physical explanation. Here we show that spatial symmetry acts as an emergent organizing principle for efficiency in viscous fluids. By analysing deformable swimmers whose strokes are not constrained to any particular symmetry class, we identify a hydrodynamic duality: symmetric and anti-symmetric strokes are dynamically equivalent, yielding identical speeds and efficiencies, which we prove are optimal among all strokes. We validate this using numerical simulations of Stokes flow, demonstrating that these symmetry rules persist even in three-dimensional body plans. Our results suggest that the prevalence of symmetric and alternating gaits in nature reflects not merely a developmental constraint, but a physical optimality principle for locomotion in viscous environments, complementing developmental and neural constraints.

physics.bio-ph↗

arXiv2026

Hermitian and non-Hermitian topology in active matter

Self-propulsion is a quintessential aspect of biological systems, which can induce nonequilibrium phenomena that have no counterparts in passive systems. Motivated by biophysical interest together with recent advances in experimental techniques, active matter has been a rapidly developing field in physics. Meanwhile, over the past few decades, topology has played a crucial role to understand certain robust properties appearing in condensed matter systems. For instance, the nontrivial topology of band structures leads to the notion of topological insulators, where one can find robust gapless edge modes protected by the bulk band topology. We here review recent progress in an interdisciplinary area of research at the intersection of these two fields. Specifically, we give brief introductions to active matter and band topology in Hermitian systems, and then explain how the notion of band topology can be extended to nonequilibrium (and thus non-Hermitian) systems including active matter. We review recent studies that have demonstrated the intimate connections between active matter and topological materials, where exotic topological phenomena that are unfeasible in passive systems have been found. A possible extension of the band topology to nonlinear systems is also briefly discussed. Active matter can thus provide an ideal playground to explore topological phenomena in qualitatively new realms beyond conservative linear systems.

cond-mat.soft↗

arXiv2025

Unifying Theories in High-Dimensional Biology: Approaches, Challenges and Opportunities

Across biological subdisciplines, the last decade has seen an explosion of high-dimensional datasets, including datasets for cells, species, immune systems, neurons and behaviour. At the ICTS workshop 'Unifying Theories in High-Dimensional Biophysics' we discussed whether this high dimensionality poses a challenge or opportunity for describing, understanding and predicting biological systems theoretically. We discussed methods, models and frameworks that can help with addressing empirical observations based on these high-dimensional datasets. We summarize the challenges and opportunities that emerged in discussions according to individual participants below.

physics.bio-ph↗

arXiv2024

A Percolation Model of Emergence: Analyzing Transformers Trained on a Formal Language

Increase in data, size, or compute can lead to sudden learning of specific capabilities by a neural network -- a phenomenon often called "emergence''. Beyond scientific understanding, establishing the causal factors underlying such emergent capabilities is crucial to enable risk regulation frameworks for AI. In this work, we seek inspiration from study of emergent properties in other fields and propose a phenomenological definition for the concept in the context of neural networks. Our definition implicates the acquisition of general structures underlying the data-generating process as a cause of sudden performance growth for specific, narrower tasks. We empirically investigate this definition by proposing an experimental system grounded in a context-sensitive formal language and find that Transformers trained to perform tasks on top of strings from this language indeed exhibit emergent capabilities. Specifically, we show that once the language's underlying grammar and context-sensitivity inducing structures are learned by the model, performance on narrower tasks suddenly begins to improve. We then analogize our network's learning dynamics with the process of percolation on a bipartite graph, establishing a formal phase transition model that predicts the shift in the point of emergence observed in our experiments when changing the data structure. Overall, our experimental and theoretical frameworks yield a step towards better defining, characterizing, and predicting emergence in neural networks.

cs.LG↗

arXiv2024

Dynamical phase transitions in single particle Brownian motion without drift

Dynamical phase transitions (DPTs) arise from qualitative changes in the long-time behavior of stochastic trajectories, often observed in systems with kinetic constraints or driven out of equilibrium. Here we demonstrate that first-order DPTs can occur even in the large deviations of a single Brownian particle without drift, but only when the system's dimensionality exceeds four. These DPTs are accompanied by temporal phase separations in the trajectories and exhibit dimension-dependent order due to the threshold behavior for bound state formation in Schrödinger operators. We also discover second-order DPTs in one-dimensional Brownian motion, characterized by universal exponents in the rate function of dynamical observables. Our results establish a novel framework linking classical DPTs to quantum phase transitions.

cond-mat.stat-mech↗

arXiv2024

Universality in the dynamical phase transitions of Brownian motion

We study the dynamical phase transitions (DPTs) appearing for a single Brownian particle without drift. We first explore how first-order DPTs in large deviations can be found even for a single Brownian particle without any force upon raising the dimension to higher than four. The DPTs accompany temporal phase separations in their dynamical paths, which we numerically confirm by fitting to scaling functions. We next investigate how second-order DPTs can appear in one-dimensional free Brownian motion by choosing the observable, which essentially captures the localization transition of the trajectories. We discuss and confirm that the DPTs predicted for high dimensions can also be found when considering many Brownian particles at lower dimensions.

cond-mat.stat-mech↗

arXiv2024

Predicting heteropolymer interactions: demixing and hypermixing of disordered protein sequences

Cells contain multiple condensates which spontaneously form due to the heterotypic interactions between their components. Although the proteins and disordered region sequences that are responsible for condensate formation have been extensively studied, the rule of interactions between the components that allow demixing, i.e., the coexistence of multiple condensates, is yet to be elucidated. Here we construct an effective theory of the interaction between heteropolymers by fitting it to the molecular dynamics simulation results obtained for more than 200 sequences sampled from the disordered regions of human proteins. We find that the sum of amino acid pair interactions across two heteropolymers predicts the Boyle temperature qualitatively well, which can be quantitatively improved by the dimer pair approximation, where we incorporate the effect of neighboring amino acids in the sequences. The improved theory, combined with the finding of a metric that captures the effective interaction strength between distinct sequences, allowed the selection of up to three disordered region sequences that demix with each other in multicomponent simulations, as well as the generation of artificial sequences that demix with a given sequence.The theory points to a generic sequence design strategy to demix or hypermix thanks to the low dimensional nature of the space of the interactions that we identify. As a consequence of the geometric arguments in the space of interactions, we find that the number of distinct sequences that can demix with each other is strongly constrained, irrespective of the choice of the coarse-grained model. Altogether, we construct a theoretical basis for methods to estimate the effective interaction between heteropolymers, which can be utilized in predicting phase separation properties as well as rules of assignment in the localization and functions of disordered proteins.

cond-mat.soft↗

arXiv2024

Experimental identification of force, velocity, and nematic order relationships in active nematic cell monolayers

Cell alignment often forms nematic order, which can lead to anomalous collective cell flow due to the so-called active force. Although it is appreciated that cell migration is driven by traction force, a quantitative evaluation of the relationships between the traction force, the nematic patterning, and the cell flow velocity is still elusive. Here we have found that cellular traction force aligns almost perfectly and is proportional in amplitude to the gradient of the nematic order tensor, not only near the topological defects but also globally. Furthermore, the flow in the monolayer was best described by adding nonlinear forces and a diffusion term derived from symmetry considerations. These nonlinear active forces enhance density instability but suppress bending instability, explaining why cell accumulation and dispersion can occur in neural progenitor cell culture while their ordering pattern is stable.

cond-mat.soft↗

arXiv2023

Activity-induced ferromagnetism in one-dimensional quantum many-body systems

We study a non-Hermitian quantum many-body model in one dimension analogous to the Vicsek model or active spin models, and investigate its quantum phase transitions. The model consists of two-component hard-core bosons with ferromagnetic interactions and activity, i.e., spin-dependent asymmetric hopping. Numerical results show the emergence of a ferromagnetic order induced by the activity, a quantum counterpart of flocking, that even survives in the absence of ferromagnetic interaction. We confirm this phenomenon by proving that activity generally increases the ground state energies of the paramagnetic states, whereas the ground state energy of the ferromagnetic state does not change. By solving the two-particle case, we find that the effective alignment is caused by avoiding the bound state formation due to the non-Hermitian skin effect in the paramagnetic state. We employ a two-site mean-field theory based on the two-particle result and qualitatively reproduce the phase diagram. We further numerically study a variant of our model with the hard-core condition relaxed, and confirm the robustness of ferromagnetic order emerging due to activity.

cond-mat.quant-gas↗

arXiv2021

Activity-induced phase transition in a quantum many-body system

A crowd of nonequilibrium entities can show phase transition behaviors that are prohibited in conventional equilibrium setups. An interesting question is whether similar activity-driven phase transitions also occur in pure quantum systems. Here we introduce a minimally simple quantum many-body model that undergoes quantum phase transitions induced by non-Hermiticity. The model is based on a classical anisotropic lattice gas model that undergoes motility-induced phase separation (MIPS), and the quantum phase diagram includes other active phases such as the flocking phase. The quantum phase transitions, which in principle can be tested in ultracold atom experiments, is also identified as the transitions of dynamical paths in the classical kinetics upon the application of biasing fields. This approach sheds light on the useful connection between classical nonequilibrium kinetics and non-Hermitian quantum physics.

cond-mat.stat-mech↗

arXiv2021

Surface wetting by kinetic control of liquid-liquid phase separation

Motivated by the observations of intracellular phase separations and the wetting of cell membranes by protein droplets, we study the nonequilibrium surface wetting by Monte Carlo simulations of a lattice gas model involving particle creation. We find that, even when complete wetting should occur in equilibrium, the fast creation of particles can hinder the surface wetting for a long time due to the bulk droplet formation. Performing molecular dynamics simulations, we show that this situation also holds in colloidal particle systems when the disorder density is sufficiently high. The results suggest an intracellular control mechanism of surface wetting by changing the speed of component synthesis.

physics.bio-ph↗

arXiv2020

Universality of active and passive phase separation in a lattice model

The motility-induced phase separation (MIPS) is the spontaneous aggregation of active particles, while equilibrium phase separation (EPS) is thermodynamically driven by attractive interactions between passive particles. Despite such difference in the microscopic mechanism, similarities between MIPS and EPS like free energy structure and critical phenomena have been discussed. Here we introduce and analyze a 2D lattice gas model that undergoes both MIPS and EPS by tuning activity and interaction parameters. Based on simulations and mean-field theory, we find that the MIPS and EPS critical points are connected through a line of nonequilibrium critical points. According to the size scaling of physical quantities and time evolution of the domain size, both the static and dynamical critical exponents seem consistent with the 2D spin-exchange Ising universality over the whole critical line. The results suggest that activity effectively enhances attractive interactions between particles and leaves intact the critical properties of phase separation.

cond-mat.stat-mech↗

arXiv2020

Chirality-driven edge flow and non-Hermitian topology in active nematic cells

Many of the biological phenomena involve collective dynamics driven by self-propelled motion and nonequilibrium force (i.e., activity) that result in features unexpected from equilibrium physics. On the other hand, biological experiments utilizing molecular motors, bacteria, and mammalian cells have served as ideal setups to probe the effect of activity in materials and compare with theory. As has been established, however, biomolecules are chiral in nature, which can lead to the chiral patterning of cells and even to the left-right symmetry breaking in our body. The general mechanism of how the dynamics of bio-matters can couple with its own inherent chirality to produce macroscopic patterns is yet to be elucidated. Here we report that cultured neural progenitor cells (NPCs), which undergo self-propelled motion with nematic cell-to-cell interactions, exhibit large scale chiral patterns when flowing out from containers made by gel. Moreover, a robust chiral cell flow is produced along the boundary when the NPCs are cultured on substrates with edges. Perturbation by actomyosin inhibitors allowed control over the chirality, resulting in the switching of the direction of the chiral patterning and boundary flow. As predicted by a hydrodynamic theory analogous to the non-Hermitian Schrodinger equation, we find an edge-localized unidirectional mode in the Fourier spectrum of the cell density, which corresponds to the topological Kelvin wave. These results establish a novel mechanism of flow that emerges from a pool of bipolar cells, and demonstrate how topological concepts from condensed matter physics can naturally arise in chiral active systems and multi-cellular phenomena.

cond-mat.soft↗

arXiv2019

Chromatin state switching in a polymer model with mark-conformation coupling

We investigate the phase transition properties of the polymer-Potts model, a chain composed of monomers with magnetic degrees of freedom, with the motivation to study the conformation and mark switching dynamics of chromatin. By the mean-field approximation, we find that the phase transition between the swollen-disordered state and the compact-ordered state is discrete; it is first-order as in the long-range Potts model, but with a significantly larger jump in magnetization (i.e., mark coherence) upon the ordering transition. The results imply how small changes in epigenetic writer concentrations can lead to a macroscopic switching of the chromatin state, suggesting a simple mechanism of discrete switching observed, for instance, in cell differentiation.

physics.bio-ph↗

arXiv2019

Universal voter model emergence in genetically labeled homeostatic tissues

Recent experiments in adult mammalian tissues have found scaling relations of the voter model in the dynamics of the genetically labeled population of stem cells. Yet, the reason for this seemingly robust appearance of the voter model remains unexplained. Here we show that the voter model kinetics is indeed a generic behavior that arises at macroscale in a linearly stable homeostatic tissue undergoing turnover. Starting from the continuum model of a multicellular system, we show that the dynamics of the labeled cell population converges to the voter model kinetics at large spatio-temporal scale of observation. We present a method to calculate the length scale and time scale of coarse-graining that is required in obtaining the effective voter model dynamics, and apply it to the growth factor competition model and the pairwise mechanical interaction model.

cond-mat.stat-mech↗

arXiv2018

Unattainability of Carnot efficiency in thermal motors: Coarse-graining and entropy production of Feynman-Smoluchowski ratchet

We revisit and analyze the thermodynamic efficiency of the Feynman-Smoluchowski (FS) ratchet, a classical thought experiment describing an autonomous heat-work converter. Starting from the full kinetics of the FS ratchet and deriving the exact forms of the hidden dissipations resulting from coarse-graining, we restate the historical controversy over its thermodynamic efficiency. The existence of hidden entropy productions implies that the standard framework of stochastic thermodynamics applied to the coarse-grained descriptions fails in capturing the dissipative feature of the system. In response to this problem, we explore an extended framework of stochastic thermodynamics to reconstruct the hidden entropy production from the coarse-grained dynamics. The approach serves as a key example of how we can systematically address the problem of thermodynamic efficiency in a multi-variable fluctuating non-equilibrium system.

cond-mat.stat-mech↗