arXiv subjects

Misaki Ozawa

Publications and source records attributed to Misaki Ozawa.

At least 19 recordsLinked to original sources

arXiv2026

Multiscale Entropies as Order Parameters for Nonequilibrium Phase Transitions

We develop a multiscale entropy framework based on the wavelet conditional renormalization group that decomposes the total Shannon entropy into contributions from different spatial scales. Generative modeling enables accurate estimation of these entropies from configurations or snapshots, even in nonequilibrium systems whose underlying probability distributions are unknown. Applications to the equilibrium $φ^4$ model and nonequilibrium active Model B+ show that multiscale entropies reveal criticality and spatial organization obscured in the total entropy. These entropies serve as scale-dependent order parameters for characterizing phase transitions and pattern formation.

cond-mat.stat-mech↗

arXiv2026

Response of a Model Glass to Athermal Quasistatic Pinching

We numerically investigate the mechanical response of amorphous solids to localized force dipoles, referred to as pinching, using athermal quasistatic simulations of model glasses with varying degrees of stability. We employ a control parameter, the imposed extension, corresponding to the increase in rest length between the two pinched particles relative to their initial separation. Increasing this extension allows us to continuously tune the system from the elastic to the plastic regime. For small extensions, the response remains elastic. In this regime, the displacement field induced by pinching is well described by linear elasticity and exhibits a long-range power-law decay consistent with dipolar forcing. Averaged responses display anisotropic, quadrupolar-like patterns, with quantitative agreement between simulations and analytical predictions. This continuum description remains valid down to particle-scale distances. As the imposed extension increases, the response becomes plastic. Pinching can then trigger either localized or system-spanning rearrangements, depending on glass stability. Well-annealed glasses exhibit localized plastic events, whereas poorly annealed systems display delocalized cascades. We introduce a method to extract the principal axis of plastic deformation and analyze the associated displacement fields and plastic activity. Overall, our results demonstrate that pinching provides a minimal local probe of amorphous solids. The resulting response, governed by both glass stability and imposed extension, offers insight into the interplay between elasticity, elementary rearrangements, and the emergence of collective plasticity.

cond-mat.soft↗

arXiv2026

Channel Flow Around an Obstacle in a Two-Dimensional Soft Particles

We numerically study confined channel flow around an obstacle using a two-dimensional soft par- ticle model, inspired by experiments performed in the same geometry. We systematically vary the polydispersity, the external driving force, and the packing fraction of the system. Our simulations capture a broad range of plastic flow phenomenologies, from highly directional, sliding-like motion characteristic of crystalline materials to more isotropic and localized rearrangements typical of amor- phous systems. We identify a threshold value of polydispersity that marks the crossover between crystalline-like and amorphous-like plasticity. In addition, we observe the existence of a critical ex- ternal force, associated with the phenomenon of yield drag, above which the system reaches steady flow and below which it remains arrested. We determine a critical packing fraction above which such yield-drag behavior emerges. Our results provide a comprehensive framework for understanding the interplay between disorder, driving, and the presence of an obstacle in channel flows.

cond-mat.soft↗

arXiv2026

Predicting Plasticity in Two-Dimensional Foam Channel Flow Around an Obstacle

We study the prediction of plastic activity in the confined channel flow of two-dimensional amor- phous soft particles around a circular obstacle. Using datasets generated with a particle-based bubble model, we formulate the prediction problem within two supervised-learning frameworks: re- gression of the non-affine displacement and binary classification of neighbor change events. A key technical challenge is that the obstacle and the confining walls explicitly break translational and rotational symmetries. We address this issue by introducing additional structural descriptors that encode the positions of particles relative to these boundaries. Starting from simple linear models, we systematically increase the complexity of the learning framework by considering a logarithmic transformation of the target variable, the incorporation of particle-size information, the addition of symmetry-breaking obstacle and wall descriptors, the coarse-graining of local structural descrip- tors, and nonlinear neural-network models. We find that the obstacle and wall descriptors provide the largest improvement in predictive performance. Nevertheless, the models considered here cap- ture mainly the overall localization of plastic activity near the obstacle and do not fully reproduce its detailed heterogeneous pattern in individual configurations. A perturbation analysis indicates that this heterogeneity is robustly encoded in the initial structure, suggesting that further progress requires more expressive structural representations and machine-learning architectures.

cond-mat.soft↗

arXiv2026

Computational Methods toward Ultrastable Glasses

Ultrastable glasses, amorphous solids with exceptionally low-energy states and enhanced kinetic, thermodynamic, and mechanical stability, have long been a subject of intense experimental interest. Over the past decade, their computational realization has emerged as a major goal in condensed matter physics, as numerical methods can exploit unphysical moves to access deeply supercooled and nonequilibrium glassy states far beyond the reach of conventional cooling protocols, thereby providing key insights into the nature of the glass transition and amorphous states and enabling the design of mechanically robust glassy materials. In this review, we outline the key steps underlying the most effective algorithms developed across the field. For each approach, we discuss its efficiency, limitations, and physical interpretation. We finally present a comparative analysis of the stability achieved across these methods, with the aim of equipping both newcomers and experts with an intuitive and comprehensive understanding of the field's current state and the opportunities it presents.

cond-mat.dis-nn↗

arXiv2026

Theory of collective learning in populations of adaptive agents

We investigate homogeneous populations of smart active agents that exchange information with their neighbors to perform a decentralized learning process aimed at achieving a prescribed macroscopic state. Such agents may, for example, represent simple microrobots. The exchanged information comprises tunable parameters governing the agent dynamics, referred to as the individual policy, together with an internal memory encoding previously visited states. This memory is used to evaluate a reward that quantifies the success of a policy to achieve the prescribed state. We extend the kinetic-theory description of collective learning in spatially homogeneous systems [Phys. Rev. Lett. 134, 248302 (2025)] and derive formal evolution equations for the distribution of policies across the population. A central outcome of our theory is the emergence of an effective reward function that fully determines the evolution of the policy distribution and encapsulates the microscopic details of the agents physical and memory dynamics. We obtain closed equations for the policy mean and variance which admit explicit time-dependent solutions under the assumption of Gaussian-distributed memories and polices. To illustrate the framework, we present a series of minimal microscopic models, considering both perfect and partial separation of physical, memory and policy exchange time scales, as well as models with one- and two-dimensional policies. The obtained theoretical results compare well with agent-based numerical simulations. The theory captures key aspects of collective learning, including the influence of population diversity and reward fluctuations on learning performance. Finally, we discuss potential applications to swarm robotics and machine learning, and highlight connections with classical models of biological evolution, including the Replicator equation and the Moran model.

cond-mat.stat-mech↗

arXiv2026

The limits of interpretability in multiple linear regression

Interpreting machine-learning models has attracted increasing attention, particularly in the physical sciences, where one often seeks to understand the underlying mechanisms rather than merely make predictions. Multiple linear regression is often regarded as an interpretable alternative to more complex models, such as deep neural networks, because its predictions are expressed as explicit weighted sums of input features. However, when input features are strongly correlated, namely in the presence of multicollinearity, the learned weights can exhibit large dataset-to-dataset fluctuations and oscillatory behavior across physically similar features, making their interpretation difficult or even impossible. Although the instability of the weights under multicollinearity is well known in statistics, its consequences for physical interpretation, in particular its connection to oscillatory weights across physically similar features, have not been systematically clarified. Here, we theoretically discuss the mechanism behind this loss of interpretability by analyzing the eigenmodes of the feature correlation matrix. We show that small-eigenvalue modes associated with multicollinearity amplify fluctuations in the weights and generate oscillatory patterns that do not necessarily reflect meaningful contributions. We test this theoretical picture numerically on physics datasets and show that Ridge regularization suppresses these unstable modes, although the resulting weights must still be interpreted with caution. We further confirm the generality of our findings beyond physics by analyzing a diverse collection of publicly available datasets. Our results clarify why, in the presence of multicollinearity, physical interpretation can remain difficult even for linear regression models.

cond-mat.dis-nn↗

arXiv2026

Entropy of Liquids and Glasses from Recurring Structural Patterns

We compute the low-temperature configurational entropy of a two-dimensional supercooled liquid. Our method, based on a higher-dimensional version of the Grassberger--Procaccia algorithm, can be implemented in a manner that is entirely agnostic with respect to both the dynamics and the theoretical framework, as any genuine notion of order should be. In this construction, entropy is obtained as the decay rate of recurrent structural patterns with increasing patch size, directly linking entropy reduction to the growing persistence of amorphous order. Because the method requires only particle positions, without any knowledge of the interaction potential or even of the particle sizes, it can be applied directly to both equilibrium and nonequilibrium aging configurations. The resulting configurational entropy, together with the higher-order Rényi complexities, agree quantitatively with values obtained from conventional definitions. Remarkably, the entropies measured during aging coincide with their equilibrium counterparts when compared at the same inherent-structure energy.

cond-mat.stat-mech↗

arXiv2026

On the anomalous elasticity in the mechanical response of amorphous solids

The response of amorphous solids to a mechanical perturbation consists in an elastic and a plastic deformation. The latter is mediated by localized irreversible rearrangements associated with Eshelby-like quadrupolar singularities in the displacement field. It has recently been argued that a density of such singularities leads to an anomalous elastic behavior taking the form of screening effects, which goes beyond classical elastic predictions. Here, we reexamine this scenario using general theoretical arguments and a description in terms of an elasto-plastic model, which we compare with atomistic simulations of the canonical Eshelby inclusion geometry. We discuss the conditions under which a finite, i.e., nonvanishing, density of quadrupolar events is created by an imposed perturbation. We argue that, except when the perturbation is macroscopic, there are many situations in which the density of quadrupolar defects is zero in the thermodynamic limit. In these cases, we find that plastically active quadrupoles emerge in a region whose size generically scales as the spatial extent $\ell$ of the mechanical perturbation. This mechanism leads to anomalous elasticity on a scale $\ell$ close to the perturbation and to conventional elasticity beyond. The simulations of the elasto-plastic model reproduce the emergence of plastic quadrupoles in a region set by $\ell$ and the associated renormalization of the effective shear modulus, but they do not exhibit the dipole-screening signatures reported in atomistic and experimental studies. Our analysis delineates the scale-dependent breakdown of long-wavelength elasticity in amorphous materials and suggests directions for incorporating anomalous screening into mesoscopic modeling frameworks.

cond-mat.soft↗

arXiv2026

Interpretability of linear regression models of glassy dynamics

Data-driven models can accurately describe and predict the dynamical properties of glass-forming liquids from structural data. Accurate predictions, however, do not guarantee an understanding of the underlying physical phenomena and the key factors that control them. In this paper, we illustrate the merits and limitations of linear regression models of glassy dynamics built on high-dimensional structural descriptors. By analyzing data for a two-dimensional glass model, we show that several descriptors commonly used in glass-transition studies display multicollinearity, which hinders the interpretability of linear models. Ridge regression suppresses some of the shortcomings of multicollinearity, but its solutions are not concise enough to be physically interpretable. Only by using dimensional reduction techniques we do eventually obtain linear models that strike a balance between prediction accuracy and interpretability. Our analysis points to a key role of local packing and composition fluctuations in the glass model under study.

cond-mat.stat-mech↗

arXiv2025

Recent Advances in Metallic Glasses

This paper reviews recent advances in the field of metallic glasses, focusing on the development of novel experimental techniques and in silico models. We discuss progress in experimental characterization, additive manufacturing, multiscale modeling approaches, and the growing role of machine learning in understanding and designing these complex materials. On the experimental side, we highlight measurements of thermophysical properties of supercooled liquids via fast chip calorimetry and enhancements in mechanical properties through rejuvenation treatments. This work underscores the crucial role of short-range order and medium-range order in controlling metallic glass mechanical properties. Recent progress in structural probes allows in situ observations of deformation mechanisms, positioning the field well to further advance our understanding of mechanical properties. Additive manufacturing of metallic glasses is discussed as one encouraging new manufacturing route for metallic glasses. We examine laser powder-bed fusion process physics and the central trade-off between amorphicity and densification, including heat affected zone devitrification and defects formation, together with emerging mitigation strategies and applications. On the theoretical and simulation side, we review advances in nanoscale, mesoscale, and continuum modeling of metallic glasses that have led to promising approaches by which multiscale schemes can incorporate data sourced from atomic-scale simulations. These efforts have helped to elucidate the connection between the glass structure and mechanical and rheological responses. We also cover the development of machine learning interatomic potentials for metallic glasses, along with machine learning driven prediction of glass forming ability and inverse design methods. Finally, challenges and directions for future research are presented and discussed.

cond-mat.mtrl-sci↗

arXiv2025

Rényi complexity in mean-field disordered systems

Configurational entropy, or complexity, plays a critical role in characterizing disordered systems such as glasses, yet its measurement often requires significant computational resources. Recently, Rényi entropy, a one-parameter generalization of Shannon entropy, has gained attention across various fields of physics due to its simpler functional form, making it more practical for measurements. In this paper, we compute the Rényi version of complexity for prototypical mean-field disordered models, including the random energy model, its generalization, referred to as the random free energy model, and the $p$-spin spherical model. We first demonstrate that the Rényi complexity with index $m$ is related to the free energy difference for a generalized annealed Franz-Parisi potential with $m$ clones. Detailed calculations show that for models having one-step replica symmetry breaking (RSB), the Rényi complexity vanishes at the Kauzmann transition temperature $T_K$, irrespective of $m>1$, while RSB solutions are required even in the liquid phase. This study strengthens the link between Rényi entropy and the physics of disordered systems and provides theoretical insights for its practical measurements.

cond-mat.dis-nn↗

arXiv2025

Athermal creep deformation of ultrastable amorphous solids

We numerically investigate the athermal creep deformation of amorphous materials having a wide range of stability. The imposed shear stress serves as the control parameter, allowing us to examine the time-dependent transient response through both the macroscopic strain and microscopic observables. Least stable samples exhibit monotonicity in the transient strain rate versus time, while more stable samples display a pronounced non-monotonic S-shaped curve, corresponding to failure by sharp shear band formation. We identify a diverging timescale associated with the fluidization process and extract the corresponding critical exponents. Our results are compared with predictions from existing scaling theories relevant to soft matter systems. The numerical findings for stable, brittle-like materials represent a challenge for theoretical descriptions. We monitor the microscopic initiation of shear bands during creep responses. Our study encompasses creep deformation across a variety of materials ranging from ductile soft matter to brittle metallic and oxide glasses, all within the same numerical framework.

cond-mat.soft↗

arXiv2025

Numerical investigation of the equilibrium Kauzmann transition in a two-dimensional atomistic glass

Dense liquids gradually transform into non-equilibrium amorphous solids as they pass through the experimental glass transition. Experimentally, ergodicity is lost because measurements are conducted within a finite time window. More than seventy years ago, Kauzmann posed a fundamental question: If experiments could run indefinitely, would there exist a critical temperature at which an ergodicity-breaking phase transition occurs? Random first-order transitions represent the modern theoretical framework for this idea, rigorously established in the mean-field limit of high-dimensional atomistic systems and several idealized physical models. However, achieving theoretical understanding in finite dimensions is challenging, while experimental and numerical limitations on accessible timescales hinder direct observation of the putative Kauzmann transition. Here, we overcome this longstanding barrier by developing a computational strategy that combines three advanced Monte Carlo methods to access the equilibrium thermodynamic properties of a two-dimensional atomistic glass-former down to zero temperature across a range of system sizes. This enables us to directly measure thermodynamic and structural observables that provide unambiguous evidence that the system undergoes a Kauzmann transition at a temperature that vanishes in the thermodynamic limit. This transition is towards an ideal glass state characterized by a complex energy landscape with a hierarchical organization of low-lying states. Our results are the first demonstration that computer simulations can fully probe the statistical mechanics of the bulk transition to a non-ergodic glass state. We anticipate that our study will serve as a foundation for future simulation work on larger systems, three-dimensional materials, and more complex glass-forming models to fully elucidate the nature of the glass state of matter.

cond-mat.soft↗

arXiv2025

Kinetic theory of decentralized learning for smart active matter

Smart active matter has the ability to control its motion guided by individual policies to achieve collective goals. We introduce a theoretical framework to study a decentralized learning process in which agents can locally exchange policies to adapt their behavior and maximize a predefined reward function. We use our formalism to derive explicit hydrodynamic equations for the policy dynamics. We apply the theory to two different microscopic models where policies correspond either to fixed parameters similar to evolutionary dynamics, or to state-dependent controllers known from the field of robotics. We find good agreement between theoretical predictions and agent-based simulations. By deriving fundamental control parameters and uncertainty relations, our work lays the foundations for a statistical physics analysis of decentralized learning.

cond-mat.stat-mech↗

arXiv2024

Selecting Relevant Structural Features for Glassy Dynamics by Information Imbalance

We investigate numerically the identification of relevant structural features that contribute to the dynamical heterogeneity in a model glass-forming liquid. By employing the recently proposed information imbalance technique, we select these features from a range of physically motivated descriptors. This selection process is performed in a supervised manner (using both dynamical and structural data) and an unsupervised manner (using only structural data). We then apply the selected features to predict future dynamics using a machine learning technique. Finally, we discuss the potential applications of this approach in identifying the dominant mechanisms governing the glassy slow dynamics.

cond-mat.soft↗

arXiv2024

Perspective on Physical Interpretations of Rényi Entropy in Statistical Mechanics

Rényi entropy is a one-parameter generalization of Shannon entropy, which has been used in various fields of physics. Despite its wide applicability, the physical interpretations of the Rényi entropy are not widely known. In this paper, we discuss some basic properties of the Rényi entropy relevant to physics, in particular statistical mechanics, and its physical interpretations using free energy, replicas, work, and large deviation.

cond-mat.stat-mech↗

arXiv2023

Creating equilibrium glassy states via random particle bonding

Creating amorphous solid states by randomly bonding an ensemble of dense liquid monomers is a common procedure which is applied to create a variety of materials such as epoxy resins, colloidal gels, and vitrimers. The properties of the resulting solid do, however, {\it a priori} strongly depend on the preparation history. This can lead to substantial aging of the material, i.e., properties such as mechanical moduli and transport coefficients depend on the time elapsed since solidification, which can lead to a slow degradation of the material in technological applications. It is therefore important to understand under which conditions random monomer bonding can lead to stable solid states, i.e., long-lived metastable states whose properties do not change over time. In this work, we present a theoretical and computational analysis of this problem, and introduce a random bonding procedure that guarantees the proper equilibration of the resulting amorphous states. Our procedure also provides a new route to investigate the fundamental properties of glassy energy landscapes by producing translationally-invariant ultrastable glassy states of simple particle models.

cond-mat.soft↗