arXiv subjects

Chengjie Luo

Publications and source records attributed to Chengjie Luo.

At least 19 recordsLinked to original sources

arXiv2026

Coexistence of patterned phases in chemically active multicomponent mixtures

Chemically active mixtures exhibit complex patterns that emerge from the interplay of physical interactions and reactions among components. Individually, these two processes are well-understood: Physical interactions can give rise to phase separation, whereas reactions can form reaction-diffusion patterns. To understand the combination of both processes, we identify a Lyapunov functional for a class of chemical reactions. By minimizing this functional, we identify a generalized Gibbs phase rule that governs the number of coexisting patterns, and we demonstrate that complex patterns can be created by the modular combination of independent phases. Our theory unveils complex stationary patterns in chemically active mixtures and provides a framework for analyzing more complex systems.

cond-mat.soft↗

arXiv2025

Self-propulsion via non-transitive phase coexistence in chemically active mixtures

Phase separation drives the formation of biomolecular condensates in cells, which comprise many components and sometimes possess multiple phases. The equilibrium physics of phase separation is well understood, but many components in condensates undergo active reactions. We demonstrate that such reactions affect phase separation by altering the chemical potential balance and by introducing an osmotic pressure difference at interfaces. However, the system does not permit a pseudo-pressure balance, and bulk compositions depend on which phases are in contact. Moreover, phase coexistence is no longer transitive, which enables self-propelled phases and more complex dynamics.

cond-mat.soft↗

arXiv2025

Chemically active droplets in crowded environments

Biomolecular condensates are essential for cellular organization and result from phase separation in systems far from thermodynamic equilibrium. Among various models, chemically active droplets play a significant role, consisting of proteins that switch between attractive and repulsive states via nonequilibrium chemical reactions. While field-based simulations have provided insights into their behavior, these coarse-grained approaches fail to capture molecular-scale effects, particularly in crowded cellular environments. Macromolecular crowding, a key feature of intracellular organization, strongly influences molecular transport within condensates, yet its quantitative impact remains underexplored. This study investigates the interplay between chemically active droplets and crowders by using particle-based models, that provide molecular insight, and a field-based model, that complements this picture. Surprisingly, crowding reduces droplet size while expanding the overall dense phase volume, challenging equilibrium-based expectations. This effect arises from the interplay between depletion interactions, diffusion hindrance, and nonequilibrium particle fluxes. Our findings provide a step towards a more comprehensive understanding of chemically active droplets in complex, realistic cellular environments.

cond-mat.soft↗

arXiv2024

Condensate Size Control by Net Charge

Biomolecular condensates are complex droplets comprising diverse molecules that interact using various mechanisms. Condensation is often driven by short-ranged attraction, but net charges can also mediate long-ranged repulsion. Using molecular dynamics simulations and an equilibrium field theory, we show that such opposing interactions can suppress coarsening so that many droplets of equal size coexist at equilibrium. This size control depends strongly on the charge asymmetry between constituents, while the strength of the short-ranged attractions has a weak influence. Essentially, droplets expel ions, so they cannot screen electrostatics effectively, implying droplets acquire a net charge and cannot grow indefinitely. Our work reveals how electrostatic effects control droplet size, which is relevant for understanding biomolecular condensates and creating synthetic patterns in chemical engineering.

cond-mat.soft↗

arXiv2024

Generic multicomponent mixtures are multistable

Liquid mixtures of many interacting components often exhibit numerous coexisting types of droplets. An exciting example is the cytosol of biological cells, where diverse droplets, called condensates, are essential for cellular function. However, how much their formation is constrained by thermodynamics is currently unclear. Linear stability analysis predicts that homogeneous mixtures become more robust to fluctuations as the number of components increases, suggesting that droplets do not form easily in multicomponent mixtures. In contrast, we show through numerical simulations and analytical scaling laws that the number of coexisting phases typically increases with the number of components in equilibrium. The combination of both results suggests that generic multicomponent mixtures can maintain many metastable states with various droplets, generalizing the nucleation-and-growth regime of binary mixtures. Our theory also indicates why cells exhibit much fewer condensates than components and how they could exploit multistability to independently form various condensates.

cond-mat.soft↗

arXiv2024

Beyond Pairwise: Higher-order physical interactions affect phase separation in multi-component liquids

Phase separation, crucial for spatially segregating biomolecules in cells, is well-understood in the simple case of a few components with pairwise interactions. Yet, biological cells challenge the simple picture in at least two ways: First, biomolecules, like proteins and nucleic acids, exhibit complex, higher-order interactions, where a single molecule may interact with multiple others simultaneously. Second, cells comprise a myriad of different components that form various droplets. Such multicomponent phase separation has been studied in the simple case of pairwise interactions, but an analysis of higher-order interactions is lacking. We propose such a theory and study the corresponding phase diagrams numerically. We find that interactions between three components are similar to pairwise interactions, whereas composition-dependent higher-order interactions between two components can oppose phase separation. This surprising result can only be revealed from the equilibrium phase diagrams, implying that the often-used stability analysis of homogeneous states is inadequate to study these systems. We thus show that higher-order interactions could play a crucial role in forming droplets in cells, and their manipulation could offer novel approaches to controlling multicomponent phase separation.

cond-mat.soft↗

arXiv2024

Classifying the age of a glass based on structural properties: A machine learning approach

It is well established that physical aging of amorphous solids is governed by a marked change in dynamical properties as the material becomes older. Conversely, structural properties such as the radial distribution function exhibit only a very weak age dependence, usually deemed negligible with respect to the numerical noise. Here we demonstrate that the extremely weak age-dependent changes in structure are in fact sufficient to reliably assess the age of a glass with the support of machine learning. We employ a supervised learning method to predict the age of a glass based on the system's instantaneous radial distribution function. Specifically, we train a multilayer perceptron for a model glassformer quenched to different temperatures, and find that this neural network can accurately classify the age of our system across at least four orders of magnitude in time. Our analysis also reveals which structural features encode the most useful information. Overall, this work shows that through the aid of machine learning, a simple structure-dynamics link can indeed be established for physically aged glasses.

cond-mat.soft↗

arXiv2023

Theory of Elastic Microphase Separation

Elastic microphase separation refers to equilibrium patterns that form by phase separation in elastic gels. Recent experiments revealed a continuous phase transition from the homogeneous phase to a regularly patterned phase, whose period decreased for stiffer systems. We here propose a model that captures these observations. The model combines a continuous field of the elastic component to describe phase separation with nonlocal elasticity theory to capture the gel's microstructure. Analytical approximations unveil that the pattern period is determined by the geometric mean between the elasto-capillary length and a microscopic length scale of the gel. Our theory highlights the importance of nonlocal elasticity in soft matter systems, reveals the mechanism of elastic microphase separation, and will improve the engineering of such systems.

cond-mat.soft↗

arXiv2023

Influence of physical interactions on spatiotemporal patterns

Spatiotemporal patterns are often modeled using reaction-diffusion equations, which combine complex reactions between constituents with ideal diffusive motion. Such descriptions neglect physical interactions between constituents, which might affect resulting patterns. To overcome this, we study how physical interactions affect cyclic dominant reactions, like the seminal rock-paper-scissors game, which exhibits spiral waves for ideal diffusion. Generalizing diffusion to incorporate physical interactions, we find that weak interactions change the length- and time-scales of spiral waves, consistent with a mapping to the complex Ginzburg-Landau equation. In contrast, strong repulsive interactions typically generate oscillating lattices, and strong attraction leads to an interplay of phase separation and chemical oscillations, like droplets co-locating with cores of spiral waves. Our work suggests that physical interactions are relevant for forming spatiotemporal patterns in nature, and it might shed light on how biodiversity is maintained in ecological settings.

physics.bio-ph↗

arXiv2023

Dead or alive: Distinguishing active from passive particles using supervised learning

A longstanding open question in the field of dense disordered matter is how precisely structure and dynamics are related to each other. With the advent of machine learning, it has become possible to agnostically predict the dynamic propensity of a particle in a dense liquid based on its local structural environment. Thus far, however, these machine-learning studies have focused almost exclusively on simple liquids composed of passive particles. Here we consider a mixture of both passive and active (i.e.\ self-propelled) Brownian particles, with the aim to identify the active particles from minimal local structural information. We compare a state-of-the-art machine learning approach for passive systems with a new method we develop based on Voronoi tessellation. Both methods accurately identify the active particles based on their structural properties at high activity and low concentrations of active particles. Our Voronoi method is, however, substantially faster to train and deploy because it requires fewer, and easy to compute, input features. Notably, both become ineffective when the activity is low, suggesting a fundamentally different structural signature for dynamic propensity and non-equilibrium activity. Ultimately, these efforts might also find relevance in the context of biological active glasses such as confluent cell layers, where subtle changes in the microstructure can hint at pathological changes in cell dynamics.

cond-mat.soft↗

arXiv2023

Emergent structural correlations in dense liquids

The complete quantitative description of the structure of dense and supercooled liquids remains a notoriously difficult problem in statistical physics. Most studies to date focus solely on two-body structural correlations, and only a handful of papers have sought to consider additional three-body correlations. Here, we go beyond the state of the art by extracting many-body static structure factors from molecular dynamics simulations and by deriving accurate approximations up to the six-body structure factor via density functional theory. We find that supercooling manifestly increases four-body correlations, akin to the two- and three-body case. However, at small wave numbers, we observe that the four-point structure of a liquid drastically changes upon supercooling, both qualitatively and quantitatively, which is not the case in two-point structural correlations. This indicates that theories of the structure or dynamics of dense liquids should incorporate many-body correlations beyond the two-particle level to fully capture their intricate behaviour.

cond-mat.stat-mech↗

arXiv2023

Physical interactions promote Turing patterns

Turing's mechanism is often invoked to explain periodic patterns in nature, although direct experimental support is scarce. Turing patterns form in reaction-diffusion systems when the activating species diffuse much slower than the inhibiting species, and the involved reactions are highly non-linear. Such reactions can originate from co-operativity, whose physical interactions should also affect diffusion. We here take direct interactions into account and show that they strongly affect Turing patterns. We find that weak repulsion between the activator and inhibitor can substantially lower the required differential diffusivity and reaction non-linearity. In contrast, strong interactions can induce phase separation, but the resulting length scale is still typically governed by the fundamental reaction-diffusion length scale. Taken together, our theory connects traditional Turing patterns with chemically active phase separation, thus describing a wider range of systems. Moreover, we demonstrate that even weak interactions affect patterns substantially, so they should be incorporated when modeling realistic systems.

physics.bio-ph↗

arXiv2022

Many-body correlations are non-negligible in both fragile and strong glassformers

It is widely believed that the emergence of slow glassy dynamics is encoded in a material's microstructure. First-principles theory [mode-coupling theory (MCT)] is able to predict the dramatic slowdown of the dynamics from only static two-point correlations as input, yet it cannot capture all of the observed dynamical behavior. Here we go beyond two-point spatial correlation functions by extending MCT systematically to include higher-order static and dynamic correlations. We demonstrate that only adding the static triplet direct correlations already qualitatively changes the predicted glass-transition diagram of binary hard spheres and silica. Moreover, we find a non-trivial competition between static triplet correlations that work to stabilize the glass state, and dynamic higher-order correlations which destabilize it for both materials. We conclude that the conventionally neglected static triplet direct correlations as well as higher-order dynamic correlations are in fact non-negligible in both fragile and strong glassformers.

cond-mat.soft↗

arXiv2022

Dynamical Susceptibilities Near Ideal Glass Transitions

Building on the recently derived inhomogeneous mode-coupling theory, we extend the generalised mode-coupling theory of supercooled liquids to inhomogeneous environments. This provides a first-principles-based, systematic and rigorous way of deriving high-point dynamical susceptibilities from variations of the many-body dynamic structure factors with respect to their conjugate field. This new framework allows for a novel and fully microscopic possibility to probe for collective relaxation mechanisms in supercooled liquids near the mode-coupling glass transition.

cond-mat.stat-mech↗

arXiv2021

Generalized Mode-Coupling Theory for Mixtures of Brownian Particles

Generalized mode-coupling theory (GMCT) has recently emerged as a promising first-principles theory to study the poorly understood dynamics of glass-forming materials. Formulated as a hierarchical extension of standard mode-coupling theory (MCT), it is able to systematically improve its predictions by including the exact dynamics of higher-order correlation functions into its hierarchy. However, in contrast to Newtonian dynamics, a fully generalized version of the theory based on Brownian dynamics is still lacking. To close this gap, we provide a detailed derivation of GMCT for colloidal mixtures obeying a many-body Smoluchowski equation. We demonstrate that a hierarchy of coupled equations can again be established and show that these, consistent with standard MCT, are identical to the ones obtained from Newtonian GMCT when taking the overdamped limit. Consequently, the non-trivial similarity between Brownian and Newtonian MCT is maintained for our newly developed multi-component GMCT. As a proof of principle, we also solve the generalized mode-coupling equations for the binary Kob-Andersen Lennard-Jones mixture undergoing Brownian dynamics, and confirm the improved predictive power of the theory upon using more levels of the GMCT hierarchy of equations.

cond-mat.soft↗

arXiv2021

Glassy dynamics of sticky hard spheres beyond the mode-coupling regime

Sticky hard spheres, i.e., hard particles decorated with a short-ranged attractive interaction potential, constitute a relatively simple model with highly non-trivial glassy dynamics. The mode-coupling theory of the glass transition (MCT) offers a qualitative account of the complex reentrant dynamics of sticky hard spheres, but the predicted glass transition point is notoriously underestimated. Here we apply an improved first-principles-based theory, referred to as generalized mode-coupling theory (GMCT), to sticky hard spheres. This theoretical framework seeks to go beyond MCT by hierarchically expanding the dynamics in higher-order density correlation functions -- an approach that may become exact if sufficiently many correlations are taken into account. We predict the phase diagrams from the first few levels of the GMCT hierarchy and the dynamics-related critical exponents, all of which are much closer to the empirical observations than MCT. Notably, the prominent reentrant glassy dynamics, the glass-glass transition, and the higher-order bifurcation singularity classes ($A_3$ and $A_4$) of sticky hard spheres are found to be preserved within GMCT at arbitrary order. Moreover, we demonstrate that when the hierarchical order of GMCT increases, the effect of the short-ranged attractive interactions becomes more evident in the dynamics. This implies that GMCT is more sensitive to subtle microstructural differences than MCT, and that the framework provides a promising first-principles approach to systematically go beyond the MCT regime.

cond-mat.soft↗

arXiv2021

Tagged-particle motion of Percus-Yevick hard spheres from first principles

We develop a first-principles-based generalized mode-coupling theory (GMCT) for the tagged-particle motion of glassy systems. This theory establishes a hierarchy of coupled integro-differential equations for self-multi-point density correlation functions, which can formally be extended up to infinite order. We use our GMCT framework to calculate the self-nonergodicity parameters and the self-intermediate scattering function for the Percus-Yevick hard sphere system, based on the first few levels of the GMCT hierarchy. We also test the scaling laws in the $α$- and $β$-relaxation regimes near the glass-transition singularity. Furthermore, we study the mean-square displacement and the Stoke-Einstein relation in the supercooled regime. We find that qualitatively our GMCT results share many similarities with the well-established predictions from standard mode-coupling theory, but the quantitative results change, and typically improve, by increasing the GMCT closure level. However, we also demonstrate on general theoretical grounds that the current GMCT framework is unable to account for violation of the Stokes-Einstein relation, underlining the need for further improvements in the first-principles description of glassy dynamics.

cond-mat.soft↗

arXiv2021

Multi-component generalized mode-coupling theory: Predicting dynamics from structure in glassy mixtures

The emergence of glassy dynamics and the glass transition in dense disordered systems is still not fully understood theoretically. Mode-coupling theory (MCT) has shown to be effective in describing some of the non-trivial features of glass formation, but it cannot explain the full glassy phenomenology due to the strong approximations on which it is based. Generalized mode-coupling theory (GMCT) is a hierarchical extension of the theory, which is able to outclass MCT by carefully describing the dynamics of higher order correlations in its generalized framework. Unfortunately, the theory has so far only been developed for single component systems and as a result works poorly for highly polydisperse materials. In this paper, we solve this problem by developing GMCT for multi-component systems. We use it to predict the glassy dynamics of the binary Kob-Andersen Lennard-Jones mixture, as well as its purely repulsive Weeks-Chandler-Andersen analogue. Our results show that each additional level of the GMCT hierarchy gradually improves the predictive power of GMCT beyond its previous limit. This implies that our theory is able to harvest more information from the static correlations, thus being able to better understand the role of attraction in supercooled liquids from a first-principles perspective.

cond-mat.soft↗