arXiv subjects

Marjolein Dijkstra

Publications and source records attributed to Marjolein Dijkstra.

At least 19 recordsLinked to original sources

arXiv2026

Many-body attractions do not stabilize gas-liquid phase separation in aqueous dispersions of charged colloids within the Poisson-Boltzmann framework

Attractive three-body interactions have been reported for like-charged colloids in low-salt suspensions, based on both finite-element Poisson-Boltzmann calculations and direct experimental measurements, and have been proposed as a mechanism to drive colloidal clustering. However, these Poisson-Boltzmann calculations typically neglect charge regulation and higher-order many-body effects. Here, we construct machine-learned (ML) many-body interaction potentials for charge-regulating colloids, trained on finite-element Poisson-Boltzmann calculations, to accurately capture three-body and higher-order contributions. We find that the three-body contribution to the many-body potential as obtained from Poisson-Boltzmann calculations on isolated colloid triplets is strongly attractive, consistent with previous work, whereas the four-body contribution for an equilateral pyramid configuration of four colloids is repulsive. We then construct ML many-body potentials for charged colloids using finite-element Poisson-Boltzmann calculations on clusters of 13 colloids, and find that the incorporation of higher-body interactions weakens the cohesive nature of the interactions. We identify a parameter regime exhibiting gas-liquid or gas-solid phase separation using the ML potentials in molecular dynamics simulations. However, when we include clusters of 48 colloids in the training data, the cohesion diminishes further, and molecular dynamics simulations using these potentials no longer include broad phase separation in aqueous dispersions of charged colloids. Finally, we compute the potential of mean force of pairs and triplets of colloids using primitive model simulations. We find that the resulting potentials are in good agreement with those obtained from the Poisson-Boltzmann calculations, thereby supporting the validity of the Poisson-Boltzmann approach for determining many-body interactions.

cond-mat.soft↗

arXiv2026

Synthetic paracrine signaling of colloids drives self-assembly limit cycles

Developing synthetic materials that exhibit life-like behavior, such as internally driven cycles, remains a central challenge in active matter. Here, we introduce a minimal colloidal model of chemical signaling in which particles produce diffusing signaling molecules that selectively promote or inhibit attractive interactions among neighboring particles. This bio-inspired, paracrine-like signaling mechanism generates context- and history-dependent many-body interactions that break time-reversal symmetry and drive the system far from equilibrium, leading to the spontaneous emergence of autonomous, internally sustained limit cycles in the composition of particle clusters. Using computer simulations, we map the resulting nonequilibrium phase behavior and identify distinct dynamical regimes controlled by the rates of signal production and degradation, together with the diffusion range of the signaling molecules. Among these, we find a robust oscillatory state in which particle clusters autonomously assemble in a cyclic fashion, driven entirely by internal feedback loops. Our results establish paracrine-signaling colloids as a minimal, physically realizable platform for programmable nonequilibrium materials with life-like functionality and provide a general route toward synthetic active matter with self-regulated collective dynamics.

cond-mat.soft↗

arXiv2026

How Quasicrystals Remember: Hierarchical Memory Under Cyclic Shear

Quasicrystals occupy a unique middle ground between periodically ordered crystals and disordered glasses, making them an ideal platform for examining the interplay between disorder and the emergence of mechanical memory. Using athermal quasistatic shear simulations, we show that two-dimensional dodecagonal quasicrystals encode and recover memory under cyclic driving. Above the yielding transition, the response becomes irreversible, characterized by persistent shear bands and locally transformed regions. Below yielding, cyclic shear with varying amplitudes produces a hierarchy of nested hysteresis loops in the stress-strain response characteristic of loop-return point memory. By resolving the underlying reversible plastic events, we reveal localized phason-like tile rearrangements as the elementary switching units and identify tile-switch hysterons responsible for memory in the quasicrystal. Such a microscopic identification of the fundamental switching units is considerably more challenging, and often impossible, in amorphous solids. Despite their structural diversity, these rearrangements share a compact core, sharp bistability, and an Eshelby-compatible elastic far field. In contrast, a periodic approximant of the quasicrystal lacks both the structural disorder and the bistable tile-switch rearrangements required for cyclic-shear memory, linking phason degrees of freedom to bistable hysterons.

cond-mat.soft↗

arXiv2026

Physical Neural Networks Need Nonlinearity, Amplification, and Suppression for Learning

The exponential growth in energy consumption of artificial intelligence systems has spurred interest in physical computing paradigms that exploit the relaxation of physical systems toward steady states. However, many existing physical networks are fundamentally linear and incapable of performing nonlinear operations crucial for meaningful machine learning tasks. Here we use simulations to show that nonlinearity alone is insufficient; physical learning systems must also support signal amplification and suppression to perform nontrivial computations. We present physically plausible circuit designs that incorporate these essential features, enabling effective nonlinear information processing. Our findings clarify the limitations of linear physical networks and provide guidance for developing energy-efficient physical learning architectures capable of general machine learning tasks.

cond-mat.dis-nn↗

arXiv2026

Machine-Learned Many-Body Potentials for Charged Colloids reveal Gas-Liquid Spinodal Instabilities only in the strong-coupling regime of Primitive Models

Past experimental observations of gas-liquid and gas-crystal coexistence in low-salinity suspensions of highly charged colloids have suggested the existence of like-charge attraction. Evidence for this phenomenon was also observed in primitive-model simulations of (asymmetric) electrolytes and of low-charge nanoparticle dispersions. These results from low-valency simulations have often been extrapolated to experimental parameter regimes of high colloid valency where like-charge attraction between colloids has been reported. However, direct simulations of highly charged colloids remain computationally demanding. To circumvent slow equilibration, we employ a machine-learning (ML) framework to construct ML potentials that accurately describe the effective colloid interactions. Our ML potentials enable fast simulations of dispersions and successfully reproduce the gas-liquid and gas-solid phase separation observed in primitive-model simulations at low charge numbers. Extending the ML-based simulations to higher valencies, where primitive-model simulations become prohibitively slow, also reveals like-charge attractions and gas-liquid spinodal instabilities, however only in the regime of strongly coupled electrostatic interactions and not in the weakly coupled Poisson-Boltzmann regime of the experimental observations of colloidal like-charge attractions.

cond-mat.soft↗

arXiv2026

Shaping boundaries to control and transport topological defects in colloidal nematic liquid crystals

Anisotropic rod-like particles form liquid crystalline phases with varying degrees of orientational and translational order. When confined geometrically, these phases can give rise to topological defects, which can be selected and controlled by tuning how the rods align near boundaries, known as anchoring. While anchoring in molecular liquid crystals can be controlled through surface functionalization, this approach is not easily applicable to microscale colloidal systems, which have so far been limited to planar anchoring. Here, using particle-based simulations, Landau-de Gennes theory, and experiments on colloidal rods, we demonstrate that topographical patterning of the boundary can effectively control the anchoring type and, in turn, the defect state in two-dimensional confined nematics. Building on this, we numerically predict that dynamically shape-shifting the boundaries can transform and transport topological defects.

cond-mat.soft↗

arXiv2026

Dynamic bidirectional coupling of membrane morphology and rod organization in flexible vesicles

The ordering of rod-like particles in soft, deformable containers emerges from the interplay of anisotropic interactions, geometric confinement, and boundary compliance. This competition couples internal particle organization to container morphology, producing behavior distinct from both rigid confinement and bulk systems. Such coupling is also relevant to biological contexts in which filamentous structures are confined by deformable membranes. Using a minimal model combining experiments and simulations of colloidal rods encapsulated in lipid vesicles, we show that soft confinement drives a bidirectional coupling between internal order and vesicle shape. This interplay gives rise to a phase diagram in which elongated vesicles promote nematic alignment at lower packing fractions, whereas higher packing fractions induce smectic-like ordering that reshapes vesicles into plate-like morphologies with increased bending energy. Furthermore, by controlling vesicle volume and membrane area, we demonstrate that boundary conditions enable reversible tuning of both vesicle shape and internal rod organization. These results establish a framework for dynamically controlling colloidal self-assembly in soft containers and provide insight into the organization of anisotropic building blocks in deformable, cell-like, confinements.

cond-mat.soft↗

arXiv2026

From Knots to Crystals: Machine-Learned Potentials for Self-Assembling Topological Solitons in Liquid Crystals

Knotted fields in classical and quantum systems have long been recognized for their non-trivial topologies and particle-like 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 and time scales 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.

cond-mat.soft↗

arXiv2025

Active Brownian particles in power-law viscoelastic media

Many active particles are embedded in environments that exhibit viscoelastic properties. An important class of such media lacks a single characteristic relaxation timescale when subjected to a time-dependent stress. Rather, the stress response spans a broad continuum of timescales, a behavior naturally described by a scale-free, fractal-like power-law relaxation modulus. Using a generalization of the fractional Langevin equation, we investigate an active Brownian particle embedded in a power-law viscoelastic environment with translational and rotational dynamics governed by independent fractional orders. We solve the model analytically, develop a numerical scheme to validate the theoretical predictions, and provide tools that can be used in further studies. A rich variety of diffusion regimes emerges, which modify the intermediate-time behavior of the mean squared displacement. Notably, we find that the competition between translational and rotational contributions favors a superdiffusive persistence over the standard ballistic motion, and over-stretches its characteristic timescale, fundamentally altering the standard relation between persistence and propulsion in active matter.

cond-mat.soft↗

arXiv2025

Surface Charge Relaxation Controls the Lifetime of Out-of-Equilibrium Colloidal Crystals

Interactions between charged colloidal particles are profoundly influenced by charge regulation and charge renormalization, rendering the effective potential highly sensitive to local particle density. In this work, we investigate how a dynamically evolving, density-dependent Yukawa interaction affects the stability of out-of-equilibrium colloidal structures. Motivated by a series of experiments where unexpectedly long-lived colloidal crystals have suggested the presence of like-charged attractions, we systematically explore the role of charge regulation and charge renormalization. Using Poisson-Boltzmann cell theory, we compute the effective colloidal charge and screening length as a function of packing fraction. These results are subsequently incorporated into Brownian dynamics simulations that dynamically resolve the evolving colloid charge as a function of time and local density. In the case of slow relaxation dynamics, our results show that incorporating these charging effects significantly prolongs the lifetimes of out-of-equilibrium colloidal crystals, providing an explanation for the experimental observation of long-lived crystals. These findings demonstrate that the interplay of surface charge dynamics and colloidal interactions can give rise to complex and rich nonequilibrium behavior in charged colloidal suspensions, opening new pathways for tuning colloidal stability through electrostatic feedback mechanisms.

cond-mat.soft↗

arXiv2025

Multimodal Physical Learning in Brain-Inspired Iontronic Networks

Inspired by the brain, we present a physical alternative to traditional digital neural networks -- a microfluidic network in which nodes are connected by conical, electrolyte-filled channels acting as memristive iontronic synapses. Their electrical conductance responds not only to electrical signals, but also to chemical, mechanical, and geometric changes. Leveraging this multimodal responsiveness, we develop a training algorithm where learning is achieved by altering either the channel geometry or the applied stimuli. The network performs forward passes physically via ionic relaxation, while learning combines this physical evolution with numerical gradient descent. We theoretically demonstrate that this system can perform tasks like input-output mapping and linear regression with bias, paving the way for soft, adaptive materials that compute and learn without conventional electronics.

cond-mat.soft↗

arXiv2025

Counting, Computing, and Pattern Recognition with Self-Assembling Non-Reciprocal DNA Tiles

Harnessing the intrinsic dynamics of physical systems for information processing opens new avenues for computation embodied in matter. Using simulations of a model system, we show that assemblies of DNA tiles capable of self-organizing into multiple target structures can perform basic computational tasks analogous to those of finite-state automata when equipped with programmable non-reciprocal interactions that drive controlled dynamical transitions between these structures. By establishing design rules for multifarious self-assembly while budgeting the energy input required to drive these non-equilibrium transitions, we demonstrate that these systems can execute a wide variety of tasks including counting, computing modulo functions, and recognizing specific input patterns. This framework integrates memory, sensing, and actuation within a single physical platform, paving the way toward energy-efficient physical computation embedded in materials ranging from DNA and enzymes to proteins and colloids.

cond-mat.soft↗

arXiv2025

Machine learning many-body potentials for charged colloids in primitive 1:1 electrolytes

Effective interactions between charged particles dispersed in an electrolyte are most commonly modeled using the Derjaguin-Landau-Verwey-Overbeek (DLVO) potential, where the ions in the suspension are coarse-grained out at mean-field level. However, several experiments point to shortcomings of this theory, as the distribution of ions surrounding colloids is governed by nontrivial correlations in regimes of strong Coulomb coupling (e.g. low temperature, low dielectric constant, high ion valency, high surface charge). Insight can be gained by explicitly including the ions in simulations of these colloidal suspensions, even though direct simulations of dispersions of highly charged spheres are computationally demanding. To circumvent slow equilibration, we employ a machine-learning (ML) framework to generate density-dependent ML potentials that accurately describe the effective colloid interactions at given system parameters. These ML potentials enable fast simulations and make large-scale simulations of charged colloids in suspension possible, opening the possibility for a systematic study of their phase behaviour, in particular gas-liquid and fluid-solid coexistence.

cond-mat.soft↗

arXiv2025

Ion Selectivity in Uncharged Tapered Nanoslits through Heterogeneous Water Polarization

We employ molecular dynamics simulations to investigate ion and water transport driven by an electric field through quasi-two-dimensional nanoslits with a tapered geometry. Despite the absence of surface charge on the channel walls and the associated electric double layer, we do observe a robust ion selectivity. This selectivity favors the transport of cations from base to tip when the electric field is directed from base to tip, and anions from base to tip when the field direction is reversed. Additionally, we observe a corresponding electro-osmotic water flow from base to tip, regardless of the electric field direction. Intriguingly, ion selectivity and electro-osmotic flow are conventionally associated with surface charge and electric double layers. However, in uncharged tapered nanoslits, we uncover a novel mechanism for these phenomena, where ion selectivity arises from the divergence of the heterogeneous water polarization.

cond-mat.soft↗

arXiv2025

Striking Similarities in Dynamics and Vibrations of 2D Quasicrystals and Supercooled Liquids

We investigate the interplay between structure and dynamics in two structurally distinct two-dimensional systems: a dodecagonal quasicrystal (DDQC) and a supercooled binary liquid. Using molecular dynamics simulations, we uncover striking dynamical similarities despite their fundamentally different structural organizations. Both systems exhibit pronounced dynamic heterogeneities, as evidenced by the cage-trapping plateaus in the mean-squared displacement and the pronounced peaks in the non-Gaussian parameter. In both cases, we observe a strong correlation between local structural order and dynamic propensity, indicating similar structure-dynamics relationships, albeit driven by distinct microscopic mechanisms. Despite these parallels, their vibrational properties diverge: the DDQC exhibits multiple peaks linked to phason dynamics, while the supercooled liquid displays a characteristic boson peak. Analysis of vibrational eigenmodes shows that both systems exhibit extended modes at low frequencies. At high frequencies, however, the DDQC maintains a higher density of topological defects, reflecting its quasi-long-range order. Finally, we contextualize these findings by comparing both systems to a square crystal. While the dynamics appears similar across all three systems, the vibrational and topological features clearly distinguish the DDQC and glass from the crystalline state. These results underscore a surprising universality in dynamical behavior across structurally diverse systems and provide new insights into how structural organization shapes motion in soft-matter systems.

cond-mat.soft↗

arXiv2025

Relationship between Structure and Dynamics of an Icosahedral Quasicrystal using Unsupervised Machine Learning

We present a comprehensive study of the structure, formation, and dynamics of a one-component model system that self-assembles into an icosahedral quasicrystal (IQC). Using molecular dynamics simulations combined with unsupervised machine learning techniques, we identify and characterize the unique structural motifs of IQCs, including icosahedral and dodecahedral arrangements, and quantify the evolution of local environments during the IQC formation process. Our analysis reveals that the formation of the IQC is driven by the emergence of distinct local clusters that serve as precursors to the fully developed quasicrystalline phase. Additionally, we examine the dynamics of the system across a range of temperatures, identifying transitions from vibrationally restricted motion to activated diffusion, and uncovering signatures of dynamic heterogeneity inherent to the quasicrystalline state. To directly connect structure and dynamics, we use a machine-learning-based order parameter to quantify the presence of distinct local environments across temperatures. We find that regions with high structural order, as captured by specific machine-learned classes, correlate with suppressed self-diffusion and minimal dynamical heterogeneity, consistent with phason-like motion within the IQC. In contrast, regions with lower structural order exhibit enhanced collective motion and increased dynamical heterogeneity. These results establish a quantitative framework for understanding the coupling between structural organization and dynamical processes in quasicrystals, providing new insights into the mechanisms governing IQC stability and dynamics.

cond-mat.soft↗

arXiv2025

Neuromorphic Computing with Microfluidic Memristors

Conical microfluidic channels filled with electrolytes exhibit volatile memristive behavior, offering a promising platform for energy-efficient, neuromorphic computing. Here, we integrate these iontronic channels as additional nonlinear elements in nonlinear Shinriki-inspired oscillators and demonstrate that they exhibit alternating chaotic and non-chaotic dynamics across a broad frequency range. Exploiting this behavior, we construct XOR and NAND gates by coupling three Memriki oscillators, and we further realize the full set of standard logic gates through combinations of NAND gates. Our results establish a new paradigm for iontronic computing and open avenues for scalable, low-power logical operations in microfluidic and bio-inspired systems.

cond-mat.soft↗

arXiv2025

Intelligent Soft Matter: Towards Embodied Intelligence

Intelligent soft matter stands at the intersection of materials science, physics, and cognitive science, promising to change how we design and interact with materials. This transformative field seeks to create materials that possess life-like capabilities, such as perception, learning, memory, and adaptive behavior. Unlike traditional materials, which typically perform static or predefined functions, intelligent soft matter dynamically interacts with its environment. It integrates multiple sensory inputs, retains experiences, and makes decisions to optimize its responses. Inspired by biological systems, these materials intend to leverage the inherent properties of soft matter: flexibility, self-evolving, and responsiveness to perform functions that mimic cognitive processes. By synthesizing current research trends and projecting their evolution, we present a forward-looking perspective on how intelligent soft matter could be constructed, with the aim of inspiring innovations in fields such as biomedical devices, adaptive robotics, and beyond. We highlight new pathways for integrating design of sensing, memory and action with internal low-power operations and discuss challenges for practical implementation of materials with "intelligent behavior". These approaches outline a path towards to more robust, versatile and scalable materials that can potentially act, compute, and "think" by their inherent intrinsic material behaviour beyond traditional smart technologies relying on external control.

cond-mat.soft↗