arXiv subjects

Marjolein Dijkstra

Publications and source records attributed to Marjolein Dijkstra.

At least 37 records · Page 2Linked to original sources

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↗

arXiv2025

Kinetic phase diagram for two-step nucleation in colloid-polymer mixtures

Two-step crystallization via a metastable intermediate phase is often regarded as a non-classical process that lies beyond the framework of classical nucleation theory (CNT). In this work, we investigate two-step crystallization in colloid-polymer mixtures via an intermediate liquid phase. Using CNT-based seeding simulations, we construct a kinetic phase diagram that identifies regions of phase space where the critical nucleus is either liquid or crystalline. These predictions are validated using transition path sampling simulations at nine different relevant state points. When the critical nucleus is liquid, crystallization occurs stochastically during the growth phase, whereas for a crystalline critical nucleus, the crystallization process happens pre-critically at a fixed nucleus size. We conclude that CNT-based kinetic phase diagrams are a powerful tool for understanding and predicting `non-classical' crystal nucleation mechanisms.

cond-mat.soft↗

arXiv2025

Learning Neural Free-Energy Functionals with Pair-Correlation Matching

The intrinsic Helmholtz free-energy functional, the centerpiece of classical density functional theory, is at best only known approximately for 3D systems. Here we introduce a method for learning a neuralnetwork approximation of this functional by exclusively training on a dataset of radial distribution functions, circumventing the need to sample costly heterogeneous density profiles in a wide variety of external potentials. For a supercritical Lennard-Jones system with planar symmetry, we demonstrate that the learned neural free-energy functional accurately predicts inhomogeneous density profiles under various complex external potentials obtained from simulations.

cond-mat.soft↗

arXiv2025

Disentangling the Effects of Curvature and Misorientation on the Shrinkage Behavior of Loop-Shaped Grain Boundaries

The material properties of polycrystals are strongly affected by the evolution and coarsening of their internal grain structures. Yet, studying this process is challenging due to the complex interactions within grain boundary networks. Here, we systematically investigate the shrinkage of isolated loop-shaped grain boundaries in 2D colloidal crystals. Unexpectedly, we find that shear coupling decreases with increasing grain misorientation, contrary to geometric predictions. This counterintuitive result is attributed to enhanced concurrent sliding driven by the annihilation of dislocations. Furthermore, by focusing on the evolution of the grain size, we reveal a transition in shrinkage kinetics between small and large loop sizes, offering an explanation for previously observed discrepancies in grain boundary mobility. These findings reveal a more intricate dependence of grain boundary behavior on curvature and misorientation than previously reported, offering new insights into polycrystal coarsening dynamics.

cond-mat.soft↗

arXiv2024

The bcc coating of Lennard-Jones crystal nuclei vanishes with a change of local structure detection algorithm

Since the influential work of ten Wolde, Ruiz-Montero, and Frenkel [Phys. Rev. Lett. 75, 2714 (1995)], crystal nucleation from a Lennard-Jones fluid has been regarded as a paradigmatic example of metastable crystal ordering at the surface of a critical nucleus. We apply seven commonly used local structure detection algorithms to characterize crystal nuclei obtained from transition path sampling simulations. The polymorph composition of these nuclei varies significantly depending on the algorithm used. Our results indicate that one should be very careful when characterizing the local structure near solid-solid and solid-fluid interfaces. Particles near such interfaces exhibit a local structure distinct from that of bulk fluid or bulk crystal phases. We argue that incorporating outlier detection into the local structure detection method is beneficial, leading to greater confidence in the classification results. Interestingly, the bcc coating nearly disappears when adopting a machine learning method with outlier detection.

cond-mat.soft↗

arXiv2024

Entropic magnetic interlayer coupling

Nanomagnetism concerns the engineering of magnetic interactions in heterostructures that consist of layers of magnetic and non-magnetic materials. Mostly, these interactions are dominated by the minimization of energy. Here, we propose an effective magnetic interlayer coupling that is dominated by the maximization of entropy. As an example, we consider the system that mediates the effective interactions to be square spin ice, in which case we find purely entropic interactions that are long-ranged. We argue that in the thermodynamic limit the entropic interlayer coupling gives rise to entropic torques on the magnetization direction. For small systems, the physical properties are well characterized by the mutual information between the two magnets that are coupled. Because entropic interactions become stronger for higher temperatures, our findings may benefit the development of nanomagnetic devices that require thermal stability.

cond-mat.mes-hall↗

arXiv2024

Collective Dynamics of Intelligent Active Brownian Particles with Visual Perception and Velocity Alignment in 3D: Spheres, Rods, and Worms

Many living systems, such as birds and fish, exhibit collective behaviors like flocking and swarming. Recently, an experimental system of active colloidal particles has been developed, where the motility of each particle is adjusted based on its visual detection of surrounding particles. These particles with visual-perception-dependent motility exhibit group formation and cohesion. Inspired by these behaviors, we investigate intelligent active Brownian particles (iABPs) equipped with visual perception and velocity alignment in three dimensions using computer simulations. The visual-perception-based self-steering describes the tendency of iABPs to move toward the center of mass of particles within their visual cones, while velocity alignment encourages alignment with neighboring particles. We examine how the behavior varies with the visual cone angle theta, self-propulsion speed (Peclet number Pe), and the interaction strengths of velocity alignment (Omega_a) and visual-based self-steering (Omega_v). Our findings show that spherical iABPs form clusters, worm-like clusters, milling behaviors, and dilute-gas phases, consistent with 2D studies. By reducing the simulation box size, we observe additional structures like band-like clusters and dense baitball formations. Additionally, rod-like iABPs form band-like, worm-like, radiating, and helical structures, while iABP worms exhibit streamlined and blob-like structures. Many of these patterns resemble collective behaviors in nature, such as ant milling, fish baitballs, and worm clusters. Advances in synthetic techniques could enable nanorobots with similar capabilities, offering insights into multicellular systems through active matter.

cond-mat.soft↗

arXiv2024

Inverse Design Method with Enhanced Sampling for Complex Open Crystals: Application to Novel Zeolite Self-Assembly in a Coarse-Grained Model

Optimizing the synthesis of zeolites and exploring novel frameworks offer pivotal opportunities and challenges in materials design. While inverse design proves highly effective for simpler crystals, its application to intricate structures like zeolites poses severe challenges. Here, we introduce an innovative inverse design workflow tailored to efficiently reproduce target zeolite frameworks in a binary coarse-grained model using enhanced sampling molecular dynamics simulations. This workflow integrates an evolutionary parameter optimization strategy with a variant of the seeding approach. Using this method, we successfully reproduce Z1 and SGT zeolites, and Type-I clathrates, find new optimal parameters for known phases, such as the SOD and CFI, and even discover novel frameworks, such as Z5. This is done within a simple coarse-grained model for a tetrahedra-forming component and a structure-directing agent. Our methodology not only enables the screening of synthesis protocols but also facilitates the discovery of hypothetical zeolites.

cond-mat.mtrl-sci↗

arXiv2024

Prospecting for Pluripotency in Metamaterial Design

From self-assembly and protein folding to combinatorial metamaterials, a key challenge in material design is finding the right combination of interacting building blocks that yield targeted properties. Such structures are fiendishly difficult to find; not only are they rare, but often the design space is so rough that gradients are useless and direct optimization is hopeless. Here, we design ultra rare combinatorial metamaterials capable of multiple desired deformations by introducing a two-fold strategy that avoids the drawbacks of direct optimization. We first combine convolutional neural networks with genetic algorithms to prospect for metamaterial designs with a potential for high performance. In our case, these metamaterials have a high number of spatially extended modes; they are pluripotent. Second, we exploit this library of pluripotent designs to generate metamaterials with multiple target deformations, which we finally refine by strategically placing defects. Our pluripotent, multishape metamaterials would be impossible to design through trial-and-error or standard optimization. Instead, our data-driven approach is systematic and ideally suited to tackling the large and intractable combinatorial problems that are pervasive in material science.

cond-mat.soft↗

arXiv2024

Variational Umbrella Seeding for Calculating Nucleation Barriers

In this work, we introduce Variational Umbrella Seeding, a novel technique for computing nucleation barriers. This new method, a refinement of the original seeding approach, is far less sensitive to the choice of order parameter for measuring the size of a nucleus. Consequently, it surpasses seeding in accuracy, and Umbrella Sampling in computational speed. We test the method extensively and demonstrate excellent accuracy for crystal nucleation of nearly hard spheres and of two distinct models of water: mW and TIP4P/ICE. This method can easily be extended to calculate nucleation barriers for homogeneous melting, condensation, and cavitation.

cond-mat.soft↗

arXiv2024

Inverse Design of Crystals and Quasicrystals in a Non-Additive Binary Mixture of Hard Disks

The development of new materials typically involves a process of trial and error, guided by insights from past experimental and theoretical findings. The inverse design approach for soft-matter systems has the potential to optimize specific physical parameters such as particle interactions, particle shape, or composition and packing fraction. This optimization aims to facilitate the spontaneous formation of specific target structures through self-assembly. In this study, we expand upon a recently introduced inverse design protocol for monodisperse systems to identify the required conditions and interactions for assembling crystal and quasicrystal phases within a binary mixture of two distinct species. This method utilizes an evolutionary algorithm to identify the optimal state point and interaction parameters, enabling the self-assembly of the desired structure. Additionally, we employ a convolutional neural network (CNN) that classifies different phases based on their diffraction patterns, serving as a fitness function for the desired structure. Using our protocol, we successfully inverse design two-dimensional crystalline structures, including a hexagonal lattice, and a dodecagonal quasicrystal, within a non-additive binary mixture of hard disks. Finally, we introduce a symmetry-based order parameter that leverages the encoded symmetry within the diffraction pattern. This order parameter circumvents the need for training a CNN, and is used as a fitness function to inverse design an octagonal quasicrystal.

cond-mat.soft↗

arXiv2024

Finding the Differences: Classical Nucleation Perspective on Homogeneous Melting and Freezing of Hard Spheres

By employing brute-force molecular dynamics, umbrella sampling, and seeding simulations, we investigate homogeneous nucleation during melting and freezing of hard spheres. We provide insights into these opposing phase transitions from the standpoint of classical nucleation theory. We observe that melting has both a lower driving force and a lower interfacial tension than freezing. The lower driving force arises from the vicinity of a spinodal instability in the solid and from a strain energy. The lower interfacial tension implies that the Tolman lengths associated with melting and freezing have opposite signs, a phenomenon that we interpret with Turnbull's rule. Despite these asymmetries, the nucleation rates for freezing and melting are found to be comparable.

cond-mat.soft↗

arXiv2023

Curvature directed anchoring and defect structure of colloidal smectic liquid crystals in confinement

Rod-like objects at high packing fractions can form smectic phases, where the rods break rotational and translational symmetry by forming lamellae. Smectic defects thereby include both discontinuities in the rod orientational order (disclinations), as well as in the positional order (dislocations). In this work, we use both experiments and simulations to probe how local and global geometrical frustrations affect defect formation in hard-rod smectics. We confine a particle-resolved, colloidal smectic within elliptical wells of varying size and shape for a smooth variation of the boundary curvature. We find that the rod orientation near a boundary - the anchoring - depends upon the boundary curvature, with an anchoring transition observed at a critical radius of curvature approximately twice the rod length. The anchoring controls the smectic defect structure. By analyzing local and global order parameters, and the topological charges and loops of networks made of the density maxima (rod centers) and density minima (rod ends), we quantify the amount of disclinations and dislocations formed with varying confinement geometry. More circular confinements, having only planar anchoring, promote disclinations, while more elliptical confinements, with antipodal regions of homeotropic anchoring, promote long-range smectic ordering and dislocation formation. Our findings demonstrate how geometrical constraints can control the anchoring and defect structures of liquid crystals - a principle that is applicable from molecular to colloidal length scales.

cond-mat.soft↗

arXiv2023

Depletion-induced crystallization of anisotropic triblock colloids

The intricate interplay between colloidal particle shape and precisely engineered interaction potentials has paved the way for the discovery of unprecedented crystal structures in both two and three dimensions. Here, we make use of anisotropic triblock colloidal particles composed of two distinct materials. The resulting surface charge heterogeneity can be exploited to generate regioselective depletion interactions and directional bonding. Using extensive molecular dynamics simulations and a dimensionality reduction analysis approach, we map out state diagrams for the self-assembly of such colloids as a function of their aspect ratio and packing fraction for varying depletant sizes in a quasi two-dimensional set-up. We observe the formation of a wide variety of crystal structures such as a herringbone, brick-wall, tilted brick-wall, and (tilted) ladder-like structures. More specifically, we determine the optimal parameters to enhance crystallization, and investigate the nucleation process. Additionally, we explore the potential of using crystal monolayers as templates for deposition, thereby creating complex three-dimensional structures that hold promise for future applications.

cond-mat.soft↗

arXiv2023

Brute-force nucleation rates of hard spheres compared with rare-event methods and classical nucleation theory

We determine the nucleation rates of hard spheres using brute-force molecular dynamics simulations. We overcome nucleation barriers of up to $28 k_B T$, leading to a rigorous test of nucleation rates obtained from rare-event methods and classical nucleation theory. Our brute-force nucleation rates show excellent agreement with umbrella sampling simulations by Filion et al. [J. Chem. Phys. 133, 244115 (2010)] and seeding simulations by Espinosa et al. [J. Chem. Phys. 144, 034501 (2016)].

cond-mat.soft↗

arXiv2023

A colloidal viewpoint on the finite sphere packing problem: the sausage catastrophe

It is commonly believed that the most efficient way to pack a finite number of equal-sized spheres is by arranging them tightly in a cluster. However, mathematicians have conjectured that a linear arrangement may actually result in the densest packing. Here, our combined experimental and simulation study provides a realization of the finite sphere packing problem by studying non-close-packed arrangements of colloids in a flaccid lipid vesicle. We map out a state diagram displaying linear, planar and cluster conformations of spheres, as well as bistable states which alternate between cluster-plate and plate-linear conformations due to membrane fluctuations. Finally, by systematically analyzing truncated polyhedral packings, we identify clusters of $56\leq N \leq 70$ spheres, excluding $N=57$ and 63, that pack more efficiently than linear arrangements.

cond-mat.soft↗

arXiv2023

Colloidal Hard Spheres: Triumphs, Challenges and Mysteries

The simplicity of hard spheres as a model system is deceptive. Although the particles interact solely through volume exclusion, that nevertheless suffices for a wealth of static and dynamical phenomena to emerge, making the model an important target for achieving a comprehensive understanding of matter. In addition, while real colloidal suspensions are typically governed by complex interactions, Pusey and Van Megen [Nature 320 340--342 (1986)] demonstrated that suitably tuned suspensions result in hard-sphere like behavior, thus bringing a valuable experimental complement to the celebrated theoretical model. Colloidal hard spheres are thus both a material in their own right and a platform upon which phenomena exhibited by simple materials can be explored in great detail. These various purposes enable a particular synergy between experiment, theory and computer simulation. Here we review the extensive body of work on hard spheres, ranging from their equilibrium properties such as phase behavior, interfaces and confinement to some of the non--equilibrium phenomena they exhibit such as sedimentation, glass formation and nucleation.

cond-mat.soft↗

arXiv2023

A Boltzmann generator for the isobaric-isothermal ensemble

Boltzmann generators (BGs) are now recognized as forefront generative models for sampling equilibrium states of many-body systems in the canonical ensemble, as well as for calculating the corresponding Helmholtz free energy. Furthermore, BGs can potentially provide a notable improvement in efficiency compared to conventional techniques such as molecular dynamics (MD) and Monte Carlo (MC) methods. By sampling from a clustered latent space, BGs can circumvent free-energy barriers and overcome the rare-event problem. However, one major limitation of BGs is their inability to sample across phase transitions between ordered phases. This is due to the fact that new phases may not be commensurate with the box dimensions, which remain fixed in the canonical ensemble. In this work, we present a novel BG model for the isothermal-isobaric (NPT) ensemble, which can successfully overcome this limitation. This unsupervised machine-learning model can sample equilibrium states at various pressures, as well as pressure-driven phase transitions. We demonstrate that the samples generated by this model are in good agreement with those obtained through MD simulations of two model systems. Additionally, we derive an estimate of the Gibbs free energy using samples generated by the NPT BG.

cond-mat.stat-mech↗