arXiv subjects

Marjolein Dijkstra

Publications and source records attributed to Marjolein Dijkstra.

At least 55 records · Page 3Linked to original sources

arXiv2023

Dynamics of supercooled liquids from static averaged quantities using machine learning

We introduce a machine-learning approach to predict the complex non-Markovian dynamics of supercooled liquids from static averaged quantities. Compared to techniques based on particle propensity, our method is built upon a theoretical framework that uses as input and output system-averaged quantities, thus being easier to apply in an experimental context where particle resolved information is not available. In this work, we train a deep neural network to predict the self intermediate scattering function of binary mixtures using their static structure factor as input. While its performance is excellent for the temperature range of the training data, the model also retains some transferability in making decent predictions at temperatures lower than the ones it was trained for, or when we use it for similar systems. We also develop an evolutionary strategy that is able to construct a realistic memory function underlying the observed non-Markovian dynamics. This method lets us conclude that the memory function of supercooled liquids can be effectively parameterized as the sum of two stretched exponentials, which physically corresponds to two dominant relaxation modes.

cond-mat.dis-nn↗

arXiv2023

Coulombic Surface-Ion Interactions Induce Nonlinear and Chemistry-Specific Charging Kinetics

While important for many industrial applications, chemical reactions responsible for charging of solids in water are often poorly understood. We theoretically investigate the charging kinetics of solid-liquid interfaces, and find that the time-dependent equilibration of surface charge contains key information not only on the reaction mechanism, but also on the valency of the reacting ions. We construct a non-linear differential equation describing surface charging by combining chemical Langmuir kinetics and electrostatic Poisson-Boltzmann theory. Our results reveal a clear distinction between late-time (near-equilibrium) and short-time (far-from-equilibrium) relaxation rates, the ratio of which contains information on the charge valency and ad- or desorption mechanism of the charging process. Similarly, we find that single-ion reactions can be distinguished from two-ion reactions as the latter show an inflection point during equilibration. Interestingly, such inflection points are characteristic of autocatalytic reactions, and we conclude that the Coulombic ion-surface interaction is an autocatalytic feedback mechanism.

cond-mat.soft↗

arXiv2023

Flexible Colloidal Molecules with Directional Bonds and Controlled Flexibility

Colloidal molecules are ideal model systems for mimicking real molecules and can serve as versatile building blocks for the bottom-up self-assembly of flexible and smart materials. While most colloidal molecules are rigid objects, the development of colloidal joints has made it possible to also include conformational flexibility into colloidal molecules. However, their unrestricted range of motion does not capture the restricted motion range and bond directionality that is typical of real molecules. In this work, we create flexible colloidal molecules with an in situ controllable motion range and bond directionality by assembling spherical particles onto cubes functionalized with complementary surface-mobile DNA. We assemble colloidal molecules with different coordination number of spheres by varying the size ratio and find that they feature a constrained range of motion above a critical size ratio. Using theory and simulations, we show that the particle shape together with the multivalent bonds create an effective free-energy landscape for the motion of the sphere on the surface of the cube. We quantify the confinement of the spheres on the surface of the cube and the probability to change facet. We find that temperature can be used as an extra control parameter to switch in situ between full and constrained flexibility of these colloidal molecules. These flexible colloidal molecules with temperature switching motion range can be used to investigate the effect of directional, yet flexible bonds in determining their self-assembly and phase behavior, and may be employed as constructional units in microrobotics and novel smart materials

cond-mat.soft↗

arXiv2022

Machine Learning of Implicit Combinatorial Rules in Mechanical Metamaterials

Combinatorial problems arising in puzzles, origami, and (meta)material design have rare sets of solutions, which define complex and sharply delineated boundaries in configuration space. These boundaries are difficult to capture with conventional statistical and numerical methods. Here we show that convolutional neural networks can learn to recognize these boundaries for combinatorial mechanical metamaterials, down to finest detail, despite using heavily undersampled training sets, and can successfully generalize. This suggests that the network infers the underlying combinatorial rules from the sparse training set, opening up new possibilities for complex design of (meta)materials.

cond-mat.soft↗

arXiv2022

Pressure-sensitive ion conduction in a conical channel: optimal pressure and geometry

Using both analytic and numerical analyses of the Poisson-Nernst-Planck equations we theoretically investigate the electric conductivity of a conical channel, which in accordance with recent experiments exhibits a strong non-linear pressure dependence. This mechanosensitive diodic behavior stems from the pressure-sensitive build-up or depletion of salt in the pore. From our analytic results we find that the optimal geometry for this diodic behavior strongly depends on the flow rate, the ideal ratio of tip-to-base-radii being equal to 0.22 at zero flow. With increased flow this optimal ratio becomes smaller and simultaneously the diodic performance becomes weaker. Consequently an optimal diode is obtained at zero-flow, which is realized by applying a pressure drop that is proportional to the applied potential and to the inverse square of the tip radius thereby countering electro-osmotic flow. When the applied pressure deviates from this ideal pressure drop the diodic performance falls sharply, explaining the dramatic mechanosensitivity observed in experiments.

cond-mat.soft↗

arXiv2022

Exploiting anisotropic particle shape to electrostatically assemble colloidal molecules with high yield and purity

Hypothesis: Colloidal molecules with anisotropic shapes and interactions are powerful model systems for deciphering the behavior of real molecules and building units for creating materials with designed properties. While many strategies for their assembly have been developed, they typically yield a broad distribution or are limited to a specific type. We hypothesize that the shape and relative sizes of colloidal particles can be exploited to efficiently direct their assembly into colloidal molecules of desired valence. Experiments: We exploit electrostatic self-assembly of negatively charged spheres made from either polystyrene or silica onto positively charged hematite cubes. We thoroughly analyze the role of the shape and size ratio of particles on the cluster size and yield of colloidal molecules. Findings: Using a combination of experiments and simulations, we demonstrate that cubic particle shape is crucial to generate high yields of distinct colloidal molecules over a wide variety of size ratios. We find that electrostatic repulsion between the satellite spheres is important to leverage the templating effect of the cubes, leading the spheres to preferentially assemble on the facets rather than the edges and corners of the cube. Furthermore, we reveal that our protocol is not affected by the specific choice of the material of the colloidal particles. Finally, we show that the permanent magnetic dipole moment of the hematite cubes can be utilized to separate colloidal molecules from non-assembled satellite particles. Our simple and effective strategy might be extended to other templating particle shapes, thereby greatly expanding the library of colloidal molecules that can be achieved with high yield and purity.

cond-mat.soft↗

arXiv2022

Guiding the self-assembly of colloidal diamond

The assembly of colloidal cubic diamond is a challenging process since the shape and interaction parameters and the thermodynamic conditions where this structure is stable are elusive. The simultaneous use of shape-anisotropic particles and strong directional interactions has proven to be a successful path to exclusively nucleate this structure. Here, using molecular dynamics simulations, we explore in detail the conditions where nucleation of cubic diamond from tetrahedral building blocks is favored. In particular, we focus on the effect of depletion and DNA-mediated interactions to form and stabilize this cubic diamond crystal. We find that a particular balance between the strength and range of the depletion interactions enhances the self-assembly of stable cubic diamond, leading to a narrow region where this structure is nucleated. Moreover, we determine that stronger short-range depletion attractions may arrest the system leading to the formation of percolating diamond networks or fully disordered gel structures. Accordingly, the internal arrangements of these structures exhibit a distinct variation in terms of fractal dimension and the presence of six-membered rings that increasingly acquire internal strain as the arrest gets more pronounced. With these results we provide a clear route for the self-assembly of cubic colloidal diamond, towards the realization of crystals with superior photonic properties.

cond-mat.soft↗

arXiv2022

Machine-learning effective many-body potentials for anisotropic particles using orientation-dependent symmetry functions

Spherically-symmetric atom-centered descriptors of atomic environments have been widely used for constructing potential or free energy surfaces of atomistic and colloidal systems and to characterize local structures using machine learning techniques. However, when particle shapes are non-spherical, as in the case of rods and ellipsoids, standard spherically-symmetric structure functions alone produce imprecise descriptions of local environments. In order to account for the effects of orientation, we introduce two- and three-body orientation-dependent particle-centered descriptors for systems composed of rod-like particles. To demonstrate the suitability of the proposed functions, we use an efficient feature selection scheme and simple linear regression to construct coarse-grained many-body interaction potentials for computationally-efficient simulations of model systems consisting of colloidal particles with anisotropic shape: mixtures of colloidal rods and nonadsorbing polymer, hard rods enclosed by an elastic microgel shell, and ligand-stabilized nanorods. We validate the machine-learning (ML) effective many-body potentials based on orientation-dependent symmetry functions by using them in direct coexistence simulations to map out the phase behavior of colloidal rods and non-adsorbing polymer. We find good agreement with results obtained from simulations of the true binary mixture, demonstrating that the effective interactions are well-described by the orientation-dependent ML potentials.

cond-mat.soft↗

arXiv2022

Steering self-organisation through confinement

Self-organisation is the spontaneous emergence of spatio-temporal structures and patterns from the interaction of smaller individual units. Examples are found across many scales in very different systems and scientific disciplines, from physics, materials science and robotics to biology, geophysics and astronomy. Recent research has highlighted how self-organisation can be both mediated and controlled by confinement. Confinement occurs through interactions with boundaries, and can function as either a catalyst or inhibitor of self-organisation. It can then become a means to actively steer the emergence or suppression of collective phenomena in space and time. Here, to provide a common framework for future research, we examine the role of confinement in self-organisation and identify overarching scientific challenges across disciplines that need to be addressed to harness its full scientific and technological potential. This framework will not only accelerate the generation of a common deeper understanding of self-organisation but also trigger the development of innovative strategies to steer it through confinement, with impact, e.g., on the design of smarter materials, tissue engineering for biomedicine and crowd management.

nlin.AO↗

arXiv2022

Kinetic phase diagram for nucleation and growth of competing crystal polymorphs in charged colloidal suspensions

We determine the full kinetic phase diagram for nucleation and growth of crystal phases in a suspension of charged colloids. We calculate nucleation barrier heights for face-centered cubic (fcc) and body-centered cubic (bcc) crystal phases for varying screening lengths and supersaturations using the seeding approach in extensive simulations. Using classical nucleation theory, we determine for the entire metastable fluid region the crystal polymorph with the lowest nucleation barrier. Surprisingly, we find a regime close to the triple point where metastable bcc can form due to a lower nucleation barrier, even though fcc is the stable phase. For higher supersaturation, we find that the difference in barrier heights decreases and we observe a mix of hexagonal close-packed (hcp), fcc and bcc structures in the growth of crystalline seeds as well as in spontaneously formed crystals.

cond-mat.soft↗

arXiv2021

Machine learning many-body potentials for colloidal systems

Simulations of colloidal suspensions consisting of mesoscopic particles and smaller species such as ions or depletants are computationally challenging as different length and time scales are involved. Here, we introduce a machine learning (ML) approach in which the degrees of freedom of the microscopic species are integrated out and the mesoscopic particles interact with effective many-body potentials, which we fit as a function of all colloid coordinates with a set of symmetry functions. We apply this approach to a colloid-polymer mixture. Remarkably, the ML potentials can be assumed to be effectively state-independent and can be used in direct-coexistence simulations. We show that our ML method reduces the computational cost by several orders of magnitude compared to a numerical evaluation and accurately describes the phase behavior and structure, even for state points where the effective potential is largely determined by many-body contributions.

cond-mat.soft↗

arXiv2021

Crystal polymorph selection mechanism of hard spheres hidden in the fluid

Nucleation plays a critical role in the birth of crystals and is associated with a vast array of phenomena such as protein crystallization and ice formation in clouds. Despite numerous experimental and theoretical studies, many aspects of the nucleation process like the polymorph selection mechanism in the early stages are far from being understood. Here, we show that the excess of particles in a face-centred-cubic (fcc)-like environment with respect to those in a hexagonal-close-packed (hcp)-like environment in a crystal nucleus of hard spheres as observed in simulations and experiments can be explained by the higher order structure in the fluid phase. We show using both simulations and experiments that, in the metastable fluid phase, fivefold symmetry clusters -- pentagonal bipyramids (PBs) -- known to be inhibitors of crystal nucleation, transform into a different cluster -- Siamese dodecahedra (SDs). Due to their geometry, these clusters form a bridge between the fivefold symmetric fluid and the fcc crystal, thus lowering its interfacial free energy with respect to the hcp crystal, and shedding new light on the polymorph selection mechanism.

cond-mat.soft↗

arXiv2021

Liquid Flow Reversibly Creates a Macroscopic Surface Charge Gradient

The charging and dissolution of mineral surfaces in contact with flowing liquids are ubiquitous in nature, as most minerals in water spontaneously acquire charge and dissolve. Mineral dissolution has been studied extensively under equilibrium conditions, even though non-equilibrium phenomena are pervasive and substantially affect the mineral-water interface. Here we demonstrate using interface-specific spectroscopy that liquid flow along a calcium fluoride surface creates a reversible, spatial charge gradient, with decreasing surface charge downstream of the flow. The surface charge gradient can be quantitatively accounted for by a reaction-diffusion-advection model, which reveals that the charge gradient results from a delicate interplay between diffusion, advection, dissolution, and desorption/adsorption. The underlying mechanism is expected to be valid for a wide variety of systems, including groundwater flows in nature and microfluidic systems.

cond-mat.soft↗

arXiv2021

Inverse design of soft materials via a deep-learning-based evolutionary strategy

Colloidal self-assembly -- the spontaneous organization of colloids into ordered structures -- has been considered key to produce next-generation materials. However, the present-day staggering variety of colloidal building blocks and the limitless number of thermodynamic conditions make a systematic exploration intractable. The true challenge in this field is to turn this logic around, and to develop a robust, versatile algorithm to inverse design colloids that self-assemble into a target structure. Here, we introduce a generic inverse design method to efficiently reverse-engineer crystals, quasicrystals, and liquid crystals by targeting their diffraction patterns. Our algorithm relies on the synergetic use of an evolutionary strategy for parameter optimization, and a convolutional neural network as an order parameter, and provides a new way forward for the inverse design of experimentally feasible colloidal interactions, specifically optimized to stabilize the desired structure.

cond-mat.soft↗

arXiv2021

Machine-learning free-energy functionals using density profiles from simulations

The formally exact framework of equilibrium Density Functional Theory (DFT) is capable of simultaneously and consistently describing thermodynamic and structural properties of interacting many-body systems in arbitrary external potentials. In practice, however, DFT hinges on approximate (free-)energy functionals from which density profiles (and hence the thermodynamic potential) follow via an Euler-Lagrange equation. Here we explore a relatively simple Machine Learning (ML) approach to improve the standard mean-field approximation of the excess Helmholtz free energy functional of a 3D Lennard-Jones system at a supercritical temperature. The learning set consists of density profiles from grand-canonical Monte Carlo simulations of this system at varying chemical potentials and external potentials in a planar geometry only. Using the DFT formalism we nevertheless can extract not only very accurate 3D bulk equations of state but also radial distribution functions using the Percus test-particle method. Unfortunately, our ML approach did not provide very reliable Ornstein-Zernike direct correlation functions for small distances.

cond-mat.soft↗

arXiv2020

Predicting the phase behavior of mixtures of active spherical particles

An important question in the field of active matter is whether or not it is possible to predict the phase behavior of these systems. Here, we study the phase coexistence of binary mixtures of torque-free active Brownian particles, for both systems with purely repulsive interactions and systems with attractions. Using Brownian dynamics simulations, we show that phase coexistences can be predicted quantitatively for these systems by measuring the pressure and "reservoir densities". Specifically, in agreement with previous literature, we find that the coexisting phases are in mechanical equilibrium, i.e. the two phases have the same pressure. Importantly, we also demonstrate that the coexisting phases are in chemical equilibrium by bringing each phase into contact with particle reservoirs, and showing that for each species these reservoirs are characterized by the same density for both phases. Using this requirement of mechanical and chemical equilibrium we accurately construct the phase boundaries from properties which can be measured purely from the individual coexisting phases. This result highlights that torque-free active Brownian systems follow simple coexistence rules, thus shedding new light on their thermodynamics.

cond-mat.soft↗

arXiv2020

Speeding up dynamics by tuning the non-commensurate size of rod-like particles in a smectic phase

Using simulations, we study the diffusion of rod-like guest particles in a smectic environment of rod-like host particles. We find that the dynamics of guest rods across smectic layers changes from a fast nematic-like diffusion to a slow hopping-type dynamics via an intermediate switching regime by varying the length of the guest rods with respect to the smectic layer spacing. We determine the optimal rod length that yields the fastest and the slowest diffusion in a lamellar environment. We show that this behavior can be rationalized by a complex 1D effective periodic potential exhibiting two energy barriers, resulting in a varying preferred mean position of the guest particle in the smectic layer. The interplay of these two barriers controls the dynamics of the guest particles yielding a slow, an intermediate and a fast diffusion regime depending on the particle length.

cond-mat.soft↗

arXiv2019

The role of topological defects in the two-stage melting and elastic behavior of active Brownian particles

We find that crystalline states of repulsive active Brownian particles at high activity melt into a hexatic state but this transition is not driven by an unbinding of bound dislocation pairs as suggested by the Kosterlitz-Thouless-Halperin-Nelson-Young (KTHNY) theory. Upon reducing the density, the crystalline state melts into a high-density hexatic state devoid of any defects. Decreasing the density further, the dislocations proliferate and introduce plasticity in the system, nevertheless maintaining the hexatic state, but eventually melting into a fluid state. Remarkably, the elastic constants of active solids are equal to those of their passive counterparts, as the swim contribution to the stress tensor is negligible in the solid state. The sole effect of activity is that the stable solid regime shifts to higher densities. Furthermore, discontinuities in the elastic constants as a function of density correspond to changes in the defect concentrations rather than to the solid-hexatic transition.

cond-mat.soft↗