Self-Assembly of Model Three- and Four-Patch Colloidal Particles in Two Dimensions.

A coarse-grained effective solvent model of two-patch particles is extended to study the self-assembly of three- and four-patch particles to two-dimensional honeycomb and square lattices, respectively. Employing this model, grand canonical ensemble simulations are done to calculate vapor-liquid equilibria and the critical temperatures for patchy particles of various patch widths. The range of stability of the liquid, although very limited compared to isotropic particles, which interact through a longer-range potential, depends on the patch width and on the number of patches. Biased sampling and unbiased simulations are also done to investigate the mechanism of nucleation and crystal growth for honeycomb and square lattices, self-assembled from three- and four-patch particles, respectively. A two-step mechanism governs the nucleation of both lattices. In the first step, the particles form a dense amorphous network, and in the second step, the particles inside the amorphous network reorient to form crystalline nuclei. Barrier heights for the nucleation of honeycomb and square lattices are 7.8 kBT and 7.4 kBT, which are close to the reported values for the nucleation of the kagome lattice. In agreement with confocal microscopy experiments, the self-assembly in a honeycomb lattice involves the formation of 5- to 7-membered rings. The 5- and 7-membered rings hamper the nucleation of the honeycomb lattice, through defect formation and rotation of the symmetry planes of crystals that form at their surfaces. With the progress of self-assembly, a substantial amount of restructuring of the defects and crystals in their vicinity is needed to heal the defects.

Predicting the artificial dynamical acceleration of binary hydrocarbon mixtures upon coarse-graining with roughness volumes and simple averaging rules.

Coarse-grained (CG) molecular models greatly reduce the computational cost of simulations allowing for longer and larger simulations, but come with an artificially increased acceleration of the dynamics when compared to the parent atomistic (AA) simulation. This impedes their use for the quantitative study of dynamical properties. During coarse-graining, grouping several atoms into one CG bead not only reduces the number of degrees of freedom but also reduces the roughness on the molecular surfaces, leading to the acceleration of dynamics. The RoughMob approach [M. K. Meinel and F. Müller-Plathe, J. Phys. Chem. B 126(20), 3737-3747 (2022)] quantifies this change in geometry and correlates it to the acceleration by making use of four so-called roughness volumes. This method was developed using simple one-bead CG models of a set of hydrocarbon liquids. Potentials for pure components are derived by the structure-based iterative Boltzmann inversion. In this paper, we find that, for binary mixtures of simple hydrocarbons, it is sufficient to use simple averaging rules to calculate the roughness volumes in mixtures from the roughness volumes of pure components and add a correction term quadratic in the concentration without the need to perform any calculation on AA or CG trajectories of the mixtures themselves. The acceleration factors of binary diffusion coefficients and both self-diffusion coefficients show a large dependence on the overall acceleration of the system and can be predicted a priori without the need for any AA simulations within a percentage error margin, which is comparable to routine measurement accuracies. Only if a qualitatively accurate description of the concentration dependence of the binary diffusion coefficient is desired, very few additional simulations of the pure components and the equimolar mixture are required.

Synthetic Force-Field Database for Training Machine Learning Models to Predict Mobility-Preserving Coarse-Grained Molecular-Simulation Potentials.

Balancing accuracy and efficiency is a common problem in molecular simulation. This tradeoff is evident in coarse-grained molecular dynamics simulation, which prioritizes efficiency, and all-atom molecular simulation, which prioritizes accuracy. Despite continuous efforts, creating a coarse-grained model that accurately captures both the system's structure and dynamics remains elusive. In this article, we present a data-driven approach for constructing coarse-grained models that aim to describe both the structure and dynamics of the system equally well. While the development of machine learning models is well-received in the scientific community, the significance of dataset creation for these models is often overlooked. However, data-driven approaches cannot progress without a robust dataset. To address this, we construct a database of synthetic coarse-grained potentials generated from unphysical all-atom models. A neural network is trained with the generated database to predict the coarse-grained potentials of real liquids. We evaluate their quality by calculating the combined loss of structural and dynamical accuracy upon coarse-graining. When we compare our machine learning-based coarse-grained potential with the one from iterative Boltzmann inversion, the machine learning prediction turns out better for all eight hydrocarbon liquids we studied. As all-atom surfaces turn more nonspherical, both ways of coarse-graining degrade. Still, the neural network outperforms iterative Boltzmann inversion in constructing good quality coarse-grained models for such cases. The synthetic database and the developed machine learning models are freely available to the community, and we believe that our approach will generate interest in efficiently deriving accurate coarse-grained models for liquids.

Self-Assembly Pathways of Triblock Janus Particles into 3D Open Lattices.

The self-assembly of triblock Janus particles is simulated from a fluid to 3D open lattices: pyrochlore, perovskite, and diamond. The coarse-grained model explicitly takes into account the chemical details of the Janus particles (attractive patches at the poles and repulsion around the equator) and it contains explicit solvent particles. Hydrodynamic interactions are accounted for by dissipative particle dynamics. The relative stability of the crystals depends on the patch width. Narrow, intermediate, and wide patches stabilize the pyrochlore-, the perovskite-, and the diamond-lattice, respectively. The nucleation of all three lattices follows a two-step mechanism: the particles first agglomerate into a compact and disordered liquid cluster, which does not crystallize until it has grown to a threshold size. Second, the particles reorient inside this cluster to form crystalline nuclei. The free-energy barriers for the nucleation of pyrochlore and perovskite are ≈10 kBT, which are close to the nucleation barriers of previously studied 2D kagome lattices. The barrier height for the nucleation of diamond, however, is much larger (>20 kBT), as the symmetry of the triblock Janus particles is not perfect for a diamond structure. The large barrier is associated with the reorientation of particles, i.e., the second step of the nucleation mechanism.

Understanding Humidity-Enhanced Adhesion of Geckos: Deep Neural Network-Assisted Multi-Scale Molecular Modeling.

A higher relative humidity leads to an increased sticking power of gecko feet to surfaces. The molecular mechanism responsible for this increase, however, is not clear. Capillary forces, water mediating keratin-surface contacts and water-induced softening of the keratin are proposed as candidates. In previous work, strong evidence for water mediation is found but indirect effects via increased flexibility are not completely ruled out. This article studies the latter hypothesis by a bottom-up coarse-grained mesoscale model of an entire gecko spatula designed without explicit water particles, so that capillary action and water-mediation are excluded. The elasticity of this model is adjusted with a deep neural network to atomistic elastic constants, including water at different concentrations. Our results show clearly that on nanoscopic flat surfaces, the softening of keratin by water uptake cannot nearly account for the experimentally observed increase in gecko sticking power. Here, the dominant mechanism is the mediation of keratin-surface contacts by intervening water molecules. This mechanism remains important on nanostructured surfaces. Here, however, a water-induced increase of the keratin flexibility may enable the spatula to follow surface features smaller than itself and thereby increase the number of contacts with the surface. This leads to an appreciable but not dominant contribution to the humidity-increased adhesion. Recently, by atomistic grand-canonical molecular dynamics simulation, the room-temperature isotherm is obtained for the sorption of water into gecko keratin, to the authors' knowledge, the first such relation for any beta-keratin. In this work, it relates the equilibrium water content of the keratin to the ambient relative humidity.

Molecular Structure and Dynamics in Wet Gecko ß-Keratin.

Molecular dynamics simulations are performed to investigate the molecular picture of water sorption in gecko keratin and the influence of relative humidity (RH) on the local structure and dynamics in water-swollen keratin. At low RHs, water sorption occurs through hydrogen bonding of water with the hydrophilic groups of keratin. At high RHs (>80%), additional water molecules connect to the first "layer" of amide-connected water molecules (multimolecular sorption) through hydrogen bonds, giving rise to a sigmoidal shape of the sorption isotherm. This causes the formation of large chain-like clusters surrounding the hydrophilic groups of keratin, which upon a further increase of the RH form a percolating water network. An examination of the dynamics of water molecules sorbed in keratin demonstrates that there are two states, bound and free, for water. The dynamics of water in these states depends on the RH. At low RHs, large-scale translational motions of tightly bound water molecules to keratin are needed to remake the entire hydration shell of the keratin. At high RHs (>80%), the water molecules more quickly exchange between the two states. The center-of-mass mean-square displacement of water molecules indicates a hopping motion of water molecules in the keratin solvation shell. The hopping mechanism is more pronounced at RHs < 80%. At higher RHs, water translation through water clusters (water network) dominates. We have observed two regimes for the dependence of dynamical properties on the RH: a regime of gradual increase of the dynamics over 10% < RH < 80% and a regime of drastic dynamic acceleration at RH > 80%. The latter regime begins exactly where the water uptake and the volume swelling also increase much more and where a drastic change in the elastic properties of gecko keratin has been observed. A nearly linear relation between the relaxation times for all dynamical processes and the water content of gecko keratin is observed.

Toward a Mobility-Preserving Coarse-Grained Model: A Data-Driven Approach.

Coarse-grained molecular dynamics (MD) simulation is a promising alternative to all-atom MD simulation for the fast calculation of system properties, which is imperative in designing materials with a specific target property. There have been several coarse-graining strategies developed over the past few years that provide accurate structural properties of the system. However, these coarse-grained models share a major drawback in that they introduce an artificial acceleration in molecular mobility. In this paper, we report a data-driven approach to generate coarse-grained models that preserve the all-atom molecular mobility. We designed a machine learning model in the form of an artificial neural network, which directly predicts the simulation-ready mobility-preserving coarse-grained potential as an output given the all-atom force field (FF) parameters as inputs. As a proof of principle, we took 2,3,4-trimethylpentane as a model system and described the development of machine learning models in detail. We quantify the artificial acceleration in molecular mobility by defining the acceleration factor as the ratio of the coarse-grained and the all-atom diffusion coefficient. The predicted coarse-grained potential generated by the best machine learning model can bring down the acceleration factor to a value of ∼2, which could be otherwise as large as 7 for a typical value of 3 × 10-9 m2 s-1 for the all-atom diffusion coefficient. We believe our method will be of interest in the community as a route to generating coarse-grained potentials with accurate dynamics.

How Does Gecko Keratin Stick to Hydrophilic and Hydrophobic Surfaces in the Presence and Absence of Water? An Atomistic Molecular Dynamics Investigation.

We developed a united-atom model of gecko keratin to investigate the influence of electrostatic and van der Waals contributions to gecko adhesion in scenarios representing gecko's natural habitats. The keratin model assumes that only intrinsically disordered regions directly contact the surface. Contact angles of two generic substrate surfaces that we created match those previously used in AFM experiments on gecko adhesion. Force probe molecular dynamics simulations pulling the keratin from the surface show that the pull-off force increases with increased water content and is inversely related to the water contact angle of the surface. This matches experimental trends. We investigated the number and charge density of keratin and water at the surface, confirming a water-mediating effect and are able to show that keratin folds polar groups to the hydrophilic surface. We decomposed energetic contributions during pull-off, and our computational model shows that in contrast to popular hypotheses, long-range electrostatic interactions determine much of the pull-off process. The contribution of electrostatics to adhesion may be in the order of the van der Waals contributions.

Water uptake by gecko ß-keratin and the influence of relative humidity on its mechanical and volumetric properties.

Grand canonical ensemble molecular dynamics simulations are done to calculate the water content of gecko ß-keratin as a function of relative humidity (RH). For comparison, we experimentally measured the water uptake of scales of the skin of cobra Naja nigricollis. The calculated sigmoidal sorption isotherm is in good agreement with experiment. To examine the softening effect of water on gecko keratin, we have calculated the mechanical properties of dry and wet keratin samples, and we have established relations between the mechanical properties and the RH. We found that a higher RH causes a decrease in the Young's modulus, the yield stress, the yield strain, the stress at failure and an increase in the strain at failure of the gecko keratin. At low RHs (less than 80%), the change in the mechanical properties is small, with most of the changes occurring at higher RHs. The changes in the macroscopic properties of the keratin are explained by the action of sorbed water on the molecular scale. It causes keratin to swell, thereby increasing the distances between amino acids. This has a weakening effect on amino acid interactions and softens the keratin material. The effect is more pronounced at higher RHs.

Gecko Adhesion on Flat and Rough Surfaces: Simulations with a Multi-Scale Molecular Model.

A multiscale modeling approach is used to develop a particle-based mesoscale gecko spatula model that is able to link atomistic simulations and mesoscale (0.44 µm) simulations. It is used to study the detachment of spatulae from flat as well as nanostructured surfaces. The spatula model is based on microscopical information about spatulae structure and on atomistic molecular simulation results. Target properties for the coarse-graining result from a united-atom model of gecko keratin in periodic boundary conditions (PBC), previously developed by the authors. Pull-off forces necessary to detach gecko keratin under 2D PBC parallel to the surface are previously overestimated when only a small region of a spatula is examined. It is shown here that this is due to the restricted geometry (i.e., missing peel-off mode) and not model parameters. The spatula model peels off when pulled away from a surface, both in the molecular picture of the pull-off process and in the force-extension curve of non-equilibrium simulations mimicking single-spatula detachment studied with atomic force microscopy equipment. The force field and spatula model can reproduce experimental pull-off forces. Inspired by experimental results, the underlying mechanism that causes pull-off forces to be at a minimum on surfaces of varying roughnesses is also investigated. A clear sigmoidal increase in the pull-off force of spatulae with surface roughness shows that adhesion is determined by the ratio between spatula pad area and the area between surface peaks. Experiments showed a correlation with root-mean-square roughness of the surface, but the results of this work indicate that this is not a causality but depends on the area accessible.

Computing the Work of Solid-Liquid Adhesion in Systems with Damped Coulomb Interactions via Molecular Dynamics: Approaches and Insights.

Recently, the dry-surface method [ Langmuir 2016, 31, 8335-8345] has been developed to compute the work of adhesion of solid-liquid and other interfaces using molecular dynamics via thermodynamic integration. Unfortunately, when long-range Coulombic interactions are present in the interface, a special treatment is required, such as solving additional Poisson equations, which is usually not implemented in generic molecular dynamics software, or as fixing some groups of atoms in place, which is undesirable most of the time. In this work, we replace the long-range Coulombic interactions with damped Coulomb interactions, and explore several thermal integration paths. We demonstrate that regardless of the integration path, the same work of adhesion values are obtained as long as the path is reversible, but the numerical efficiency differs vastly. Simple scaling of the interactions is most efficient, requiring as little as 8 sampling points, followed by changing the Coulomb damping parameter, while modifying the Coulomb interaction cutoff length performs worst. We also demonstrate that switching long-range Coulombic interactions to damped ones results in a higher work of adhesion by about 10 mJ/m2 because of slightly different liquid molecule orientation at the solid-liquid interface, and this value is mostly unchanged for surfaces with substantially different Coulombic interactions at the solid-liquid interface. Finally, even though it is possible to split the work of adhesion into van der Waals and Coulomb components, it is known that the specific per-component values are highly dependent on the integration path. We obtain an extreme case, which demonstrates that caution should be taken even when restricting to qualitative comparison.

Conductance Switching in Liquid Crystal-Inspired Self-Assembled Monolayer Junctions.

We present the prototype of a ferroelectric tunnel junction (FTJ), which is based on a self-assembled monolayer (SAM) of small, functional molecules. These molecules have a structure similar to those of liquid crystals, and they are embedded between two solid-state electrodes. The SAM, which is deposited through a short sequence of simple fabrication steps, is extremely thin (3.4 ± 0.5 nm) and highly uniform. The functionality of the FTJ is ingrained in the chemical structure of the SAM components: a conformationally flexible dipole that can be reversibly reoriented in an electrical field. Thus, the SAM acts as an electrically switchable tunnel barrier. Fabricated stacks of Al/Al2O3/SAM/Pb/Ag with such a polar SAM show pronounced hysteretic, reversible conductance switching at voltages in the range of ±2-3 V, with a conductance ratio of the low and the high resistive states of up to 100. The switching mechanism is analyzed using a combination of quantum chemical, molecular dynamics, and tunneling resistance calculation methods. In contrast to more common, inorganic material-based FTJs, our approach using SAMs of small organic molecules allows for a high degree of functional complexity and diversity to be integrated by synthetic standard methods, while keeping the actual device fabrication process robust and simple. We expect that this technology can be further developed toward a level that would then allow its application in the field of information storage and processing, in particular for in-memory and neuromorphic computing architectures.

Contact line friction and dynamic contact angles of a capillary bridge between superhydrophobic nanostructured surfaces.

The Cassie-Baxter state of wetting explains a large equilibrium contact angle and the slippery dynamics of a water droplet on a superhydrophobic rough surface. It also causes a contact angle hysteresis (CAH) that cannot be fully described by dynamic wetting theories including the molecular kinetic theory (MKT). We analyze the contact line dynamics on a superhydrophobic surface in the framework of the MKT. Multi-body dissipative particle dynamics simulations of a capillary bridge confined between two rough surfaces under steady shear are performed. We find that, in addition to the contact line friction force from the MKT, an additional friction force contribution is needed on rough surfaces. It can be obtained by subtracting from the total friction force the force solely caused by the actual liquid-solid contact area. We find that the additional force is almost constant at all contact line velocities. Thus, it is directly related to the CAH. The CAH originates not only from contact line pinning but also from the shear flow due to the strong friction in the central region of the liquid-solid interface away from the contact line. The analysis of the particle flow inside the capillary bridge shows that liquid particles trapped in the grooves of the surface texture actually move with the same velocity as the surface and exert strong additional friction to other liquid particles. This work extends the MKT to rough surfaces, as well as to elucidate the origin of the CAH of a capillary bridge. The finding would help to better understand other situations of dynamic wetting on superhydrophobic surfaces.

Mobility of Polymer Melts in a Regular Array of Carbon Nanotubes.

In polymer nanocomposites, mechanical properties essentially depend on the alignment of nanoparticles and polymers. In this work, we investigate an entangled polymer melt in a confinement computationally, in order to get an insight into the mobility behavior of the polymer chains. The confinement consists of nanotubes, arranged in a hexagonal array. We use dissipative particle dynamics, a fast, soft-core simulation method, and reintroduce entanglement dynamics via slip-springs. We observe a distinct influence of the confinement as diffusion is increased in the direction parallel to the nanotubes. Furthermore, we observe that an orientation of the polymers parallel to the nanotubes and chains are compressed in the direction orthogonal to their primitive path. The diffusion parallel to the nanotubes increases further as we increase the nanotube volume fraction in our systems. Moreover, we investigate the slip-spring distribution in the proximity of the nanotube surfaces of our fast and simple slip-spring model, which we find to coincide with results reported for more sophisticated and expensive methods. Our DPD model shows potential applicability to a wide range of polymer nanocomposites while preserving reptation behavior, which is typically lost due to the use of soft-core models.

Slip-Spring Hybrid Particle-Field Molecular Dynamics for Coarse-Graining Branched Polymer Melts: Polystyrene Melts as an Example.

The topology of chains significantly modifies the dynamical properties of polymer melts. Here, we extend a recently developed efficient simulation method, namely the slip-spring hybrid particle-field (SS-hPF) model, to study the structural and dynamical properties of branched polymer melts over large spatial-temporal scales. In the coarse-grained SS-hPF simulation of polymers, the bonded potentials are derived by iterative Boltzmann inversion from the underlying fine-grained model. The nonbonded potentials are computed from a density functional field instead of pairwise interactions used in standard molecular dynamics simulations, which increases the computational efficiency by a factor of 10-20. The entangled dynamics is lost due to the soft-core nature of density functional field interactions. It is recovered by a multichain slip-spring model that is rigorously parametrized from existing experimental or simulation data. To quantitatively predict the relaxation and diffusion of branched polymers, which are dominated by arm retraction rather than chain reptation, the slip-spring algorithm is augmented to improve the polymer dynamics near the branch point. Multiple dynamical observables, e.g., diffusion coefficients, arm relaxations, and tube survival probabilities, are characterized in an example coarse-grained model of symmetric and asymmetric star-shaped polystyrene melts. Consistent dynamical behaviors are identified and compared with theoretical predictions. With a single rescaling factor, the prediction of diffusion coefficients agrees well with the available experimental measurements. In this work, an efficient approach is provided to build chemistry-specific coarse-grained models for predicting the dynamics of branched polymers.

Roughness Volumes: An Improved RoughMob Concept for Predicting the Increase of Molecular Mobility upon Coarse-Graining.

The reduced number of degrees of freedom in a coarse-grained molecular model compared to its parent atomistic model not only makes it possible to simulate larger systems for longer time scales but also results in an artificial mobility increase. The RoughMob method [Meinel, M. K. and Müller-Plathe, F. J. Chem. Theory Comput. 2020, 16, 1411.] linked the acceleration factor of the dynamics to the loss of geometric information upon coarse-graining. Our hypothesis is that coarse-graining a multiatom molecule or group into a single spherical bead smooths the molecular surface and, thus, leads to reduced intermolecular friction. A key parameter is the molecular roughness difference, which is calculated via a numerical comparison of the molecular surfaces of both the atomistic and coarse-grained models. Augmenting the RoughMob method, we add the concept of the region where the roughness acts. This information is contained in four so-called roughness volumes. For 17 systems of homogeneous hydrocarbon fluids, simple one-bead coarse-grained models are derived by the structure-based iterative Boltzmann inversion. They include 13 different homogeneous aliphatic and aromatic molecules and two different mapping schemes. We present a simple way to correlate the roughness volumes to the acceleration factor. The resulting relation is able to a priori predict the acceleration factors for an extended size and shape range of hydrocarbon molecules, with different mapping schemes and different densities.

Addressing Surface Effects at the Particle-Continuum Interface in a Molecular Dynamics and Finite Elements Coupled Multiscale Simulation Technique.

Atomistic-to-continuum coupling methods are used to unravel molecular mechanisms of polymers and polymer composites. These multiscale techniques advantageously combine the computational efficiency of continuum approaches while keeping the accuracy of particle-based methods. The Capriccio method [Pfaller et al. Comput. Methods Appl. Mech. Eng. 2013, 260, 109-129.] is a well-proven multiscale technique, which connects finite elements (FE) with molecular dynamics (MD) in a partitioned-domain approach. A vital aspect of these multiscale methods is to provide physically sound boundary conditions to the particle domain suppressing any interface effects at the domain boundary occurring due to the coupling. These interfacial coupling artifacts still pose a significant problem, especially for amorphous polymers due to their highly irregular microstructure. We solve this problem by extending the particle-continuum interface by a layer of passive atoms which move with the outer continuum, thereby providing the missing interactions with a surrounding polymer bulk to the inner particle region. This solution allows us to successfully reproduce structural and mechanical properties obtained under conventional periodic boundary conditions, like density, stress, Young's modulus, and Poisson's ratio. Furthermore, we demonstrate the application of a nonaffine deformation by means of a simple bending test. In general, our revised method provides a framework to apply complex deformations for molecular scientists, while it allows the engineering community to examine challenging phenomena such as fracture behavior at a molecular level.

How Ethanolic Disinfectants Disintegrate Coronavirus Model Membranes: A Dissipative Particle Dynamics Simulation Study.

We have developed dissipative particle dynamics models for pure dipalmitoylphosphatidylcholine (DPPC), dioleoylphosphatidylcholine (DOPC), and dimyristoylphosphatidylcholine (DMPC) as well as their binary and ternary mixed membranes, as coronavirus model membranes. The stabilities of pure and mixed membranes, surrounded by aqueous solutions containing up to 70 mol % ethanol (alcoholic disinfectants), have been investigated at room temperature. We found that aqueous solutions containing 5-10 mol % ethanol already have a significant weakening effect on the pure and mixed membranes. The magnitude of the effect depends on the membrane composition and the ethanol concentration. Ethanol permeabilizes the membrane, causing its lateral swelling and thickness shrinking and reducing the orientational order of the hydrocarbon tail of the bilayer. The free energy barrier for the permeation of ethanol in the bilayers is considerably reduced by the ethanol uptake. The rupture-critical ethanol concentrations causing the membrane failure are 20.7, 27.5, and 31.7 mol % in the aqueous phase surrounding pure DMPC, DOPC, and DPPC membranes, respectively. Characterizing the failure of lipid membranes by a machine-learning neural network framework, we found that all mixed binary and/or ternary membranes disrupt when immersed in an aqueous solution containing a rupture-critical ethanol concentration, ranging from 20.7 to 31.7 mol %, depending on the composition of the membrane; the DPPC-rich membranes are more intact, while the DMPC-rich membranes are least intact. Due to the tight packing of long, saturated hydrocarbon tails in DPPC, increasing the DPPC content of the mixed membrane increases its stability against the disinfectant. At high DPPC concentrations, where the DOPC and DMPC molecules are confined between the DPPC lipids, the ordered hydrocarbon tails of DPPC also induce order in the DOPC and DMPC molecules and, hence, stabilize the membrane more. Our simulations on pure and mixed membranes of a diversity of compositions reveal that a maximum ethanol concentration of 32 mol % (55 wt %) in the alcohol-based disinfectants is enough to disintegrate any membrane composed of these three lipids.

Self-Assembly of Model Triblock Janus Colloidal Particles in Two Dimensions.

A simplified two-dimensional effective-solvent model of triblock Janus particles, consisting of three interaction sites in a linear configuration, a core particle, and two particles modeling the attractive patches at the poles, is developed to study the mechanism of nucleation and self-assembly in triblock Janus particles. The potential energy parameters are tuned against phase transition temperatures and free energy barriers to the nucleation of crystalline phases, calculated from our previous detailed model of Janus particles. Vapor-liquid equilibria and critical temperatures are calculated by grand-canonical molecular dynamics simulations for particles of different patch widths. With metadynamics, phase equilibria, mechanism of nucleation, and free energy barriers to nucleation are investigated. The minimum free energy path to nucleation indicates two steps. The first step, with a higher free energy increase, consists of the densification of the fluid into a disordered cluster. In the second step, of a lower free energy barrier, the inner particles of the disordered cluster reorient to form a crystalline nucleus. This two-step mechanism of nucleation of a kagome lattice is in complete agreement with the experiment and with our previous simulations using a detailed model of Janus particles. Large systems at a slight supersaturation generate multiple crystalline domains, which are misaligned at the grain boundaries. In complete agreement with the experiment and with previous simulation results, we observe a two-step mechanism for crystal growth: melting of the smaller (less stable) crystallites to a fluid followed by recrystallization at the surface of neighboring bigger (more stable) crystallites. A comparison of the present softer modeling of a Janus particle with harder models in the literature for self-assembly of Janus particles indicates that softer potentials stabilize open lattices (e.g., kagome) more than dense lattices (e.g., hexagonal). Also, experimental locations of phase transition points and barrier heights to nucleation are better reproduced by the present model than by the existing simple models.