arXiv subjects

Takahiro Yokoyama

Publications and source records attributed to Takahiro Yokoyama.

7 recordsLinked to original sources

arXiv2026

Interfacial Packing of DNA Nanostars Regulates Dynamics on Synthetic Cell Membranes

DNA nanostructures are emerging as programmable components for engineering synthetic cell membranes, yet how their collective packing and deformability regulate molecular dynamics at membrane interfaces remains poorly understood. Here, we investigate the packing and mobility of DNA nanostars with tunable stiffness on lipid-coated droplets. The negatively charged nanostars spontaneously adsorb onto cationic membranes, and their interfacial packing is changed by the bulk DNA concentration and droplet size, which together determine the number of encapsulated nanostars. Combining fluorescence recovery after photobleaching experiments with coarse-grained simulations, we reveal distinct packing-dynamics relationships for rigid and soft nanostars. For rigid nanostars, diffusion first decreases gradually and then drops sharply with increasing interfacial packing, approaching a dynamically arrested state consistent with jamming-like behavior. In contrast, at equivalent experimental conditions, soft nanostars systematically reach lower interfacial packing fractions and show a weaker decrease in apparent mobility. This behavior is consistent with their weaker membrane affinity, which facilitates adsorption-desorption with the bulk. When this exchange is suppressed in simulations, crowding reduces the lateral diffusion of both nanostar types, but produces distinct dense configurations: soft nanostars become strongly deformed, whereas rigid nanostars largely retain their shape and form interlocked, gear-like arrangements. Nanostar adsorption also impedes lipid diffusion, while differences in membrane affinity result in distinct lipid mobile fractions. These findings reveal how the interplay between nanostar packing and deformability regulates molecular transport at membrane interfaces, providing a physical design principle for tuning lateral fluidity and crowding in artificial cells.

cond-mat.soft↗

arXiv2025

Composition-agnostic prediction of self-assembly in multicomponent amphiphile mixtures from molecular structure

Predicting self-assembly in multi-component amphiphilic systems is challenging due to the complexity of intercomponent interactions and the combinatorial growth of possible formulations. In this study, we develop a unified machine-learning framework that directly predicts self-assembly behavior from the molecular structures of constituent components, independent of the number or identity of those components. We extend the critical packing parameter (CPP) to multi-component systems and generate a large dataset of self-assembled morphologies using dissipative particle dynamics (DPD) simulations. By systematically evaluating twelve combinations of feature extraction methods and model architectures, we find that models incorporating a fully connected graph convolutional network (GCN) layer achieve superior performance, with the GCN-GCN architecture accurately capturing both intramolecular relationships and intercomponent interactions. Notably, this model exhibits strong extrapolative capability: it accurately predicts CPP values for five-component mixtures even when trained only on systems with fewer components, and it maintains high accuracy for mixtures composed of molecular species that are entirely absent from the training data. These results demonstrate that a composition-agnostic predictive framework can enable efficient virtual screening and provide a foundation for the rational design of complex amphiphilic materials.

cond-mat.soft↗

arXiv2025

Molecular simulations of phase separation in elastic polymer networks

Phase separation within polymer networks plays a central role in shaping the structure and mechanics of both synthetic materials and living cells, including the formation of biomolecular condensates within cytoskeletal networks. Previous experiments and theoretical studies indicate that network elasticity can regulate demixing and stabilize finite-sized domains, yet the microscopic origin of this size selection remains elusive. Here, we use coarse-grained molecular dynamics simulations with implicit solvent to investigate how network architecture controls phase separation and limits domain growth. By systematically varying chain contour length, chain rigidity, and network topology, we uncover that finite domains emerge when intrinsic chain- or network-level length scales, such as persistence length or entanglement length, impose local constraints on coarsening. Further, the size of these finite domains is highly correlated with these microscopic network properties, but depends surprisingly little on bulk elasticity. Taken together, our findings establish a molecular basis for understanding droplet formation in polymer networks, and provide guiding principles for engineering materials and interpreting condensate behavior in cells.

cond-mat.soft↗

arXiv2024

Machine learning-enabled exploration of mesoscale architectures in amphiphilic-molecule self-assembly

Amphiphilic molecules spontaneously form self-assembled structures of various shapes depending on their molecular structures, the temperature, and other physical conditions. The functionalities of these structures are dictated by their formations and their properties must be evaluated for reproduction using molecular simulations. However, the assessment of such intricate structures involves many procedural steps. This study investigates the potential of machine-learning models to extract structural features from mesoscale non-ordered self-assembled structures, and suggests a methodology in which machine-learning models for the structural analysis of self-assembled structures are trained on particle types and coordinate data. In the proposed approach, graph neural networks are utilised to extract local structural data for analysis. In simulations using several hundred self-assembled structures of up to 4050 coarse-grained particles, local structures are successfully extracted and classified with up to 78.35 % accuracy. As the machine-learning models learn structural characteristics without the need for human-made feature engineering, the proposed method has important potential applications in the field of materials science.

cond-mat.soft↗

arXiv2023

Machine learning prediction of self-assembly and analysis of molecular structure dependence on the critical packing parameter

Amphiphilic molecules spontaneously form self-assembly structures based on physical conditions such as molecular structure, concentration, and temperature. These structures exhibit various useful functions according to their morphology. The concept of the critical packing parameter serves to correlate self-organized structures with chemical composition. However, unless both molecular arrangement and self-assembly patterns are understood, direct computational utilization for molecular design remains challenging. In this study, we attempt to predict the self-assembled structure of a molecule directly from its chemical structure and analyze factors influencing it using machine learning. Dissipative particle dynamics simulations were used to reproduce many self-assembly structures composed of various chemical structures, and their critical packing parameters were calculated. A machine learning model was built using the chemical structures as input data and the critical packing parameters as output data.As a result, both Random Forest and a type of Recurrent Neural Network known as GRU demonstrated high predictive accuracy. It has been revealed through feature importance analysis and dependence on sample size that the amphiphilic nature of molecules significantly influences the self-assembly structures. Additionally, the importance of selecting an appropriate molecular structure representation for each algorithm has been emphasized. The results of this research will help to further streamline product development in the fields of materials science, materials chemistry, and medical materials.

cond-mat.mtrl-sci↗

arXiv2023

An Accurate Graph Generative Model with Tunable Features

A graph is a very common and powerful data structure used for modeling communication and social networks. Models that generate graphs with arbitrary features are important basic technologies in repeated simulations of networks and prediction of topology changes. Although existing generative models for graphs are useful for providing graphs similar to real-world graphs, graph generation models with tunable features have been less explored in the field. Previously, we have proposed GraphTune, a generative model for graphs that continuously tune specific graph features of generated graphs while maintaining most of the features of a given graph dataset. However, the tuning accuracy of graph features in GraphTune has not been sufficient for practical applications. In this paper, we propose a method to improve the accuracy of GraphTune by adding a new mechanism to feed back errors of graph features of generated graphs and by training them alternately and independently. Experiments on a real-world graph dataset showed that the features in the generated graphs are accurately tuned compared with conventional models.

cs.LG↗

arXiv2023

Structure Formation of Amphiphilic Nanocubes at Rest and Under Shear

We investigate the self-assembly of amphiphilic nanocubes under rest and shear using molecular dynamics (MD) simulations and kinetic Monte Carlo (KMC) calculations. These particles combine both interaction and shape anisotropy, making them valuable models for studying folded proteins and DNA-functionalized nanoparticles. The nanocubes can self-assemble into various finite-sized aggregates ranging from rods to self-avoiding random walks, depending on the number and placement of the hydrophobic faces. Our study focuses on suspensions containing multi- and one-patch cubes, with their ratio systematically varied. When the binding energy is comparable to the thermal energy, the aggregates consist of only few cubes that spontaneously associate/dissociate. However, highly stable aggregates emerge when the binding energy exceeds the thermal energy. Generally, the mean aggregation number of the self-assembled clusters increases with the number of hydrophobic faces and decreases with the fraction of one-patch cubes. In sheared suspensions, the more frequent collisions between nanocube clusters lead to faster aggregation dynamics but also to smaller terminal steady-state mean cluster sizes. The MD and KMC simulations are in excellent agreement, and the analysis of the rate kernels enables the identification of the primary mechanisms responsible for the (shear-induced) cluster growth and breakup.

cond-mat.soft↗