arXiv subjects

Antonia Statt

Publications and source records attributed to Antonia Statt.

At least 19 recordsLinked to original sources

arXiv2026

Phase Behavior of Unilamellar Hybrid Lipid-Diblock Copolymer Membranes

Hybrid lipid block copolymer membranes are promising for many applications in drug delivery, single molecule detection, in-membrane protein folding, and synthetic cells. However, rational design is difficult due to the many design parameters which determine the nano- and micron-scale morphology and properties. In this work, we propose a physically-informed framework which incorporates chemical immiscibility, hydrophobic thickness mismatch and geometric constraints to predict the morphology of hybrid membranes. For this purpose, we extend existing theory for amphiphilic monolayers to model the thickness of diblock copolymer bilayers, demonstrating that both the hydrophobic and hydrophilic block lengths determine the thickness. We identify and rationalize the four primary membrane morphologies observed: mixed, laterally phase-separated, unzipped (thick-thin coexistence), and polymer-rich. Specifically, chemical immiscibility differentiates mixed membranes from laterally phase separated membranes, and hydrophobic mismatch drives transitions to unzipped or polymer-rich morphologies. Areal density, finally, determines the crossover between unzipped and polymer-rich states. We validate our theoretical predictions using coarse-grained molecular dynamics across a broad parameter space, including multiple lipid species (DOPC, DPPC), polymer species (1,4 PBD-b-PEO, 1,2 PBD-b-PEO, PE-b-PEO), block lengths, temperatures, and compositions. The resulting phase maps unify previously reported experimental and simulation observations and enable a generic and mechanistic understanding for the effect of system parameters on the nanoscale morphology.

cond-mat.soft↗

arXiv2026

Axial dispersion in dilute solutions of linear and branched polymers in parallel-plate and expansion-contraction microchannels

The axial dispersion of polymers in microchannels depends on an interplay between microchannel geometry, polymer architecture, and hydrodynamics. Here, we investigate the axial dispersion of linear, comb, and star polymers in parallel-plate and sinusoidal expansion-contraction microchannels at dilute concentrations using multiparticle collision dynamics simulations. The polymers all contain the same number of monomers but differ in their architecture, and their concentration is fixed at either one value that is dilute for all polymers or the same value relative to the overlap concentration for each polymer. The dispersion coefficients measured at a nominal solvent volumetric flow rate are found to depend on both architecture and concentration. We show that the dispersion coefficients collapse as a function of the P\'eclet number after accounting for confinement effects on the polymer diffusion coefficient and polymer contributions to the flow field, and the dispersion coefficients in the parallel-plate microchannel can be reasonably predicted using a theory that accounts for inhomogeneous distribution of the polymers in the microchannel.

cond-mat.soft↗

arXiv2026

Effect of Pre-Shear and Dispersity on Crystallization of a Model Polymer with Soft Pair Interactions using Molecular Dynamics Simulations

Polymer crystallization is a process of great interest in both fundamental theory and industrial settings, particularly in polymer processing and applications involving semi-crystalline materials. The effect of processing on the initial stages of crystallization is not fully understood. Our study investigates the influence of pre-shear on monodisperse melts and bidisperse blends of a generic, segmentally coarse-grained polymer model. Through molecular dynamics simulations, we explore how polydispersity affects crystallization, where we found that the addition of short chains to a melt of longer chains increased the final crystallinity by about 10%, and increased the initial growth rate by roughly a factor of two. In contrast, however, pre-shearing the hot melt before quenching only showed a minor increase in both growth rates and final crystallinty, except in monodisperse melts of short chains. Crystal grain shapes were most influenced by pre-shearing monodisperse melts, where both asphericity and prolateness decreased. Additionally, we determined topological connectivity of crystal grains through tie- and loop-chain analysis. Again, only monodisperse melts showed a significant increase of tie chain fractions with pre-shear, while all other systems showed only modest increases. Our findings provide insight into the changes of crystallinity and cluster morphologies that emerge when pre-sheared, offering a deeper understanding of the initial crystallization processes in polymer melts when subjected to pre-shear.

cond-mat.soft↗

arXiv2026

Roadmap for Condensates in Cell Biology

Biomolecular condensates govern essential cellular processes yet elude description by traditional equilibrium models. This roadmap, distilled from structured discussions at a workshop and reflecting the consensus of its participants, clarifies key concepts for researchers, funding bodies, and journals. After unifying terminology that often separates disciplines, we outline the core physics of condensate formation, review their biological roles, and identify outstanding challenges in nonequilibrium theory, multiscale simulation, and quantitative in-cell measurements. We close with a forward-looking outlook to guide coordinated efforts toward predictive, experimentally anchored understanding and control of biomolecular condensates.

physics.bio-ph↗

arXiv2025

Machine-Learning Potentials for Efficient Simulations of Anisotropic Colloids

Simulating interactions between non-spherical colloidal particles is computationally challenging due to the complex dependency of forces and energies on their geometry. We introduce and evaluate both descriptor-based and end-to-end models for predicting interaction energies and forces. Then, we compare various descriptors coupled with different regression models, like Behler-Parinello descriptors, Smooth Overlap of Atomic Positions, and neuroevolution potential, as well as multiple end-to-end models, namely SchNet, DimeNet, and DimeNet++. Among these, the neuroevolution potential (NEP) offers an optimal balance between accuracy and computational efficiency. NEP, originally developed for atomistic systems, represents interactions between rigid anisotropic bodies using point clouds, which enables the representation of any arbitrary shape. Molecular dynamics simulations using NEP, accurately reproduced structural properties across diverse particle shapes including cubes, tetrahedra, pentagonal bipyramids, and twisted cylinders, while achieving roughly up to an order-of-magnitude speedup over other methods. Additionally, we show that the extension of the method to multi-face shapes with different interactions on their surface is straightforward. We used a twisted cylinder, which lacked any point group symmetry, to demonstrate the flexibility and accuracy of NEP. Our approach enables scalable simulations of complex colloidal systems and can potentially help to facilitate efficient studies on shape dependent interactions and phase behavior in the future.

cond-mat.soft↗

arXiv2025

Mesoscale particle-based simulations of flow in expansion-contraction microchannels at low Reynolds number

We computationally study the flow of Newtonian fluids through sinusoidal expansion-contraction microchannels at low Reynolds number. We first use a perturbation method to analytically derive series solutions for the stream function and volumetric flow rate that extend prior work [P.K. Kitanidis and B.B. Dykaar, Transport in Porous Media 26, 89-98 (1997)] up to tenth order. We then employ two particle-based mesoscale methods, dissipative particle dynamics (DPD) and multiparticle collision dynamics (MPCD), to simulate the same flows. We find that the fluid velocity at the expansion and contraction points as well as the volumetric flow rate are in good agreement between DPD, MPCD, and the fourth-order series solution for a wide range of microchannel geometries. The mesoscale fluid models exhibit some slip at the walls, leading to a small but consistent overprediction of the velocity and volumetric flow rate. The series solution fails for short microchannel lengths and large amplitudes; we identify lengths and amplitudes for which it converges to a given order. Overall, we find that DPD and MPCD are convenient and reasonably accurate methods, particularly for microchannel geometries where the series solution fails or is cumbersome to implement.

physics.flu-dyn↗

arXiv2025

Simulating Curved Lipid Membranes Using Anchored Frozen Patches

Lipid bilayers often form high-curvature configurations due to self-assembly conditions or certain biological processes. However, particle-based simulations of lipid membranes are predominantly of flat lipid membranes because planar membranes are easily connected over periodic boundary conditions. To simulate a curved lipid membrane, one can simulate an entire vesicle, a cylinder, or a bicelle (disk-like bilayer aggregate). One can also use artificial methods to control curvature, such as applying virtual walls of beads, radial harmonic potentials, or ``tape up the edges''. These existing methods have limitations due to the method by which curvature is imposed. Herein, we propose an alternative method of introducing arbitrary curvature by anchoring a curved lipid membrane with ``frozen'' equilibrated membrane patches. The method presented here is compatible with all particle-based lipid models and easily extended to many geometries. As an example, we simulate curved membranes with DPPC, DOPC, DLPC and DOPE lipids as parameterized by the Martini3 coarse-grained model. This method introduces limited finite-size artifacts, prevents lipid flip-flop at membrane edges, and allows fluctuations of the free membrane center. We provide verification of the method on flat membranes and discussion on extracting shape and per-leaflet quantities (thickness, order parameter) from curved membranes. Curvature produces asymmetric changes in lipid leaflet properties. Finally, we explore the coupled effect of curvature and membrane asymmetry in both number and lipid type. We report the resulting unique morphologies (inducing gel phase, faceting) and behaviors (thickness dependent on adjacent leaflet type) that are accessible with this method.

cond-mat.soft↗

arXiv2024

Molecular Dynamics Simulations of Anisotropic Particles Accelerated by Neural-Net Predicted Interactions

Rigid bodies, made of smaller composite beads, are commonly used to simulate anisotropic particles with molecular dynamics or Monte Carlo methods. To accurately represent the particle shape and to obtain smooth and realistic effective pair interactions between two rigid bodies, each body may need to contain hundreds of spherical beads. Given an interacting pair of particles, traditional MD methods calculate the inter-body distances between the beads of the rigid bodies within a certain distance. For a system containing many anisotropic particles, distance calculations are computationally costly and limit the attainable system size and simulation time. However, the effective interaction between two rigid particles only depends on the distance between their center of masses and their relative orientation. Therefore, a function directly mapping the center of mass distance and orientation to the interaction energy between the two rigid bodies, would completely bypass inter-bead distance calculations. It is challenging to derive such a general function analytically for most non-spherical rigid bodies. We have trained neural nets, powerful tools to fit nonlinear functions to complex datasets, to achieve this task. The pair configuration is taken as input and the energy, forces and torques between two rigid particles are predicted directly. We show that MD simulations of cubes and cylinders performed with forces and torques obtained from the gradients of the energy neural-nets quantitatively match traditional simulations that uses composite rigid bodies. Both structural quantities and dynamic measures are in agreement, while achieving up to 23 times speed up over traditional molecular dynamics, depending on hardware and system size. The method can, in principle, be applied to any irregular shape with any pair interaction, provided that sufficient training data can be obtained.

cond-mat.soft↗

arXiv2023

Influence of Shape on Heteroaggregation of Model Microplastics: A Simulation Study

Nano- and microplastics are a growing threat for the environment, especially in aqueous habitats. For assessing the influence on the ecosystem and possible solution strategies, it is necessary to investigate the fate of microplastics (MP) in the environment. MPs are typically surrounded by natural organic matter, which can cause them to aggregate. However, the effect of MP shape and flow conditions on this heteroaggregation is not well understood. To address this gap, we perform simulations of heteroaggregation of different MP shapes with smaller spherical organic matter. We demonstrate that the shape had a strong impact on the aggregate structure. MPs with mostly smooth surfaces formed compact structures with a large number of neighbors with weak connection strength and a higher fractal dimension. MPs with edges and corners aggregated into more fractal structures with fewer neighbors, but with stronger connections. Using MPCD, we investigated aggregates under shear flow. The critical shear rate at which the aggregates break up is much larger for spherical and rounded cube MPs, i.e, the compact aggregate structure of spheres outweighs their weaker connection strength. Most notably, the rounded cube exhibited unexpectedly high resistance against breakup under shear. We attribute this to being fairly compact due to weaker, flexible neighbor connections, which are still strong enough to prevent particles to break off during shear flow. Irrespective of the stronger connections between neighbouring MPs, the fractal aggregates of cubes break up at lower shear rates. We find that cube aggregates reduced their radius of gyration significantly, indicating restructuring, while most neighbor connections were kept intact. Aggregates of spheres, however, kept their overall size while undergoing local rearrangements, that broke a significant portion of their neighbor interactions.

cond-mat.soft↗

arXiv2022

Predicting aggregate morphology of sequence-defined macromolecules with Recurrent Neural Networks

Self-assembly of dilute sequence-defined macromolecules is a complex phenomenon in which the local arrangement of chemical moieties can lead to the formation of long-range structure. The dependence of this structure on the sequence necessarily implies that a mapping between the two exists, yet it has been difficult to model so far. Predicting the aggregation behavior of these macromolecules is challenging due to the lack of effective order parameters, a vast design space, inherent variability, and high computational costs associated with currently available simulation techniques. Here, we accurately predict the morphology of aggregates self-assembled from sequence-defined macromolecules using supervised machine learning. We find that regression models with implicit representation learning perform significantly better than those based on engineered features such as $k$-mer counting, and a Recurrent-Neural-Network-based regressor performs the best out of nine model architectures we tested. Furthermore, we demonstrate the high-throughput screening of monomer sequences using the regression model to identify candidates for self-assembly into selected morphologies. Our strategy is shown to successfully identify multiple suitable sequences in every test we performed, so we hope the insights gained here can be extended to other increasingly complex design scenarios in the future, such as the design of sequences under polydispersity and at varying environmental conditions.

cond-mat.soft↗

arXiv2021

Computational Study of Mechanochemical Activation in Nanostructured Triblock Copolymers

Force-driven chemical reactions have emerged as an attractive platform for diverse applications in polymeric materials. However, the network topologies necessary for efficiently transducing macroscopic forces to the molecular scale are not well-understood. In this work, we use coarse-grained molecular dynamics simulations to investigate the impact of network topology on mechanochemical activation in a self-assembled triblock copolymers. We find that mechanochemical activation during tensile deformation depends strongly on both the polymer composition and chain conformation in these materials, with activation requiring higher stress in materials with a higher glassy block content, and most activation occurring in the tie chains connecting different glassy domains. Our work suggests that changes in the network topology significantly impact mechanochemical activation efficiencies in these materials, suggesting that this area will be a fruitful avenue for further experimental research.

cond-mat.soft↗

arXiv2021

Unsupervised learning of sequence-specific aggregation behavior for a model copolymer

We apply a recently developed unsupervised machine learning scheme for local atomic environments to characterize large-scale, disordered aggregates formed by sequence-defined macromolecules. This method provides new insight into the structure of these disordered, dilute aggregates, which has proven difficult to understand using collective variables manually derived from expert knowledge. In contrast to such conventional order parameters, we are able to classify the global aggregate structure directly using descriptions of the local environments. The resulting characterization provides a deeper understanding of the range of possible self-assembled structures and their relationships to each other. We also provide a detailed analysis of the effects of finite system size, stochasticity, and kinetics of these aggregates based on the learned collective variables. Interestingly, we find that the spatiotemporal evolution of systems in the learned latent space is smooth and continuous, despite being derived from only a single snapshot from each of about 1000 monomer sequences. These results demonstrate the insight which can be gained by applying unsupervised machine learning to soft matter systems, especially when suitable order parameters are not known.

cond-mat.soft↗

arXiv2020

Stability of force-driven shear flows in nonequilibrium molecular simulations with periodic boundaries

We analyze the hydrodynamic stability of force-driven parallel shear flows in nonequilibrium molecular simulations with three-dimensional periodic boundary conditions. We show that flows simulated in this way can be linearly unstable, and we derive an expression for the critical Reynolds number as a function of the geometric aspect ratio of the simulation domain. Approximate periodic extensions of Couette and Poiseuille flows are unstable at Reynolds numbers two orders of magnitude smaller than their aperiodic equivalents because the periodic boundaries impose fundamentally different constraints on the flow. This instability has important implications for simulating shear rheology and for designing nonequilibrium simulation methods that are compatible with periodic boundary conditions.

physics.flu-dyn↗

arXiv2019

Model for disordered proteins with strongly sequence-dependent liquid phase behavior

Phase separation of intrinsically disordered proteins is important for the formation of membraneless organelles, or biomolecular condensates, which play key roles in the regulation of biochemical processes within cells. In this work, we investigated the phase separation of different sequences of a coarse-grained model for intrinsically disordered proteins and discovered a surprisingly rich phase behavior. We studied both the fraction of total hydrophobic parts and the distribution of hydrophobic parts. Not surprisingly, sequences with larger hydrophobic fractions showed conventional liquid-liquid phase separation. The location of the critical point was systematically influenced by the terminal beads of the sequence, due to changes in interfacial composition and tension. For sequences with lower hydrophobicity, we observed not only conventional liquid-liquid phase separation, but also reentrant phase behavior, in which the liquid phase density decreases at lower temperatures. For some sequences, we observed formation of open phases consisting of aggregates, rather than a normal liquid. These aggregates had overall lower densities than the conventional liquid phases, and exhibited complex geometries with large interconnected string-like or membrane-like clusters. Our findings suggest that minor alterations in the ordering of residues may lead to large changes in the phase behavior of the protein, a fact of significant potential relevance for biology.

cond-mat.soft↗

arXiv2019

Quantized bounding volume hierarchies for neighbor search in molecular simulations on graphics processing units

We present an algorithm for neighbor search in molecular simulations on graphics processing units (GPUs) based on bounding volume hierarchies (BVHs). The BVH is compressed into a low-precision, quantized representation to increase the BVH traversal speed compared to a previous implementation. We find that neighbor search using the quantized BVH is roughly two to four times faster than current state-of-the-art methods using uniform grids (cell lists) for a suite of benchmarks for common molecular simulation models. Based on the benchmark results, we recommend using the BVH instead of a single cell list for neighbor list generation in molecular simulations on GPUs.

physics.comp-ph↗

arXiv2018

Unexpected secondary flows in reverse nonequilibrium shear flow simulations

We simulated two particle-based fluid models, namely multiparticle collision dynamics and dissipative particle dynamics, under shear using reverse nonequilibrium simulations (RNES). In cubic periodic simulation boxes, the expected shear flow profile for a Newtonian fluid developed, consistent with the fluid viscosities. However, unexpected secondary flows along the shear gradient formed when the simulation box was elongated in the flow direction. The standard shear flow profile was obtained when the simulation box was longer in the shear-gradient dimension than the flow dimension, while the secondary flows were always present when the flow dimension was at least 25% larger than the shear-gradient dimension. The secondary flows satisfy the boundary conditions imposed by the RNES and have a lower rate of viscous dissipation in the fluid than the corresponding unidirectional flows. This work highlights a previously unappreciated limitation of RNES for generating shear flow in simulation boxes that are elongated in the flow dimension, an important consideration when applying RNES to complex fluids like polymer solutions.

physics.flu-dyn↗

arXiv2018

Influence of hydrodynamic interactions on stratification in drying mixtures

Nonequilibrium molecular dynamics simulations are used to investigate the influence of hydrodynamic interactions on vertical segregation (stratification) in drying mixtures of long and short polymer chains. In agreement with previous computer simulations and theoretical modeling, the short polymers stratify on top of the long polymers at the top of the drying film when hydrodynamic interactions between polymers are neglected. However, no stratification occurs at the same drying conditions when hydrodynamic interactions are incorporated through an explicit solvent model. Our analysis demonstrates that models lacking hydrodynamic interactions do not faithfully represent stratification in drying mixtures, in agreement with recent analysis of an idealized model for diffusiophoresis, and must be incorporated into such models in future.

cond-mat.soft↗

arXiv2017

Molecular dynamics simulations in hybrid particle-continuum schemes: Pitfalls and caveats

Heterogeneous multiscale methods (HMM) combine molecular accuracy of particle-based simulations with the computational efficiency of continuum descriptions to model flow in soft matter liquids. In these schemes, molecular simulations typically pose a computational bottleneck, which we investigate in detail in this study. We find that it is preferable to simulate many small systems as opposed to a few large systems, and that a choice of a simple isokinetic thermostat is typically sufficient while thermostats such as Lowe-Andersen allow for simulations at elevated viscosity. We discuss suitable choices for time steps and finite-size effects which arise in the limit of very small simulation boxes. We also argue that if colloidal systems are considered as opposed to atomistic systems, the gap between microscopic and macroscopic simulations regarding time and length scales is significantly smaller. We also propose a novel reduced-order technique for the coupling to the macroscopic solver, which allows us to approximate a non-linear stress-strain relation efficiently and thus further reduce computational effort of microscopic simulations.

cond-mat.soft↗