CyberSec Research

A disordered quasi-liquid layer of water is thought to cover the ice surface, but many issues, such as its onset temperature, its thickness, or its actual relation to bulk liquid water have been a matter of unsettled controversy for more than a century. In this perspective article, current computer simulations and experimental results are discussed under the light of a suitable theoretical framework. It is found that using a combination of wetting physics, the theory of intermolecular forces, statistical mechanics and out of equilibrium physics a large number of conflicting results can be reconciled and collected into a consistent description of the ice surface. This helps understand the crucial role of surface properties in a range of important applications, from the enigmatic structure of snow crystals to the slipperiness of ice.

Electro-viscoelastic theory for polymer melts has been extensively studied experimentally for the past century, primarily for manufacturing purposes. However, the modeling and theory for this have been minimal, leaving many questions on the mechanisms and behavior of an arbitrary flow scheme. To remedy this, previously solved overdamped Langevin equations for the Doi-Rouse model are modified to include charge and electric field potential forces. The charge sequence on the chain is hypothesized to be a cosine sequence along the chain, resembling multiple electric dipoles that conveniently correspond to a Rouse mode of the chain. These are then solved for the shear stress under homogeneous shear rates and electric fields to find directional viscosity increases depending on the shearing and electric field orientation. Using the newly derived shear stress from the Doi-Rouse approach, a continuum model is proposed that resembles a modified upper-convected Maxwell model, including polarization stresses in terms of an electric field dyadic. This new continuum model, named the upper-convected electro-Maxwell model, is verified using Kremer-Grest polymer chains simulated with molecular dynamics for multiple flow schemes and a specified charge sequence along the chain. Furthermore, the MD results verified the difference in the overall and charge sequence relaxation times through the shear and normal stress polarizations, showing the necessity for the upper-convected derivative of the electric field dyadic to correct the viscosity scaling. Finally, the dynamic properties of the polarized polymer melt are examined analytically, finding that the phase shift is unaffected by the electric field contribution.

Inspired by biological systems, we introduce a general framework for quasi-static shape control of human-scale structures under slowly varying external actions or requirements. In this setting, shape control aims to traverse the stable sub-manifolds of the equilibrium set to meet some predefined requirements or optimization criteria. As finite deformations are allowed, the equilibrium set may have a non-trivial topology. This paper explores the implications of large shape changes and high compliance, such as the emergence of unstable equilibria and equilibrium sets with non-trivial topology. We identify various adaptivity scenarios, ranging from inverse kinematics to optimization and path planning problems, and discuss the role of time-dependent loads and requirements. The applicability of the proposed concepts is demonstrated through the example of a curved Kirchhoff rod that is susceptible to snap-through behavior.

A systematic comparison was carried out to assess the influence of representative thermostat methods in constant-temperature molecular dynamics simulations. The thermostat schemes considered include the Nos\'e--Hoover thermostat and its chain generalisation, the Bussi velocity rescaling method, and several implementations of the Langevin dynamics. Using a binary Lennard-Jones liquid as a model glass former, we investigated how the sampling of physical observables, such as particle velocities and potential energy, responds to changes in time step across these thermostats. While the Nos\'e--Hoover chain and Bussi thermostats provide reliable temperature control, a pronounced time-step dependence was observed in the potential energy. Amongst the Langevin methods, the Gr{\o}nbech-Jensen--Farago scheme provided the most consistent sampling of both temperature and potential energy. Nonetheless, Langevin dynamics typically incurs approximately twice the computational cost due to the overhead of random number generation, and exhibits a systematic decrease in diffusion coefficients with increasing friction. This study presents a broad comparison of thermostat methods using a binary Lennard-Jones glass-former model, offering practical guidance for the choice of thermostats in classical molecular dynamics simulations. These findings provide useful insights for diverse applications, including glass transition, phase separation, and nucleation.

Molecular dynamics computer simulations have been conducted on neat liquid methanol, using three different united atom (three site) interatomic potentials: TraPPE [J. Phys. Chem. B 105, 3093 (2001)], UAM-I [J. Mol. Liq. 323, 114576 (2021)] and OPLS/2016 [J. Chem. Phys. 145, 034508 (2016)]. The effects of pressure, between 1 bar and 6 kbar, have been evaluated on total scattering structure factors, partial radial distribution functions, as well as on collective characteristics such as ring size distributions and cluster size distributions. Agreement with experimental density is nearly quantitative for all the three force fields, and major trends observed for recent pressure dependent neutron diffraction data are reproduced qualitatively. The OPLS/2016 force field, in general, generates properties that are markedly different from results originating from the other potentials. Pressure effects are hardly noticeable on most partial radial distribution functions and on the distribution of the number of hydrogen bonded neighbours. On the other hand, collective structural properties, like cluster- and ring size distributions exhibit significant changes with increasing pressure: larger clusters become more numerous, whereas the number of cyclic clusters, i.e., rings, decrease. The self-diffusion coefficient decreases with increasing pressure, and the same is valid for the average lifetime of hydrogen bonds.

We employ cell dynamics simulation based on the CH/BD model to investigate the self-assembly behavior of a mixed system consisting of diblock copolymers (AB), homopolymers (C), and Janus nanorods. The results indicate that, at different component ratios, the mixed system undergoes various phase transitions with an increasing number of nanorods. Specifically, when the homopolymer component is 0.40, the mixed system transitions from a disordered structure to a parallel lamellar structure, subsequently to a tilted layered structure, and ultimately to a perpendicular lamellar structure as the number of nanorods increases. To explore this phenomenon in greater depth, we conduct a comprehensive analysis of domain sizes and pattern evolution. Additionally, we investigate the effects of the repulsive interaction strength between polymers, wetting strength, length of nanorods, and degree of asymmetry on the self-assembly behavior of the mixed system. This research provides significant theoretical and experimental insights for the preparation of novel nanomaterials.

Crystals and other condensed phases are defined primarily by their inherent symmetries, which play a crucial role in dictating their structural properties. In crystallization studies, local order parameters (OPs) that describe bond orientational order are widely employed to investigate crystal formation. Despite their utility, these traditional metrics do not directly quantify symmetry, an important aspect for understanding the development of order during crystallization. To address this gap, we introduce a new set of OPs, called Point Group Order Parameters (PGOPs), designed to continuously quantify point group symmetry order. We demonstrate the strength and utility of PGOP in detecting order across different crystalline systems and compare its performance to commonly used bond-orientational order metrics. PGOP calculations for all non-infinite point groups are implemented in the open-source package SPATULA (Symmetry Pattern Analysis Toolkit for Understanding Local Arrangements), written in parallelized C++ with a Python interface. The code is publicly available on GitHub.

Diffusive motion is a fundamental transport mechanism in physical and biological systems, governing dynamics across a wide range of scales -- from molecular transport to animal foraging. In many complex systems, however, diffusion deviates from classical Brownian behaviour, exhibiting striking phenomena such as Brownian yet non-Gaussian diffusion (BYNGD) and anomalous diffusion. BYNGD describes a frequently observed statistical feature characterised by the coexistence of linear mean-square displacement (MSD) and non-Gaussian displacement distributions. Anomalous diffusion, in contrast, involves a nonlinear time dependence of the MSD and often reflects mechanisms such as trapping, viscoelasticity, heterogeneity, or active processes. Both phenomena challenge the conventional framework based on constant diffusivity and Gaussian statistics. This review focuses the theoretical modelling of such behaviour via the Langevin equation with fluctuating diffusivity (LEFD) -- a flexible stochastic framework that captures essential features of diffusion in heterogeneous media. LEFD not only accounts for BYNGD but also naturally encompasses a wide range of anomalous transport phenomena, including subdiffusion, ageing, and weak ergodicity breaking. Ergodicity is discussed in terms of the correspondence between time and ensemble averages, as well as the trajectory-to-trajectory variability of time-averaged observables. The review further highlights the empirical relevance of LEFD and related models in explaining diverse experimental observations and underscores their value to uncovering the physical mechanisms governing transport in complex systems.

In this letter, we characterize quantitatively the complex phenomenon of debubbling via aerophilic membranes by examining local interactions at the scale of single bubbles. We identify three asymptotic limits of evacuation dictated by Rayleigh, Ohnesorge and Darcy dynamics, the physics of which we capture using simple scaling laws. We show that beyond a threshold permeability, bubble evacuations become constant in time - a feature we understand as an inertio-capillary limit. Our experiments reveal that the fastest bubble evacuations require an interface that is nearly a liquid, but not quite.

The ability of virus shells to encapsulate a wide range of functional cargoes, especially multiple cargoes - siRNAs, enzymes, and chromophores - has made them an essential tool in biotechnology for advancing drug delivery applications and developing innovative new materials. Here we present a mechanistic study of the processes and pathways that lead to multiple cargo encapsulation in the co-assembly of virus shell proteins with ligand-coated nanoparticles. Based on the structural identification of different intermediates, enabled by the contrast in electron microscopy provided by the metal nanoparticles that play the cargo role, we find that multiple cargo encapsulation occurs by self-assembly via a specific ``assembly line'' pathway that is different from previously described \emph{in vitro} assembly mechanisms of virus-like particles (VLP). The emerging model explains observations that are potentially important for delivery applications, for instance, the pronounced nanoparticle size selectivity.

Understanding and predicting the glassy dynamics of small organic molecules is critical for applications ranging from pharmaceuticals to energy and food preservation. In this work, we present a theoretical framework that combines molecular dynamics simulations and Elastically Collective Nonlinear Langevin Equation (ECNLE) theory to predict the structural relaxation behavior of small organic glass-formers. By using propanol, glucose, fructose, and trehalose as model systems, we estimate the glass transition temperature (Tg) from stepwise cooling simulations and volume-temperature analysis. These computed Tg values are then inserted into the ECNLE theory to calculate temperature-dependent relaxation times and diffusion coefficients. Numerical results agree well with experimental data in prior works. This approach provides a predictive and experimentally-independent route for characterizing glassy dynamics in molecular materials.

Ferroelectric nematic fluids are promising materials for tunable nonlinear photonics, with applications ranging from second harmonic generation to sources of entangled photons. However, the few reported values of second-order susceptibilities vary widely depending on the molecular architecture. Here, we systematically measure second-order NLO susceptibilities of five different materials that exhibit the ferroelectric nematic phase, as well as the more recently discovered layered smectic A ferroelectric phase. The materials investigated include archetypal molecular architectures as well as mixtures showing room-temperature ferroelectric phases. The measured values, which range from 0.3 to 20 pm/V, are here reasonably predicted by combining calculations of molecular-level hyperpolarizabilities and a simple nematic potential, highlighting the opportunities of modelling-assisted design for enhanced NLO ferroelectric fluids.

Nanofluidic memristors promise brain-inspired information processing with ions, yet their microscopic origin remains debated. So far, ionic memory has been attributed to ion-specific interactions, dynamic wetting, chemical reactions or mechanical deformations, yet typically without direct evidence. Here, by combining operando interferometric imaging with electrokinetic measurements, we directly visualize voltage-induced blistering of the confining walls of two-dimensional (2D) nanochannels, as key origin of memristive hysteresis. We identify two distinct classes of blisters: unidirectional, driven by electrostatic forces on surface charges, and bidirectional, arising from osmotic pressure due to concentration polarization. This mechanistic framework explains device evolution and device-to-device variability, and reframes stochastic blistering as a functional design element. Our results constitute a direct proof of electromechanical coupling as a robust pathway to ionic memory in 2D nanochannels and open routes toward high-performance ionic memristors and electrically actuated nanofluidic valves.

Stochastic resetting is a powerful strategy known to accelerate the first-passage time statistics of stochastic processes. While its effects on Markovian systems are well understood, a general framework for non-Markovian dynamics is still lacking, mostly due to its mathematical complexity. Here, we present an analytical and numerical framework to study non-Markovian processes under resetting, focusing on the first-passage properties of escape kinetics from metastable states. We show that resetting disrupts the inherent time correlation, inducing Markovianity, thereby leading to an efficient escape mechanism. This work, therefore, provides a much needed theoretical approach for incorporating resetting into complex chemical and biological processes, which follow non-Markovian dynamics.

We present an overdamped continuum description of oriented active solids in which interactions respect the symmetries of space but do not obey the principle of action and reaction. Taking position and orientation as kinematic variables, we examine the conservation of the linear and angular momentum variables in an elementary volume. We find that nonreciprocal interactions yield, in addition to the areal stresses and moment stresses of classical elasticity, volumetric forces and torques that act as local sources of momentum and angular momentum. Since, by symmetry, these can only depend on the strains, nonreciprocity requires the extension of constitutive modeling to strain-dependent volumetric forces and torques. Using Cartan's method of moving frames and Curie's principle, we derive the materially linear constitutive law that underpins the nonreciprocal, geometrically nonlinear elasticity of the continuum. We study this constitutive law exhaustively for a one-dimensional active solid and identify striking nonreciprocal effects - traveling waves, linear instabilities, spontaneous motion of and about the center of mass - that are absent in a passive, reciprocally interacting solid. Numerical simulations of a particulate active solid model, consisting of a linear assembly of hydrodynamically interacting active particles, yields long-wavelength behavior that is in excellent agreement with theory. Our study provides the foundation for a principled macroscopic mechanics of oriented active solids with symmetry-invariant, nonreciprocal microscopic interactions.

The twice-activation method for analysis of experimental viscosity-temperature data reveals a set of interconnected parameters describing the state of the flowing glass-forming liquid in terms of convergation point which describes an infinite set of viscosity-temperature relations for the liquid considered. The observed uncertainty in the viscosity-temperature behavior permits to consider glass-forming liquid as the self-organizing system realizing by the bonds wave as dissipative pattern. The acoustic bond wave and the switching bond wave are considered generally and in different groups of glass-formers: inorganic, organic and polymers. The demonstrated correlation between two coordinates of convergation point and kinetic and thermodynamic measures of fragility permits to resolve the problem of the measures discrepancy.

We study the transport of active Brownian particles (ABPs) in three-dimensional (3D) oscillatory geometries, which are spatially periodic. We establish a generalized Fick-Jacobs approach, which reduces a 3D system to an effective 1D system based on the assumption that a fast equilibration of particles along the transversal directions of the geometry. The transport characteristics of ABPs are computed semi-analytically and corroborated by numerical simulations. At the optimal frequency of the geometry oscillation, particles exhibit higher average velocity $\langle v \rangle$ and effective diffusion coefficient $D_{\text{eff}}$, resembling the phenomena of stochastic resonance. This effect is further enhanced by the self-propelled velocity of ABPs and the amplitude of geometry oscillations. These findings have significant implications for the development of micro- and nanofluidic devices with enhanced control over particle transport and precise manipulation of small-scale biomedical devices.

Polymer reference interaction site model (PRISM) theory, a descendent of Ornstein-Zernike liquid state theory, is a powerful tool to predict the structure and thermodynamics of equilibrium polymer systems, but its accuracy and applicability can be limited in some important cases. Typically, these shortcomings are traced to the analytical closure relationships used to solve the integral equations. Here, we propose a machine learning (ML)-based closure relation trained on a dataset of coarse-grained molecular dynamics simulations of homopolymer melts and solutions. PRISM theory with the ML closure outperforms traditional atomic closures (e.g., Percus-Yevick) in predicting the structure of typical coarse-grained model systems. We also use the ML closure to accurately model the results of small-angle neutron scattering experiments. This ML-enhanced PRISM theory can therefore enable rapid soft materials discovery and design efforts.