arXiv subjects

Vagelis Harmandaris

Publications and source records attributed to Vagelis Harmandaris.

11 recordsLinked to original sources

arXiv2026

Ion-Modulated Polyelectrolyte Complexation of DNA and Polyacrylic Acid from Molecular Dynamics Simulations

The formation of complexes between like-charged polyelectrolytes challenges conventional electrostatic intuition and highlights the central role of ions in mediating macromolecular organization. Here, we investigate the salt-dependent association of DNA with poly(acrylic acid) (PAA) using atomistic molecular dynamics simulations in NaCl, MgCl$_2$, and CaCl$_2$ solutions. A time-resolved state classification scheme, based on heavy-atom distance and hydrogen-bond formation, was applied to distinguish bound and unbound configurations, enabling quantitative analysis of how ion valency modulates complex stability and structure. The results reveal a clear hierarchy of association strength with Ca$^{2+}$ promoting persistent complex formation through direct inner-sphere coordination between DNA phosphates and PAA carboxylates, Mg$^{2+}$ mediating weaker, transient bridging interactions and Na$^+$ exhibiting only electrostatic screening action with negligible bridge formation. Structural analysis shows that multivalent ions not only enhance complex stability but also reshape the molecular organization of both macromolecules. Ca$^{2+}$ induces expansion of DNA and compaction of PAA within a strongly bridged complex characterized by directional alignment and backbone-dominated binding, whereas Mg$^{2+}$ promotes more transient groove associations and Na$^+$ supports flexible, weakly correlated contacts. Our findings provide molecular-level insight into ion-specific mechanisms underlying polyelectrolyte organization and inform the design of responsive biomaterials and nucleic acid-based assemblies in multivalent ionic environments.

cond-mat.soft↗

arXiv2025

Reconstructing the Magnetic Field in an Arbitrary Domain via Data-driven Bayesian Methods and Numerical Simulations

Inverse problems are prevalent in numerous scientific and engineering disciplines, where the objective is to determine unknown parameters within a physical system using indirect measurements or observations. The inherent challenge lies in deducing the most probable parameter values that align with the collected data. This study introduces an algorithm for reconstructing parameters by addressing an inverse problem formulated through differential equations underpinned by uncertain boundary conditions or variant parameters. We adopt a Bayesian approach for parameter inference, delineating the establishment of prior, likelihood, and posterior distributions, and the subsequent resolution of the maximum a posteriori problem via numerical optimization techniques. The proposed algorithm is applied to the task of magnetic field reconstruction within a conical domain, demonstrating precise recovery of the true parameter values.

physics.comp-ph↗

arXiv2024

Relaxation Dynamics of a Liquid in the Vicinity of an Attractive Surface: The Process of Escaping from the Surface

We analyze the displacements of the particles of a glass-forming molecular liquid perpendicular to a confining solid surface, using extensive molecular dynamics simulations with atomistic models. In the vicinity of an attractive surface, the liquid molecules are trapped. Transient localization of liquid molecules near the surface introduces a relaxation process, related to the escape of molecules from the surface, into the dynamics of the interfacial liquid layer. To describe this process, we analyze several dynamical observables of the confined liquid. The self-intermediate scattering function and the mean-squared displacement of the particles located in the interfacial layer are dominated by the process of escaping from the surface. This relaxation process is also associated with a strong heterogeneity in the mobility of the interfacial particles. The studied model liquid is hydrogenated methyl methacrylate. For the confining wall, we consider different models, namely a periodic single layer of graphene and a frozen amorphous configuration of the bulk liquid (frozen wall). Near graphene, where the liquid molecules form a layered structure and adopt parallel-to-surface orientation, a clear separation between small-scale movements of the molecules near the surface and the process of escaping from the surface is observed. This is reflected in the three-step relaxation of the interfacial layer. However, near the frozen wall, where the liquid molecules do not have a preferential alignment, a clear three-step relaxation is not seen, even though the dynamical quantities are controlled by the process of escaping from the surface.

cond-mat.soft↗

arXiv2021

From Order to Disorder of Alkanethiol SAMs on Complex Au (211), (221) and (311) Surfaces: Impact of the Substrate

We investigate the impact of the substrate on the structural properties and the morphology of alkanethiol self-assembled monolayers (SAMs) on gold, using first principles calculations and atomistic molecular dynamics simulations. We consider hexadecanethiols on Au(211), Au(221) and Au(311) surfaces which contain few-atom wide terraces separated by monoatomic steps similar to the complex Au surfaces used in experiments. The structure of the SAMs is probed via several structural properties including tilt angles, mean C atom heights from the surface, precession angles, gauche defects, gyration tensors and relative shape anisotropy. Comparing these properties to those of the well-studied SAMs on Au(111), we observe similarities but also striking differences. A clear order to disorder transition is observed by changing the substrate: well-ordered SAMs on (111) and (211) surfaces become mixed ordered-disordered structures on (311) and fully disordered on (221). The presence of steps on the Au surfaces also results in preferential tilt orientations with long-range order. Our results show that in addition to the expected grafting density dependence, the transition from order to disorder crucially depends on substrate morphology. The onset of ordering behavior is related to the atomic structure of the surface. The key parameter that affects long-range order is the energy for changing the dihedral angle between Au-S-$\mathrm{C^{(1)}-C^{(2)}}$ of the adsorbed alkanethiol.

cond-mat.mtrl-sci↗

arXiv2020

Dynamical heterogeneities in non-entangled polystyrene and poly(ethylene oxide) star melts

Star polymers can exhibit a heterogeneous dynamical behavior due to their internal structure. In this work we employ atomistic molecular dynamics simulations to study translational motion in non-entangled polystyrene and poly(ethylene oxide) star-shaped melts. We focus on the local heterogeneous dynamics originating from the multi-arm star-like architecture and quantify the intramolecular dynamical gradient. By examining the translational motion at length scales of the order of the Kuhn length, we aim to find common features for both studied chemistries and to provide a critical and direct comparison with theoretical models of polymer dynamics. We discuss the observed tendencies with respect to the continuous Rouse model adjusted for the star-like architectures. Two versions of the Rouse model are examined: one assuming uniform friction on every Rouse bead and another one considering larger branch point friction. Apart from chain connectivity between neighboring beads, both versions disregard interactions between the chains. Despite the tolerable description of the simulation data, neither model appears to reflect the mobility gradient accurately. The detailed quantitative atomistic models employed here bridge the gap between the theoretical and general, coarse-granined models of star-like polymers which lack the indispensable chemical details.

cond-mat.soft↗

arXiv2020

Dynamics and Structure of Monolayer Polymer Crystallites on Graphene

Graphene-based nanostructured systems and van-der-Waals heterostructures comprise a material class of growing technological and scientific importance. Joining materials with vastly different properties, polymer-graphene heterosystems promise diverse applications in surface- and nanotechnology, including photovoltaics or nanotribology. Fundamentally, molecular adsorbates are prototypical systems to study confinement-induced phase transitions exhibiting intricate dynamics, which require a comprehensive understanding of the dynamical and static properties on molecular time and length scales. Here, we investigate the dynamics and the structure of a single polyethylene chain on free-standing graphene by means of molecular dynamics simulations. In equilibrium, the adsorbed polymer is orientationally linked to the graphene as two-dimensional folded-chain crystallites or, at elevated temperatures, as a floating solid. The associated superstructure can be reversibly melted on a picosecond time scale upon quasi-instantaneous substrate heating, involving ultrafast heterogeneous melting via a transient floating phase. Our findings elucidate time-resolved molecular-scale ordering and disordering phenomena in individual polymers interacting with solids, yielding complementary information to collective friction and viscosity, and linking to recent experimental observables from ultrafast electron diffraction. We anticipate that the approach will help in resolving non-equilibrium phenomena of hybrid polymeric systems over a broad range of time and length scales.

cond-mat.soft↗

arXiv2020

Data-driven Uncertainty Quantification for Systematic Coarse-grained Models

In this work, we present methodologies for the quantification of confidence in bottom-up coarse-grained models for molecular and macromolecular systems. Coarse-graining methods have been extensively used in the past decades in order to extend the length and time scales accessible by simulation methodologies. The quantification, though, of induced errors due to the limited availability of fine-grained data is not yet established. Here, we employ rigorous statistical methods to deduce guarantees for the optimal coarse models obtained via approximations of the multi-body potential of mean force, with the relative entropy, the relative entropy rate minimization, and the force matching methods. Specifically, we present and apply statistical approaches, such as bootstrap and jackknife, to infer confidence sets for a limited number of samples, i.e., molecular configurations. Moreover, we estimate asymptotic confidence intervals assuming adequate sampling of the phase space. We demonstrate the need for non-asymptotic methods and quantify confidence sets through two applications. The first is a two-scale fast/slow diffusion process projected on the slow process. With this benchmark example, we establish the methodology for both independent and time-series data. Second, we apply these uncertainty quantification approaches on a polymeric bulk system. We consider an atomistic polyethylene melt as the prototype system for developing coarse-graining tools for macromolecular systems. For this system, we estimate the coarse-grained force field and present confidence levels with respect to the number of available microscopic data.

stat.ME↗

arXiv2016

Parameterization of Coarse-grained Molecular Interactions through Potential of Mean Force Calculations and Cluster Expansions Techniques

We present a systematic coarse-graining (CG) strategy for many particle molecular systems based on cluster expansion techniques. We construct a hierarchy of coarse-grained Hamiltonians with interaction potentials consisting of two, three and higher body interactions. The accuracy of the derived cluster expansion based on interatomic potentials is examined over a range of various temperatures and densities and compared to direct computation of pair potential of mean force. The comparison of the coarse-grained simulations is done on the basis of the structural properties, against the detailed all-atom data. We give specific examples for methane and ethane molecules in which the coarse-grained variable is the center of mass of the molecule. We investigate different temperature and density regimes, and we examine differences between the methane and ethane systems. Results show that the cluster expansion formalism can be used in order to provide accurate effective pair and three-body CG potentials at high $T$ and low $ρ$ regimes. In the liquid regime the three-body effective CG potentials give a small improvement, over the typical pair CG ones; however in order to get significantly better results one needs to consider even higher order terms.

cond-mat.soft↗

arXiv2016

Path-space variational inference for non-equilibrium coarse-grained systems

In this paper, we discuss information-theoretic tools for obtaining optimized coarse-grained molecular models for both equilibrium and non-equilibrium molecular dynamics. The latter are ubiquitous in physicochemical and biological applications, where they are typically associated with coupling mechanisms, multi-physics and/or boundary conditions. In general the non-equilibrium steady states are not known explicitly as they do not necessarily have a Gibbs structure. The presented approach can compare microscopic behavior of molecular systems to parametric and non-parametric coarse-grained one using the relative entropy between distributions on the path space and setting up a corresponding path space variational inference problem. The methods can become entirely data-driven when the microscopic dynamics are replaced with corresponding correlated data in the form of time series. Furthermore, we present connections and generalizations of force matching methods in coarse-graining with path-space information methods, as well as demonstrate the enhanced transferability of information-based parameterizations to general observables due to information inequalities. We further discuss methodological connections between information-based coarse-graining of molecular systems and variational inference methods primarily developed in the machine learning community. However, we note that the work presented here addresses variational inference for correlated time series due to the focus on dynamics. The applicability of the proposed methods is demonstrated on high-dimensional stochastic processes given by Langevin, overdamped and driven Langevin dynamics of interacting particles.

math.NA↗

arXiv2015

The geometry of generalized force matching in coarse-graining and related information metrics

Using the probabilistic language of conditional expectations we reformulate the force matching method for coarse-graining of molecular systems as a projection on spaces of coarse observables. A practical outcome of this probabilistic description is the link of the force matching method with thermodynamic integration. This connection provides a way to systematically construct a local mean force in order to optimally approximate the potential of mean force through force matching. We introduce a generalized force matching condition for the local mean force in the sense that allows the approximation of the potential of mean force under both linear and non-linear coarse graining mappings (e.g., reaction coordinates, end-to-end length of chains). Furthermore, we study the equivalence of force matching with relative entropy minimization which we derive for general non-linear coarse graining maps. We present in detail the generalized force matching condition through applications to specific examples in molecular systems.

math.NA↗

arXiv2014

Parametric Sensitivity Analysis for Stochastic Molecular Systems using Information Theoretic Metrics

In this paper we extend the parametric sensitivity analysis (SA) methodology proposed in Ref. [Y. Pantazis and M. A. Katsoulakis, J. Chem. Phys. 138, 054115 (2013)] to continuous time and continuous space Markov processes represented by stochastic differential equations and, particularly, stochastic molecular dynamics as described by the Langevin equation. The utilized SA method is based on the computation of the information-theoretic (and thermodynamic) quantity of relative entropy rate (RER) and the associated Fisher information matrix (FIM) between path distributions. A major advantage of the pathwise SA method is that both RER and pathwise FIM depend only on averages of the force field therefore they are tractable and computable as ergodic averages from a single run of the molecular dynamics simulation both in equilibrium and in non-equilibrium steady state regimes. We validate the performance of the extended SA method to two different molecular stochastic systems, a standard Lennard-Jones fluid and an all-atom methane liquid and compare the obtained parameter sensitivities with parameter sensitivities on three popular and well-studied observable functions, namely, the radial distribution function, the mean squared displacement and the pressure. Results show that the RER-based sensitivities are highly correlated with the observable-based sensitivities.

cs.IT↗