From particle condensation to polymer aggregation

We draw an analogy between droplet formation in dilute particle and polymer systems. Our arguments are based on finite-size scaling results from studies of a two-dimensional lattice gas to three-dimensional bead-spring polymers. To set the results in perspective, we compare with in part rigorous theoretical scaling laws for canonical condensation in a supersaturated gas at fixed temperature, and derive corresponding scaling predictions for an undercooled gas at fixed density. The latter allows one to efficiently employ parallel multicanonical simulations and to reach previously not accessible scaling regimes. While the asymptotic scaling can not be observed for the comparably small polymer system sizes, they demonstrate an intermediate scaling regime also observable for particle condensation. Altogether, our extensive results from computer simulations provide clear evidence for the close analogy between particle condensation and polymer aggregation in dilute systems.Comment: 10 pages, 6 figure

Exploring different regimes in finite-size scaling of the droplet condensation-evaporation transition

We present a finite-size scaling analysis of the droplet condensation-evaporation transition of a lattice gas (in two and three dimensions) and a Lennard-Jones gas (in three dimensions) at fixed density. Parallel multicanonical simulations allow sampling of the required system sizes with precise equilibrium estimates. In the limit of large systems, we verify the theoretical leading-order scaling prediction for both the transition temperature and the finite-size rounding. In addition, we present an emerging intermediate scaling regime, consistent in all considered cases and with similar recent observations for polymer aggregation. While the intermediate regime locally may show a different effective scaling, we show that it is a gradual crossover to the large-system scaling behavior by including empirical higher-order corrections. This implies that care has to be taken when considering scaling ranges, possibly leading to completely wrong predictions for the thermodynamic limit. In this study, we consider a crossing of the phase boundary orthogonal to the usual fixed temperature studies. We show that this is an equivalent approach and, under certain conditions, may show smaller finite-size corrections.Comment: 12 pages, 9 figures, to appear in Phys. Rev.

Scaling Properties of Parallelized Multicanonical Simulations

We implemented a parallel version of the multicanonical algorithm and applied it to a variety of systems with phase transitions of first and second order. The parallelization relies on independent equilibrium simulations that only communicate when the multicanonical weight function is updated. That way, the Markov chains efficiently sample the temporary distributions allowing for good estimations of consecutive weight functions. The systems investigated range from the well known Ising and Potts spin systems to bead-spring polymers. We estimate the speedup with increasing number of parallel processes. Overall, the parallelization is shown to scale quite well. In the case of multicanonical simulations of the $q$-state Potts model ($q\ge6$) and multimagnetic simulations of the Ising model, the optimal performance is limited due to emerging barriers.Comment: Contribution to the Proceedings of "Recent Developments in Computer Simulational Studies in Condensed Matter Physics 2013

Homeostatic plasticity and external input shape neural network dynamics

In vitro and in vivo spiking activity clearly differ. Whereas networks in vitro develop strong bursts separated by periods of very little spiking activity, in vivo cortical networks show continuous activity. This is puzzling considering that both networks presumably share similar single-neuron dynamics and plasticity rules. We propose that the defining difference between in vitro and in vivo dynamics is the strength of external input. In vitro, networks are virtually isolated, whereas in vivo every brain area receives continuous input. We analyze a model of spiking neurons in which the input strength, mediated by spike rate homeostasis, determines the characteristics of the dynamical state. In more detail, our analytical and numerical results on various network topologies show consistently that under increasing input, homeostatic plasticity generates distinct dynamic states, from bursting, to close-to-critical, reverberating and irregular states. This implies that the dynamic state of a neural network is not fixed but can readily adapt to the input strengths. Indeed, our results match experimental spike recordings in vitro and in vivo: the in vitro bursting behavior is consistent with a state generated by very low network input (< 0.1%), whereas in vivo activity suggests that on the order of 1% recorded spikes are input-driven, resulting in reverberating dynamics. Importantly, this predicts that one can abolish the ubiquitous bursts of in vitro preparations, and instead impose dynamics comparable to in vivo activity by exposing the system to weak long-term stimulation, thereby opening new paths to establish an in vivo-like assay in vitro for basic as well as neurological studies.Comment: 14 pages, 8 figures, accepted at Phys. Rev.

Effect of grafting on the binding transition of two flexible polymers

We investigate the binding transition of two flexible polymers grafted to a steric surface with closeby end points. While free polymers show a discontinuous transition, grafting to a steric flat surface leads to a continuous binding transition. This is supported by results from Metropolis and parallel multicanonical simulations. A combination of canonical and microcanonical analyses reveals that the change in transition order can be understood in terms of the reduced translational entropy of the unbound high-temperature phase upon grafting.Comment: 10 pages, 6 figures, submitted to Eur. Phys. J Spec. Topic

From Particle Condensation to Polymer Aggregation: Phase Transitions and Structural Phases in Mesoscopic Systems: From Particle Condensation to Polymer Aggregation:Phase Transitions and Structural Phases in Mesoscopic Systems

Die vorliegende Arbeit befasst sich mit den Gleichgewichtseigenschaften und Phasenübergängen in verdünnten Teilchen- und Polymersystemen, mit einem Fokus auf Teilchenkondensation und Polymeraggregation. Dazu werden sowohl analytische Argumente als auch hochentwickelte Monte Carlo Simulationen verwendet. Um die in dieser Arbeit erreichten Systemgrößen zu simulieren, wurde eine parallele Version der multikanonischen Methode entwickelt. Die Leistungsfähigkeit dieser Erweiterung wird an mehreren relevanten Beispielen demonstriert. Um Teilchenkondensation und Polymeraggregation in finiten Systemen und in geometrisch beschränkten Strukturen besser zu verstehen, wird der Einfluss von verschiedenen Parametern auf die jeweiligen Übergange untersucht. Dies beinhaltet unter anderem die Systemgröße und Dichte, sowie im Speziellen für semiflexible Polymere deren Steifigkeit. Betrachtet werden sowohl kanonische Observablen (Energie, Tropfen- bzw. Aggregatgröße, etc.) mit der dazugehörigen Übergangstemperatur und -breite, als auch eine mikrokanonische Analyse sowie die Barrieren der Freien Energie. Für semiflexible Polymere wird insbesondere der Einfluss von Steifigkeit auf die resultierende Struktur der Aggregate untersucht, die von amorphen Kugeln für flexible Polymere bis hin zu verdrehten Bündeln für steifere Polymere reichen. Ein weiterer Fokus liegt auf der Untersuchung von Übereinstimmungen zwischen den generischen Mechanismen in Kondensation und Aggregation: dem Übergang zwischen einer homogenen Phase und einer inhomogenen (gemischten) Phase. Auf diesem Niveau kann man Polymeraggregation als Kondensation von ausgedehnten Objekten verstehen. Dies zeigt sich vor allem in dem Skalierungsverhalten von kanonischen und mikrokanonischen Observablen, insbesondere an einem unerwarteten aber konsistenten Bereich für mittelgroße (mesoskopische) Systemgrößen