A simple Discrete-Element-Model of Brazilian Test

We present a statistical model which is able to capture some interesting features exhibited in the Brazilian test. The model is based on breakable elements which break when the force experienced by the elements exceed their own load capacity. In this model when an element breaks, the capacity of the neighboring elements are decreased by a certain amount assuming weakening effect around the defected zone. We numerically investigate the stress-strain behavior, the strength of the system, how it scales with the system size and also it's fluctuation for both uniformly and weibull distributed breaking threshold of the elements in the system. We find that the strength of the system approaches it's asymptotic value $\sigma_c=1/6$ and $\sigma_c=5/18$ for uniformly and Weibull distributed breaking threshold of the elements respectively. We have also shown the damage profile right at the point when the stress-strain curve reaches at it's maximum and then it is compared with our experimental observations

Conformation and dynamics of partially active linear polymers

We perform numerical simulations of isolated, partially active polymers, driven out-of-equilibrium by a fraction of their monomers. We show that, if the active beads are all gathered in a contiguous block, the position of the section along the chain determines the conformational and dynamical properties of the system. Notably, one can modulate the diffusion coefficient of the polymer from {active-like to passive-like} just by changing the position of the active block. Further, in special cases, enhancement of diffusion can be achieved by decreasing the overall polymer activity. Our findings may help in the modelization of active biophysical systems, such as filamentous bacteria or worms.Comment: 12 pages, 10 figures + supplemental 4 pages, 7 figure

Inequality of avalanche sizes in models of fracture

Prediction of an imminent catastrophic event in a driven disordered system is of paramount importance - from the laboratory scale controlled fracture experiment to the largest scale of mechanical failure i.e., earthquakes. It has been long conjectured that the statistical regularities in the energy emission time series mirrors the "health" of such driven systems and hence have the potential for forecasting imminent catastrophe. Among other statistical regularities, a measure of how unequal the avalanche sizes are, is potentially a crucial indicator of imminent failure. The inequalities of avalanche sizes are quantified using inequality indices traditionally used in socio-economic systems: the Gini index (g), the Hirsch index (h) and the Kolkata index (k). It is then shown analytically (for mean field) and numerically (for non mean field) in models of quasi-brittle materials that the indices show universal behavior near the breaking points in such models and hence could serve as indicators of imminent breakdown of stressed disordered systems.Comment: Accepted for publication in Phys. Rev.

Machine learning understands knotted polymers

Simulated configurations of flexible knotted rings confined inside a spherical cavity are fed into long-short term memory neural networks (LSTM NNs) designed to distinguish knot types. The results show that they perform well in knot recognition even if tested against flexible, strongly confined and therefore highly geometrically entangled rings. In agreement with the expectation that knots are delocalized in dense polymers, a suitable coarse-graining procedure on configurations boosts the performance of the LSTMs when knot identification is applied to rings much longer than those used for training. Notably, when the NNs fail, usually the wrong prediction still belongs to the same topological family of the correct one. The fact that the LSTMs are able to grasp some basic properties of the ring's topology is corroborated by a test on knot types not used for training. We also show that the choice of the NN architecture is important: simpler convolutional NNs do not perform so well. Finally, all results depend on the features used for input: surprisingly, coordinates or bond directions of the configurations provide the best accuracy to the NNs, even if they are not invariant under rotations (while the knot type is invariant). Other rotational invariant features we tested are based on distances, angles, and dihedral angles

Jamming and percolation in the random sequential adsorption of a binary mixture on the square lattice

We study the competitive irreversible adsorption of a binary mixture of monomers and square-shaped particles of linear size $R$ on the square lattice. With the random sequential adsorption model, we investigate how the jamming coverage and percolation properties depend on the size ratio $R$ and relative flux $F$. We find that the onset of percolation of monomers is always lower for the binary mixture than in the case with only monomers ($R=1$). Moreover, for values $F$ below a critical value, the higher is the flux or size of the largest species, the lower is the value of the percolation threshold for monomers.Comment: Submitted to a special issue of Journal of Physics A: Mathematical and Theoretical to mark the 70th Birthday of Robert M. Zif