Use of machine learning for unraveling hidden correlations between particle size distributions and the mechanical behavior of granular materials

A data-driven framework was used to predict the macroscopic mechanical behavior of dense packings of polydisperse granular materials. The discrete element method, DEM, was used to generate 92,378 sphere packings that covered many different kinds of particle size distributions, PSD, lying within 2 particle sizes. These packings were subjected to triaxial compression and the corresponding stress–strain curves were fitted to Duncan–Chang hyperbolic models. An artificial neural network (NN) scheme was able to anticipate the value of the model parameters for all these PSDs, with an accuracy similar to the precision of the experiment and even when the NN was trained with a few hundred DEM simulations. The estimations were indeed more accurate than those given by multiple linear regressions (MLR) between the model parameters and common geotechnical and statistical descriptors derived from the PSD. This was achieved in spite of the presence of noise in the training data. Although the results of this massive simulation are limited to specific systems, ways of packing and testing conditions, the NN revealed the existence of hidden correlations between PSD of the macroscopic mechanical behavior.

Dynamic Susceptibilities in Dense Soft Athermal Spheres Under a Finite-Rate Shear

The mechanical responses of dense packings of soft athermal spheres under a finite-rate shear are studied by means of molecular dynamics simulations. We investigate the volume fraction and shear rate dependence of the fluctuations in the shear stress and the interparticle contact number. In particular, we quantify them by defining the susceptibility as the ratio of the global to local fluctuations. The obtained susceptibilities form ridges on the volume fraction-shear rate plane, which are reminiscent of the Widom lines around the critical point in an equilibrium phase transition.

Electron microscopy of nanoparticle superlattice formation at a solid-liquid interface in nonpolar liquids

Nanoparticle superlattice films form at the solid-liquid interface and are important for mesoscale materials, but are notoriously difficult to analyze before they are fully dried. Here, the early stages of nanoparticle assembly were studied at solid-liquid interfaces using liquid-phase electron microscopy. Oleylamine-stabilized gold nanoparticles spontaneously formed thin layers on a silicon nitride (SiN) membrane window of the liquid enclosure. Dense packings of hexagonal symmetry were obtained for the first monolayer independent of the nonpolar solvent type. The second layer, however, exhibited geometries ranging from dense packing in a hexagonal honeycomb structure to quasi-crystalline particle arrangements depending on the dielectric constant of the liquid. The complex structures formed by the weaker interactions in the second particle layer were preserved, while the surface remained immersed in liquid. Fine-tuning the properties of the involved materials can thus be used to control the three-dimensional geometry of a superlattice including quasi-crystals.

SOME DENSE RANDOM PACKINGS GENERATED BY THE DEAD LEAVES MODEL

The intact grains of the dead leaves model enables us to generate random media with non overlapping grains. Using the time non homogeneous sequential model with convex grains, theoretically very dense packings can be generated, up to a full covering of space. For these models, the theoretical volume fraction, the size distribution of grains, and the pair correlation function of centers of grains are given.

Generating dense packings of hard spheres by soft interaction design

Packing spheres efficiently in large dimension dd is a particularly difficult optimization problem. In this paper we add an isotropic interaction potential to the pure hard-core repulsion, and show that one can tune it in order to maximize a lower bound on the packing density. Our results suggest that exponentially many (in the number of particles) distinct disordered sphere packings can be efficiently constructed by this method, up to a packing fraction close to 7 \, d \, 2^{-d}7d2−d. The latter is determined by solving the inverse problem of maximizing the dynamical glass transition over the space of the interaction potentials. Our method crucially exploits a recent exact formulation of the thermodynamics and the dynamics of simple liquids in infinite dimension.