Communication: Equation of state of hard oblate ellipsoids by replica exchange Monte Carlo

Machine-learning effective many-body potentials for anisotropic particles using orientation-dependent symmetry functions

Spherically-symmetric atom-centered descriptors of atomic environments have been widely used for constructing potential or free energy surfaces of atomistic and colloidal systems and to characterize local structures using machine learning techniques. However, when particle shapes are non-spherical, as in the case of rods and ellipsoids, standard spherically-symmetric structure functions alone produce imprecise descriptions of local environments. In order to account for the effects of orientation, we introduce two- and three-body orientation-dependent particle-centered descriptors for systems composed of rod-like particles. To demonstrate the suitability of the proposed functions, we use an efficient feature selection scheme and simple linear regression to construct coarse-grained many-body interaction potentials for computationally-efficient simulations of model systems consisting of colloidal particles with anisotropic shape: mixtures of colloidal rods and nonadsorbing polymer, hard rods enclosed by an elastic microgel shell, and ligand-stabilized nanorods. We validate the machine-learning (ML) effective many-body potentials based on orientation-dependent symmetry functions by using them in direct coexistence simulations to map out the phase behavior of colloidal rods and non-adsorbing polymer. We find good agreement with results obtained from simulations of the true binary mixture, demonstrating that the effective interactions are well-described by the orientation-dependent ML potentials.

Ordering transitions of weakly anisotropic hard rods in narrow slitlike pores

The effect of strong confinement on the positional and orientational ordering is examined in a system of hard rectangular rods with length L and diameter D (L>D) using the Parsons-Lee modification of the second virial density-functional theory. The rods are nonmesogenic (L/D<3) and confined between two parallel hard walls, where the width of the pore (H) is chosen in such a way that both planar (particle's long axis parallel to the walls) and homeotropic (particle's long axis perpendicular to the walls) orderings are possible and a maximum of two layers is allowed to form in the pore. In the extreme confinement limit of H≤2D, where only one-layer structures appear, we observe a structural transition from a planar to a homeotropic fluid layer with increasing density, which becomes sharper as L→H. In wider pores (2D<H<3D) planar order with two layers, homeotropic order, and even combined bilayer structures (one layer is homeotropic, while the other is planar) can be stabilized at high densities. Moreover, first-order phase transitions can be seen between different structures. One of them emerges between a monolayer and a bilayer with planar orders at relatively low packing fractions.

Self-assembly of Janus ellipsoids II: Janus prolate spheroids

In self-assembly, the anisotropy of the building blocks and their formation of complex structures have been the subject of considerable recent research. Extending recent research on Janus particles and completing the study of Janus spheroids, we conduct a systematic investigation on the self-assembly of Janus prolate spheroids based on a primitive model that we proposed. Janus prolate spheroids are particles that have a prolate spheroidal body and two hemi-surfaces along the major axis coded with different chemical properties. Using Monte Carlo simulations, we investigate the effects of the aspect ratio on the self-assembly process. In contrast to the vesicle-like aggregates for Janus oblate spheroids, we obtain various ordered cluster structures for Janus prolate spheroids through self-assembly. With an increasing aspect ratio, we find a transition of cluster morphology, from vesicles to tubular micelles and micelles. In particular, a relatively small change in the aspect ratio leads to a rather significant change in morphology. We apply a cluster analysis to understand the mechanism associated with such a transition.