We report measurements of the vapour–liquid coexistence densities and surface tension of fully flexible Lennard–Jones chain molecules ranging in length from 4 to 60 beads. We demonstrate that the surface tension for all chain lengths collapses to a single master curve when plotted according to the universal parachor correlation. We find a universal parachor exponent 3.79 ± 0.05 for conditions close to the critical point, with a deviation observed for the longest chains far below the critical point.

We present a machine learning technique to discover and distinguish relevant ordered structures from molecular simulation snapshots or particle tracking data. Unlike other popular methods for structural identification, our technique requires no a priori description of the target structures. Instead, we use nonlinear manifold learning to infer structural relationships between particles according to the topology of their local environment. This graph-based approach yields unbiased structural information which allows us to quantify the crystalline character of particles near defects, grain boundaries, and interfaces. We demonstrate the method by classifying particles in a simulation of colloidal crystallization, and show that our method identifies structural features that are missed by standard techniques.

Functional soft materials, comprising colloidal and molecular building blocks that self-organize into complex structures as a result of their tunable interactions, enable a wide array of technological applications. Inverse methods provide a systematic means for navigating their inherently high-dimensional design spaces to create materials with targeted properties. While multiple physically motivated inverse strategies have been successfully implemented in silico, their translation to guiding experimental materials discovery has thus far been limited to a handful of proof-of-concept studies. In this perspective, we discuss recent advances in inverse methods for design of soft materials that address two challenges: (1) methodological limitations that prevent such approaches from satisfying design constraints and (2) computational challenges that limit the size and complexity of systems that can be addressed. Strategies that leverage machine learning have proven particularly effective, including methods to discover order parameters that characterize complex structural motifs and schemes to efficiently compute macroscopic properties from the underlying structure. We also highlight promising opportunities to improve the experimental realizability of materials designed computationally, including discovery of materials with functionality at multiple thermodynamic states, design of externally directed assembly protocols that are simple to implement in experiments, and strategies to improve the accuracy and computational efficiency of experimentally relevant models.

We study the structure and dynamics of semidilute solutions of semiflexible polymers at rest and under shear using hybrid molecular dynamics simulations that take hydrodynamic interactions into account. We show that the polymer center-of-mass diffusion coefficient significantly decreases with increasing chain stiffness at fixed monomer density. The zero-shear viscosity shows a corresponding increase due to the intermolecular interactions of stiffer chains. We apply steady shear flow to the polymer solutions and show that at high shear rates the flow properties become almost independent of polymer stiffness. We characterize the polymer conformations under shear and find that in this regime polymers are elongated and aligned along the flow direction, but semiflexible polymers surprisingly form an increased number of hairpin-like conformations due to increased tumbling.