(500d) An Implicit Approach for Simulating Strongly Directional Interactions

Molecular models, consisting of simple shapes with “sticky” sites that interact with each other through short ranged but strongly orientational dependent intermolecular forces, have been of popular use for treating association interactions in molecular simulations. Generally, sampling the configurational space of such models, particularly with high association directionality and strengths, is difficult because of the difficulty for systems to access the “bonded” configurations, and to get out of the traps once they do. Earlier studies had developed several biased Monte Carlo (MC) schemes for enhancing sampling efficiency. However, applicability of those methods is often limited to scenarios with low densities, such as vapor-liquid phase equilibrium of associating fluids. Here we present an implicit approach for accounting for directional interactions in molecular simulations. The approach is based on replacing directional interactions with non-directional bond-forming moves in Monte Carlo schemes. Parameters of the bond-forming move are obtained from low-cost two-body simulations with explicit directional interactions, so that directionality of the original model is implicitly embedded in the bond-forming. Accuracy and efficiency of the implicit approach is tested in simulations of strongly associating fluids. It is shown that the implicit approach is statistically equivalent to the directional model for ensemble-averaged properties, such as energies and structures. Efficiency of the implicit method, measured as the wall-clock time for generating an uncorrelated sample, is seen to be comparable to the optimum aggregation-volume-bias Monte Carlo algorithm at gas-state densities, but an order magnitude faster at liquid-state densities. Beyond simple associating fluids, the implicit approach can be readily extended to be applied in simulations of polymeric systems with directional interactions, such as supramolecular assembly of multicomponent polymers.