Molecular dynamics simulation of nanomaterials using an artificial neural net

We report a method of conducting molecular dynamics (MD) simulations that uses an artificial neural net (ANN) to significantly increase computational speed. The technique enables dynamical simulation of hard objects with essentially arbitrarily complex geometry and is well suited to the simulation of granular matter over a wide range of densities. In hard systems, binary collisions are well defined and the ANN approach enables an efficient algorithm to determine the time to next collision with high accuracy. The method has been used to enable an MD study of an ensemble of 1800 hard, smooth, impenetrable equilateral triangles in a two-dimensional periodic space. At high packing fraction (0.6<rho<0.9), the hard-triangle system exists as a liquid-crystalline-like phase (LCP) in which there is no long-range translational order but in which there is nearly perfect long-range orientational order. As the packing fraction decreases, the LCP undergoes a transition to a fluid state in which the long-range orientational correlation vanishes but short-range order is retained. Long-lived clusters, notably hexamers, are clearly apparent in the liquid phase and appear to be stabilized by a sort of internal "orientational" osmotic pressure. Insofar as can be inferred from our machine calculations, the transition between the LCP and the liquid occurs around rhosimilar to0.57 and appears to be second order. At low density, the hard-triangle system undergoes "chattering" collisions in which pairs of triangles collide and become associated, undergoing multiple collisions with each other before colliding with a third particle. The radial distribution function obtained from both molecular dynamics and Monte Carlo calculations shows a weak peak at low packing fraction.