Glass-forming liquids and polymers: with a little help from computational statistical physics

The past decade has seen a resurgence in the study of the glass transition, particularly using computational approaches. Advances in computer architectures and algorithms have allowed the study of larger system sizes for longer time scales, which has enabled computational scientists to explore both new aspects of supercooled liquids, glasses, and the glass transition, as well as to re-investigate old puzzles. One aspect of these systems in particular that is currently receiving a great deal of attention, and which has seen progress in part due to the use of computational statistical physics, is the elucidation of the detailed molecular motion and how this motion changes as a liquid approaches the glass transition. In particular, simulation has led to the discovery of dynamical, ordered structures within the disordered, glass-forming liquid [1–10]. It has long been known that despite the similarity in structure of a liquid and its glass, relaxation times, diffusivities and viscosities change by up to 14 orders of magnitude as a liquid is cooled through its glass transition. Why the dynamics can change so dramatically while static structure seemingly changes so little has been a long standing, open question in the field of glass research. Clearly, molecular motion becomes increasingly difficult as the temperature or spe-