Exploring the Relationship Between Softness and Excess Entropy in Glass-forming Systems

Abstract: We explore the relationship between a machine-learned structural quantity (softness) and excess entropy in simulations of supercooled liquids. Excess entropy is known to scale well the dynamical properties of liquids, but this quasi-universal scaling is known to breakdown in the supercooled and glassy regimes. Using numerical simulations, we test whether a local form of the excess entropy can lead to predictions that derive from softness, which has been shown to correlate well with the tendency for individual particles to rearrange. To that end, we explore leveraging softness to compute excess entropy in the traditional fashion over softness groupings. Our results show that by computing the excess entropy over softness-binned groupings, we can build a strong quantitative relationship between the rearrangement barriers across the explored systems.