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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 supercooled and glassy regimes. Using numerical simulations, we test whether a local form of the excess entropy can lead to predictions similar to those made by softness, such as the strong correlation with particles’ tendency to rearrange. In addition, we explore leveraging softness to compute excess entropy in the traditional fashion over softness groupings. Our results show that the excess entropy computed over softness-binned groupings is correlated with activation barriers to rearrangement. 

In the midst of the COVID-19 pandemic, many live musical activities had to be postponed and even canceled to protect musicians and the audience. Orchestral ensembles face a particular challenge of contamination, because they are personally heavy and instrumentally diverse. A chief concern is whether wind instruments are vectors of contamination through aerosol dispersion. This study, made possible by the participation of members of The Philadelphia Orchestra, brings insight into the modes of production and early life of aerosols of human origin emitted by wind instruments. We find that these instruments produce aerosol levels that are comparable to normal speech in quantity and size distribution. However, the exit jet flow speeds are much lower than violent expiratory events (coughing and sneezing). For most wind instruments, the flow decays to background indoor-air levels at approximately 2 m away from the instrument's opening. Long range aerosol dispersion is, thus, via ambient air currents. 

Raman spectroscopy showing the initial formation of SnSe₂followed by the stabilization of SnSe with increased growth time.