Simulation data for 'Investigating the quasi-liquid layer on ice surfaces: a comparison of order parameters'

Ice surfaces are characterized by pre-melted quasi-liquid layers (QLLs) which mediate both crystal growth processes and interactions with external agents. Understanding QLLs at the molecular level is necessary to unravel the mechanisms of ice crystal formation. Computational studies of the QLLs heavily rely on the accuracy of the methods employed for identifying the local molecular environment and arrangements, discriminating solid-like and liquid-like water molecules. We compared the results obtained using different order parameters to characterize the QLLs on hexagonal ice (Ih) and cubic ice (Ic) model surfaces investigated with molecular dynamics (MD) simulations (Surf_MD_data reported here) in a range of temperatures. To evaluate the threshold between distinguishing ice and water, we also performed MD simulations in the bulk systems of ice and water (Bulk_MD_data reported here). For the classification task, in addition to the traditional Steinhardt order parameters in different flavours, we select an entropy fingerprint and a deep learning neural networks approach (DeepIce), which are conceptually different methodologies.