Attractive versus truncated repulsive supercooled liquids: The dynamics is encoded in the pair correlation function
This work addresses the challenge of predicting dynamics in supercooled liquids for researchers in soft matter physics, though it is incremental as it builds on existing methods.
The study tackled the problem of distinguishing glassy dynamics in liquids with different interaction potentials by showing that a weighted integral of the pair correlation function, derived via machine learning, accurately captures their dynamical differences despite nearly identical structures.
We compare glassy dynamics in two liquids that differ in the form of their interaction potentials. Both systems have the same repulsive interactions but one has also an attractive part in the potential. These two systems exhibit very different dynamics despite having nearly identical pair correlation functions. We demonstrate that a properly weighted integral of the pair correlation function, which amplifies the subtle differences between the two systems, correctly captures their dynamical differences. The weights are obtained from a standard machine learning algorithm.