# Attractive versus truncated repulsive supercooled liquids: The dynamics is encoded in the pair correlation function

## TL;DR

This study shows that subtle differences in pair correlation functions, captured through machine learning, can explain the distinct glassy dynamics in liquids with similar structures but different interaction potentials.

## Contribution

It introduces a machine learning-based weighting method to connect pair correlation functions with dynamical differences in supercooled liquids.

## Key findings

- Weighted integral of pair correlation function predicts dynamical differences.
- Attractive interactions significantly alter glassy dynamics.
- Machine learning effectively captures subtle structural influences.

## Abstract

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.