Averaging local structure to predict the dynamic propensity in supercooled liquids

TL;DR

This paper introduces a simplified, efficient model for predicting the local dynamics of supercooled liquids by averaging structural features, achieving comparable accuracy to complex GNNs with far fewer parameters.

Contribution

The authors propose a new method that leverages averaged local structural features to predict dynamics, reducing model complexity while maintaining accuracy.

Findings

The new model matches GNN accuracy with ~1000 parameters.

Averaging local structures improves prediction efficiency.

Insights into the importance of radial and angular descriptors.

Abstract

Predicting the local dynamics of supercooled liquids based purely on local structure is a key challenge in our quest for understanding glassy materials. Recent years have seen an explosion of methods for making such a prediction, often via the application of increasingly complex machine learning techniques. The best predictions so far have involved so-called Graph Neural Networks (GNN) whose accuracy comes at a cost of models that involve on the order of 10$^5$ fit parameters. In this Letter, we propose that the key structural ingredient to the GNN method is its ability to consider not only the local structure around a central particle, but also averaged structural features centered around nearby particles. We demonstrate that this insight can be exploited to design a significantly more efficient model that provides essentially the same predictive power at a fraction of the computational complexity (approximately 1000 fit parameters), and demonstrate its success by fitting the dynamic propensity of Kob-Andersen and binary hard-sphere mixtures. We then use this to make predictions regarding the importance of radial and angular descriptors in the dynamics of both models.