  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.
